The development of prosthetic feet that closely replicate the natural biomechanics of the human foot remains a significant challenge in prosthetics engineering. This paper presents the design and testing of a novel agonist-antagonist architecture for the ankle joint of a passive prosthetic foot featuring an adaptive sole. The ankle mechanism, inspired by the dynamics of the human leg-ankle-foot complex, utilizes compliant elements in an agonist-antagonist configuration to passively achieve an ankle torque close to that of a sound ankle without the need for external actuation. Concurrently, the adaptive sole adjusts its shape in response to different terrains, potentially improving stability and comfort for the user. The theoretical model underlying the proposed design is presented, followed by a preliminary validation through simulations. Finally, a prototype based on the new architecture is tested by a healthy subject using customized walking boots, demonstrating its potential to improve the functional performance of prosthetic feet in diverse environments.

Intuitively, prostheses with user-controllable stiffness could mimic the intrinsic behavior of the human musculoskeletal system, promoting safe and natural interactions and task adaptability in real-world scenarios. However, prosthetic design often disregards compliance because of the additional complexity, weight, and needed control channels. This article focuses on designing a variable stiffness actuator (VSA) with weight, size, and performance compatible with prosthetic applications, addressing its implementation for the elbow joint. While a direct biomimetic approach suggests adopting an agonist-antagonist (AA) layout to replicate the biceps and triceps brachii with elastic actuation, this solution is not optimal to accommodate the varied morphologies of residual limbs. Instead, we employed the AA layout to craft an elbow prosthesis fully contained in the user's forearm, catering to individuals with distal transhumeral amputations. In addition, we introduce a variant of this design where the two motors are split in the upper arm and forearm to distribute mass and volume more evenly along the bionic limb, enhancing comfort for patients with more proximal amputation levels. We characterize and validate our approach, demonstrating that both architectures meet the target requirements for an elbow prosthesis. The system attains the desired 120$^{\circ }$range of motion, achieves the target stiffness range of [2, 60] N$\cdot$m/rad, and can actively lift up to 3 kg. Our novel design reduces weight by up to 50% compared to existing VSAs for elbow prostheses while achieving performance comparable to the state of the art. Case studies suggest that passive and variable compliance could enable robust and safe interactions and task adaptability in the real world.

Endowing robots with advanced tactile abilities based on biomimicry involves designing human-like tactile sensors, computational models, and motor control policies to enhance contact information retrieval. Here, we consider compliance discrimination with a soft biomimetic tactile optical sensor (TacTip). In previous work, we proposed a vision-based approach derived from a computational model of human tactile perception to discriminate object compliance with the TacTip, based on contact area spread computation over the indenting force. In this work, we first increased the robustness of our vision-based method with a more precise estimation of the initial contact area condition, which enables correct compliance estimation also when the probing direction is other than normal to the specimen surface. Then, we integrated within our validated framework the mechanisms of internal muscular regulation (co-contraction) that humans adopt during object compliance probing, to maximize the information uptake. To this aim, we used human co-contraction patterns extracted during object softness probing to control a Variable Stiffness Actuator (that emulates the agonistic-antagonistic behavior of human muscles), which is used to actuate the indenter system endowed with the TacTip for object compliance exploration. We found that our model-based approach for compliance discrimination, fed with more precisely estimated initial conditions, significantly improves with the human-inspired impedance regulation, with respect to the usage of a rigid actuator.

Due to their fast and efficient locomotion, two-wheeled humanoids are fascinating systems with the potential to be involved in many application domains, including healthcare, manufacturing, and many others. However, these robots constitute a challenging case of study for control purposes due to the two-wheeled inverted pendulum dynamics that characterizes their mobility and support, as it is underactuated and unstable. In this article, we propose a novel whole-body control approach to stabilize two-wheeled humanoids. To tackle the control problem of their forward motion and pitch equilibrium, leveraging on the observation that such systems are usually characterized by a faster and a slower dynamics (being the pitch angle faster and the forward displacement slower), we design a composite whole-body control that combines two computed-torque control loops to stabilize both dynamics to the desired trajectories. The control approach is introduced and its derivation is described for the simpler case of a two-wheeled inverted pendulum first, and for a whole two-wheeled humanoid after. To prove its validity, the control approach is tested experimentally on the two-wheeled humanoid robot Alter-Ego. The robot proves to be able to perform complicated interaction tasks, including opening a door, grasping a heavy object, and resisting to external dynamic disturbances.

This study presents the Prosthetic Upper-Limb Sensory Enhancement (PULSE), a novel dual feedback device completely integrated into a prosthetic socket. The core of the system includes two compact vibrotactile actuators and two silicone chambers in contact with the user’s skin. These components provide high-frequency tactile cues for initial contact and surface information (e.g. texture) as well as pressure stimuli related to grasping force. Ten able-bodied participants and one subject with limb loss validated the system, accomplishing an object discrimination task in two different modalities (with and without the feedback). Standardized questionnaires evaluate users’ satisfaction and workload, enabling a systematic and robust device assessment. The results show that the PULSE device enhanced performance compared to its absence without causing discomfort for a prosthetic user and able-bodied participants. The findings highlight the potential of dual haptic feedback to enhance sensory perception in prosthetic applications and offer valuable insights for future prosthetic design.

Characterizing the risk of operations is a fundamental requirement in robotics, and a crucial ingredient of safe planning. The problem is multifaceted, with multiple definitions arising in the vast recent literature fitting different application scenarios and leading to different computational approaches. A basic element shared by most frameworks is the definition and evaluation of the probability of collision for a mobile object in an environment with obstacles. We observe that, even in basic cases, different interpretations are possible. This article proposes an index we call “Risk Density,” which offers a theoretical link between conceptually distant assumptions about the interplay of single collision events along a continuous path. We show how this index can be used to approximate the collision probability in the case where the robot evolves along a nominal continuous curve from random initial conditions. Indeed, under this hypothesis the proposed approximation outperforms some well-established methods either in accuracy or computational cost.

Robot feet are crucial for maintaining dynamic stability and propelling the body during walking, especially on uneven terrains. Traditionally, robot feet were mostly designed as flat and stiff pieces of metal, which meets its limitations when the robot is required to step on irregular grounds, e.g., stones. While one could think that adding compliance under such feet would solve the problem, this is not the case. To address this problem, we introduced the SoftFoot, an adaptive foot design that can enhance walking performance over irregular grounds. The proposed design is completely passive and varies its shape and stiffness based on the exerted forces, through a system of pulley, tendons, and springs opportunely placed in the structure. This article outlines the motivation behind the SoftFoot and describes the theoretical model which led to its final design. The proposed system has been experimentally tested and compared with two analogous conventional feet, a rigid one and a compliant one, with similar footprints and soles. The experimental validation focuses on the analysis of the standing performance, measured in terms of the equivalent support surface extension and the compensatory ankle angle, and the rejection of impulsive forces, which is important in events such as stepping on unforeseen obstacles. Results show that the SoftFoot has the largest equivalent support surface when standing on obstacles, and absorbs impulsive loads in a way almost as good as a compliant foot.

This paper discusses how the disciplines of Design and Engineering are jointly addressing disability and somehow affecting its very interpretation. The discussion focuses on high-tech prostheses, where robotic devices substitute human body parts. The application of robotic technologies to prosthetics has a relatively long history. Nevertheless, only in the last decade have we witnessed applications reach the market and become available for a large base of users who were offered prostheses with superior motor and sensory performance. The process of bringing ever more advanced technologies to fruition by prosthetic users is fully ongoing today, with some promising solutions coming from robotics (such as, e.g. AI techniques or soft robotics materials) to be transferred to human use. In this transfer process, technology alone is insufficient to warrant success, and the need for a close collaboration between the Engineering domain and the Design disciplines is apparent. We address this point with specific reference to a case study, i.e. the transformation of an innovative but by-now established technology in the industrial robotics field (the “Pisa/IIT SoftHand”) into a prosthetic hand (the “SoftHand Pro”). Besides obvious technical considerations about size, connections, control, and so on, which can be addressed with a thorough technical revision of the design, what makes the profound difference between the two devices is that, as a prosthesis, the SoftHand is intended as a human body part, and not as an external tool. To reach its ultimate goals, the hand should become a part of the human user, with his body and mind. The empirical approach and tools of Designers afford the possibility to enrich the re-design process, considering the final user at the centre of the process, in a sort of renewed humanistic approach. The paper reflects this multidisciplinary approach and is structured as follows: the first part describes a cultural framework for the use of high-technology upper limb prostheses. This culture is defined through two significant relations (Users & Society; Users & Device). Inputs come from desk research conducted in different fields, ranging from Social Psychology to Medicine and Rehabilitation area. In this scenario, it is possible to extract design insights applicable to the design brief. The introduction of a robotic prosthetic hand (SoftHand Pro) and a related, single-user case study follow. The aim here is also to illustrate a process where engineering innovations are facilitated by tools from the Design field in the attempt to make the whole process coherently centred on users. Involved are all aspects, from material technology to the covering and finishing of the prosthetic device. The resulting, final prototype of the SoftHand Pro is finally presented.

With the improvements in their computational and physical intelligence, robots are now capable of operating in real-world environments. However, manipulation and grasping capabilities are still areas that require significant improvements. To address this, we introduce a new data-driven grasp planning algorithm called Grasp it Like a Pro 2.0. This algorithm utilizes a small number of human demonstrations to teach a robot how to grasp arbitrary objects. By decomposing objects into basic shapes, our algorithm generates candidate grasps that can generalize to different object's geometry. The algorithm selects the grasp to execute based on a selection policy that maximizes a novel grasp quality metric introduced in this article. This metric considers the complex interdependencies between the predicted grasp, the local approximation produced by the basic shape decomposition, and the gripper used. We evaluate our approach against multiple baselines using different grippers and objects. The results demonstrate the effectiveness of our method in generating and selecting high-quality and reliable grasps. With a soft underactuated robotic hand, our algorithm achieves a 94.0% success rate in 150 grasps across 30 different objects. Similarly, with a rigid gripper, it achieves an 85.0% success rate in 80 grasps across 16 different objects.

The attention given to impedance control in recent years does not match a similar focus on the choice of impedance values that the controller should execute. Current methods are hardly general and often compute fixed controller gains relying on the use of expensive sensors. In this article, we address the problem of online impedance planning for Cartesian impedance controllers that do not assign the closed-loop inertia. We propose an optimization-based algorithm that, given the Cartesian inertia, computes the stiffness and damping gains without relying on force/torque measurements and so that the effects of perturbations are less than a maximum acceptable value. By doing so, we increase robot resilience to unexpected external disturbances while guaranteeing performance and robustness. The algorithm provides an analytical solution in the case of impedance-controlled robots with diagonally dominant inertia matrix. Instead, established numerical methods are employed to deal with the more common case of nondiagonally dominant inertia. Our work attempts to create a general impedance planning framework, which needs no additional hardware and is easily applicable to any robotic system. Through experiments on real robots, including a quadruped and a robotic arm, our method is shown to be employable in real time and to lead to satisfactory behaviors.

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.

Energy consumption is an important issue in robotics. This article deals with the problem of reducing the energy consumption of compliant electro-mechanical systems while performing periodic tasks. After deriving performance indices to quantify the energy consumption of a mechanical system, we propose a method to determine both the optimal compliant actuation parameters, and link trajectories to minimize energy consumption. We show how this problem can be cast in a simpler one where the optimization regards only parameters that define the shape of periodic trajectories to be subsequently determined by using numerical optimization tools. Indeed, in our framework, the optimal stiffness and spring preload can be analytically obtained as a function of the desired link trajectories. We then provide simulations and experimental validations of the obtained results on a two-link compliant manipulator platform which performs a repetitive pick-and-place task. Our experiments show that the use of compliant actuators instead of rigid ones and the optimization of their compliant parameters give rise to an energy saving up to 62% with respect to rigid actuation. Moreover, the simultaneous optimization of the compliant parameters and link trajectories provide an additional energy saving up to 20%.

Embedding elastic elements into legged robots through mechanical design enables highly efficient oscillating patterns that resemble natural gaits. However, current trajectory planning techniques miss the opportunity of taking advantage of these natural motions. This work proposes a locomotion planning method that aims to unify traditional trajectory generation with modal oscillations. Our method utilizes task-space linearized modes for generating center of mass trajectories on the sagittal plane. We then use nonlinear optimization to find the gait timings that match these trajectories within the Divergent Component of Motion planning framework. This way, we can robustly translate the modes-aware centroidal motions into joint coordinates. We validate our approach with promising results and insights through experiments on a compliant quadrupedal robot.

Frequently, ground robots are hampered by debris and objects on the ground, and safely surpassing them is not always trivial. On the contrary, a robot capable of flying is intrinsically immune to such obstacles and, therefore, greatly enhances the possibility of inspecting and intervening in adverse surroundings for humans. This work introduces a novel teleoperated aerial platform for inspection and intervention in unstructured environments. The robot is composed of an aerial base, two arms, and a two-degrees-of-freedom head that consent the access of human operators in any workplace in total safety. The arms are designed with a joint structure of tendons and are held by elastic components. This composition considerably improves the robustness by inserting softness and redistributing the weights to lessen the actions on the drone. Moreover, the aerial platform employs two soft hands capable of adapting to the shape of the objects under grasp, increasing the manipulation performance. We presented the mechanical and control design, a gazebo simulation employed to test the controllers, and a physical structure for the experimental validation of the system. The system is available as Open-Source material.

The vast majority of state-of-the-art walking robots employ flat or ball feet for locomotion, presenting limitations while stepping on obstacles, slopes, or unstructured terrain. Moreover, traditional feet for quadrupeds lack sensing systems that are able to provide information about the environment and about the foot interaction with the surroundings. This further diminishes their value. Inspired by our previous work on soft feet for bipedal robots, we present the SoftFoot-Q, an articulated adaptive foot for quadrupeds. This device is conceived to be robust and able to overcome the limitations of currently employed feet. The core idea behind our adaptive foot design is first introduced and validated through a simplified mathematical formulation of the problem. Subsequently, we present the chosen mechanical implementation to attempt overcoming current limitations. The realized prototype of adaptive foot is integrated and tested on the compliantly actuated quadrupedal robot ANYmal together with an ROS-based real-time foot pose reconstruction software. Both extensive field tests and indoor experiments show noticeable performance improvements, in terms of reduced slippage of the robot, with respect to both flat and ball feet.

In many real-world manipulation problems, the constraints imposed by the environment on an object are tight. In these cases, most state-of-the-art planners struggle to fit satisfactorily in low dimensional sub-manifolds, while still ensuring geometric and force feasibility. On the other hand, humans are at ease with such situations and indeed exploit constraints to manipulate objects proficiently.To face this challenge, we propose to merge state-of-art randomized grasp planning methods with model-based grasp analysis. We use the partial form-closure analysis framework to find the geometrically feasible motions of the object. Then, to ensure that the desired motions are physically realizable by the robot, we resort to an extension of the force-closure analysis framework accounting also for dynamic friction. We use these instruments to construct a random tree in a simplified planning space containing only object-robot configurations that are reachable through effectively applicable contact forces. The algorithm, validated in simulation and in preliminary experiments with a collaborative robot, features the ability to compute solutions for heavily constrained real-world manipulation problems.

The need for users' safety and technology accept-ability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions. Classic solutions for anthropomorphic movement generation usually rely on optimization procedures, which build upon hypotheses devised from neuroscientific literature, or capitalize on learning methods. However, these approaches come with limitations, e.g. limited motion variability or the need for high dimensional datasets. In this work, we present a technique to directly embed human upper limb principal motion modes computed through functional analysis in the robot trajectory optimization. We report on the implementation with manipulators with redundant anthropomorphic kinematic architectures - although dissimilar with respect to the human model used for functional mode extraction - via Cartesian impedance control. In our experiments, we show how human trajectories mapped onto a robotic manipulator still exhibit the main characteristics of human-likeness, e.g. low jerk values. We discuss the results with respect to the state of the art, and their implications for advanced human-robot interaction in industrial co-botics and for human assistance.

An approach toward intuitive and easy robot programming, consists to transfer skills from humans to machines, through demonstration. A vast literature exists on learning from multiple demonstrations. This paper, on the other hand, tackles the problem of providing all needed information to execute a certain task by resorting to one single demonstration - hence, a problem closer to programming than to learning. We use wearable consumer devices - but no keyboard nor coding - as programming tools, to let the programmer tele-operate the robot, which in turn records the most salient features and affordances from the object, environment, robot, and human. To enable this goal we combine off-the-shelf soft-articulated robotic components with the framework of Dynamic Movement Primitives, which we contribute to extend to generalize human trajectories and impedance regulation skills. This framework enables to teach robot quickly and in a intuitive way without coding. Experimental tests have been performed on a dual-arm system composed by two 7-dofs collaborative robots equipped with anthropomorphic end-effectors. Experiments show the functionality of the framework and verify the effectiveness of the impedance extension.

Trajectory tracking of flexible link robots is a classical control problem. Historically, the link elasticity was considered as something to be removed. Hence, the control performance was guaranteed by adopting high-gain feedback loops and, possibly, a dynamic compensation with the result to stiffen up the dynamic behavior of the robot. Nowadays, robots are pushed more and more towards a safe physical interaction with a less and less structured environment. Hence, the design and control of the robots moved to an on-purpose introduction of highly compliant elements in the robot bodies, the so-called soft robotics, and towards control approaches that aim to provide the tracking performance without a substantial change in the robot dynamic behavior. Following this approach, we present an iterative learning control that relies mainly on a feedforward component, hence preserves the robot dynamics, for trajectory tracking of a one-link flexible arm. We provide a condition, based on the system dynamics and similar to the Strong Inertially Coupled property, that ensures the applicability of the proposed control method. Finally, we report simulation and experimental tests to validate the theoretical results.

Autonomous vehicles are undergoing a rapid development thanks to advances in perception, planning and control methods and technologies achieved in the last two decades. Moreover, the lowering costs of sensors and computing platforms are attracting industrial entities, empowering the integration and development of innovative solutions for civilian use. Still, the development of autonomous racing cars has been confined mainly to laboratory studies and small to middle scale vehicles. This paper tackles the development of a planning and control framework for an electric full scale autonomous racing car, which is an absolute novelty in the literature, upon which we report our preliminary experiments and perspectives on future work. Our system leverages real time Nonlinear Model Predictive Control to track a pre-planned racing line. We describe the whole control system architecture including the mapping and localization methods employed.

Intrinsic Tactile Sensing (ITS) is a well-established technique, relying on force/torque and geometric surface description to find contact centroids. The method works well for rigid surfaces. However, finding a solution for deformable surfaces is an open issue. This work presents two solutions to extend ITS to deformable surfaces, relying on force-deformation characteristics of the surface under exploration: (i) a closed-form approach that calculates the contact centroid using standard ITS, but on a shrunk geometry approximating the deformed surface; (ii) an iterative procedure that takes into account soft surface deformation, and force/torque equilibrium to minimize a cost function. We have tested both using ellipsoid silicone specimens, with different softness levels and indented along different directions. Both linear and quadratic fitting for the force-indentation behavior were employed. The two methods have distinct advantages and limitations. However, a combination of two methods, using one to produce the initial guess for the other, turns out to be very effective. Indeed, in our validation this solution showed convergence under 1ms, attaining errors lower than 1 mm. The proposed approaches were implemented in a ROS-based toolbox, integrating both solutions.

This article presents the design of soft material robots with tunable damping properties. This study derives from the investigation of an under-actuated dynamic approach involving multi-chamber pneumatic systems. The co-design of the mechanical parameters (stiffness and damping) of the system along with the time profile of the input allows to obtain different behaviors using a reduced number of feeding line. In this work we analyze via simulations and experiments several approaches to tune the damping of soft robots. The most effective solution employs a layer of granular material immersed in viscous oil within the chamber wall. This method has been employed to realize bending actuators with a continuous deformation pattern. Finally, we show an application involving a two-fingered gripper fed by a single pneumatic line, which is able to perform pinch and power grasp.

The deployment of robotics in real-world scenarios, which may involve harsh and irregular physical interactions with the environment, such as those when robots operating in a disaster scenario, or the interactions that prosthetic devices may experience, demands hardware, which is physically resilient. The end-effectors, as the main media of interaction, are probably the parts at the highest risk. The capability of robotic hands to survive severe impacts is thus a necessity for the effective deployment of reliable robotic solutions in real-world tasks. Although, this robustness capability has been noted and discussed in the robotics community for long time, the literature does not provide a systematic study nor there is any proposal of standardized test or metric to evaluate hand resilience. In this work, inspired by the works of Charpy and Izod for the systematic definition of resilience and toughness of materials through impact tests, we consider extending the standard test to robot hands. We introduce a resilience evaluation framework, including a precisely defined experimental set-up and test procedure. As an example of application of the procedure, we apply it to experimentally characterize two robot hands, with a similar conceptual architecture but different size and material. From these tests we obtain several insights, including the observation that the dominant factor in hand resilience is their compliance and actuation principle, and that the use, under certain design conditions, of lightweight materials, such as plastic instead of aluminum, may not necessarily reduce the mechanical strength of the overall system.

Since the spread of robotic systems embedding in their mechanics purposefully designed elastic elements, the interest in characterizing and exploiting non-linear oscillatory behaviors has progressively grown. However, few works so far looked at the problem from the point of view of modal analysis. This is particularly surprising if considered the central role that modal theory had in the development of classic results in analysis and control of linear mechanical systems. With the aim of making a step toward translating and extending this powerful tool to the robotic field, we present the complete modal characterization of a simple yet representative non-linear elastic robot: the 2D planar mass-spring-damper system. Generic non-linear elastic forces and dissipative effects are considered. We provide here exact descriptions of the two non-linear normal modes of the system. We then extend the analysis to generic combinations of the modes in conservative case and for small damping. Simulations are provided to illustrate the theoretical results. This is one of the very firsts applications of normal mode theory to dynamically coupled non-linear systems, and the first exact result in the field.

Real world applications require robots to operate in unstructured environments. This kind of scenarios may lead to unexpected environmental contacts or undesired interactions, which may harm people or impair the robot. Adjusting the behavior of the system through impedance control techniques is an effective solution to these problems. However, selecting an adequate impedance is not a straightforward process. Normally, robot users manually tune the controller gains with trial and error methods. This approach is generally slow and requires practice. Moreover, complex tasks may require different impedance during different phases of the task. This paper introduces an optimization algorithm for online planning of the Cartesian robot impedance to adapt to changes in the task, robot configuration, expected disturbances, external environment and desired performance, without employing any direct force measurements. We provide an analytical solution leveraging the mass-spring-damper behavior that is conferred to the robot body by the Cartesian impedance controller. Stability during gains variation is also guaranteed. The effectiveness of the method is experimentally validated on the quadrupedal robot ANYmal. The variable impedance helps the robot to tackle challenging scenarios like walking on rough terrain and colliding with an obstacle.

One of the main research trends toward next-generation prostheses and bionic aids is to better replicate human motor behaviours and to improve the interconnection with the human sensory-motor architecture. One of the natural characteristics of the human arm of utmost importance in our interaction with the environment is our ability to vary the mechanical impedance of our joints by commanding the co-contraction of antagonist muscles. Integration in prostheses of such features is currently under studies. The introduction of physical variable impedance in the mechatronic structure of the devices could at the same time improve interaction and robustness and allow for more sophisticated controls with the goal of naturalness of motion. The system proposed in this paper is a variable stiffness elbow joint for upper limb prostheses that reproduces mechanical abilities of the human joint, in terms of performance, inherent compliance and natural behaviour. This variable stiffness mechanism can be actively controlled by the user, and by using an agonist-antagonistic configuration of proper elastic elements, its output functions are similar to the models of the human muscle. The design and mechanical implementation of the device are detailed in this document together with its experimental validation and characterisation.

The development of effective reduced order models for soft robots is paving the way toward the development of a new generation of model based techniques, which leverage classic rigid robot control. However, several soft robot features differentiate the soft-bodied case from the rigid-bodied one. First, soft robots are built to work in the environment, so the presence of obstacles in their path should always be explicitly accounted by their control systems. Second, due to the complex kinematics, the actuation of soft robots is mapped to the state space nonlinearly resulting in spaces with different sizes. Moreover, soft robots often include internal constraints and thus actuation is typically limited in the range of action and it is often unidirectional. This paper proposes a control pipeline to tackle the challenge of controlling soft robots with internal constraints in environments with obstacles. We show how the constraints on actuation can be propagated and integrated with geometrical constraints, taking into account physical limits imposed by the presence of obstacles. We present a hierarchical control architecture capable of handling these constraints, with which we are able to regulate the position in space of the tip of a soft robot with the discussed characteristics.

This paper has developed a coordination control method for a dual-arm exoskeleton robot based on human impedance transfer skills, where the left (master) robot arm extracts the human limb impedance stiffness and position profiles, and then transfers the information to the right (slave) arm of the exoskeleton. A computationally efficient model of the arm endpoint stiffness behavior is developed and a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. A reference command consisting of the stiffness and position profiles of the operator is computed and realized by one robot in real-time. Considering the dynamics uncertainties of the robotic exoskeleton, an adaptive-robust impedance controller in task space is proposed to drive the slave arm tracking the desired trajectories with convergent errors. To verify the robustness of the developed approach, a study of combining adaptive control and human impedance transfer control under the presence of unknown interactive forces is conducted. The experimental results of this paper suggest that the proposed control method enables the subjects to execute a coordination control task on a dual-arm exoskeleton robot by transferring the stiffness from the human arm to the slave robot arm, which turns out to be effective.

Recently, the avenue of adaptable, soft robotic hands has opened simplified opportunities to grasp different items; however, the potential of soft end effectors (SEEs) is still largely unexplored, especially in human-robot interaction. In this paper, we propose, for the first time, a simple touch-based approach to endow a SEE with autonomous grasp sensory-motor primitives, in response to an item passed to the robot by a human (human-to-robot handover). We capitalize on human inspiration and minimalistic sensing, while hand adaptability is exploited to generalize grasp response to different objects. We consider the Pisa/IIT SoftHand (SH), an under-actuated soft anthropomorphic robotic hand, which is mounted on a robotic arm and equipped with Inertial Measurement Units (IMUs) on the fingertips. These sensors detect the accelerations arisen from contact with external items. In response to a contact, the hand pose and closure are planned for grasping, by executing arm motions with hand closure commands. We generate these motions from human wrist poses acquired from a human maneuvering the SH to grasp an object from a table. We obtained 86% of successful grasps, considering many objects passed to the SH in different manners. We also tested our techniques in preliminary experiments, where the robot moved to autonomously grasp objects from a surface. Results are positive and open interesting perspectives for soft robotic manipulation.

Grasp and manipulation is a complex task, deceivingly simple to accomplish for humans in everyday life, yet challenging to implement in a robotic hand. There is a trend in literature to use information obtained from studies on human grasp for the design and control of robotic manipulators. However, the effectiveness of such approach is dependent on the measurement tools that are available for use with human hands. While there are many sensing solutions that are designed for this purpose, obtaining a complete set of measurements of forces during grasp interaction is still challenging. In this work we aim to bridge this gap by introducing ExoSense, a passive hand exoskeleton. This device can provide position and orientation of the fingertips and, when integrated with the fingertip wearable force/torque sensing system ThimbleSense, a complete characterization of manipulation in terms of generalized forces and position of contacts on each fingertip in a completely wearable and unconstrained manner. After validating the device in terms of end-effector posture measurements and overall accuracy of grasp measurements, we report on a preliminary experiment aiming to show the potentialities of the system to study human internal grasp force variations and for neuroscientific investigation in general.

Thanks to the growing interest in soft robotics, hydropneumatics and inflatable system dynamics are attracting renewed attention from the scientific community. Typical fluidic systems are composed of several chambers and require a complex and bulky network of active components for their control. This paper presents a novel approach to fluidic actuation, which consists in the co-design of both the mechanical parameters of the system and of custom input signals, to enable the elicitation of different behaviors of the system with fewer control components. The principle is presented in theory and simulation and then experimentally validated through the application to a case study, an in-pipe inchworm-like robot. It is shown that it is possible to obtain forward and backward movements by modulating a unique input.

This paper aims at the development of a unified method for online identification and tracking of a kinematic musculoskeletal model of human arm to pave the way for related realtime applications, such as human-robot interaction, teleoperation and biomedical analysis. In order to decouple the identification of the joint angles of human arm kinematic model from a variety of motion capture (MoCap) setups, a generalized human arm triangle, which can be easily calculated by raw motion data, is introduced as an intermediate unified expression interface of human arm posture. An analytical solution to the Inverse Kinematics (IK) problem from the proposed human arm triangle to the joint angles of a commonly used OpenSim human right arm model is derived in detail. Once the human arm kinematic model is reconstructed, the involved muscles can be located correspondingly for related applications. Comparative simulation and experiment are conducted to validate the performance of the proposed IK and the whole tracking method. The results manifest that the calculation efficiency of the proposed IK can achieve an enormous speedup of 400-600 times with respect to the OpenSim built-in IK while maintaining comparable accuracy. Therefore, the proposed method can be an important tool to enable many online applications using human arm musculoskeletal model.

Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.

In this paper, two upper limbs of an exoskeleton robot are operated within a constrained region of the operational space with unidentified intention of the human operator's motion as well as uncertain dynamics including physical limits. The new human-cooperative strategies are developed to detect the human subject's movement efforts in order to make the robot behavior flexible and adaptive. The motion intention extracted from the measurement of the subject's muscular effort in terms of the applied forces/torques can be represented to derive the reference trajectory of his/her limb using a viable impedance model. Then, adaptive online estimation for impedance parameters is employed to deal with the nonlinear and variable stiffness property of the limb model. In order for the robot to follow a specific impedance target, we integrate the motion intention estimation into a barrier Lyapunov function based adaptive impedance control. Experiments have been carried out to verify the effectiveness of the proposed dual-arm coordination control scheme, in terms of desired motion and force tracking.

In recent years, a clear trend toward simplification emerged in the development of robotic hands. The use of soft robotic approaches has been a useful tool in this prospective, enabling complexity reduction by embodying part of grasping intelligence in the hand mechanical structure. Several hand prototypes designed according to such principles have accomplished good results in terms of grasping simplicity, robustness, and reliability. Among them, the Pisa/IIT SoftHand demonstrated the feasibility of a large variety of grasping tasks, by means of only one actuator and an opportunely designed tendon-driven differential mechanism. However, the use of a single degree of actuation prevents the execution of more complex tasks, like fine preshaping of fingers and in-hand manipulation. While possible in theory, simply doubling the Pisa/IIT SoftHand actuation system has several disadvantages, e.g., in terms of space and mechanical complexity. To overcome these limitations, we propose a novel design framework for tendon-driven mechanisms, in which the main idea is to turn transmission friction from a disturbance into a design tool. In this way, the degrees of actuation (DoAs) can be doubled with little additional complexity. By leveraging on this idea, we design a novel robotic hand, the Pisa/IIT SoftHand 2. We present here its design, modeling, control, and experimental validation. The hand demonstrates that by opportunely combining only two DoAs with hand softness, a large variety of grasping and manipulation tasks can be performed, only relying on the intelligence embodied in the mechanism. Examples include rotating objects with different shapes, opening a jar, and pouring coffee from a glass.

In this paper we present a novel geometric approach to motion planning for constrained robot systems. This problem is notoriously hard, as classical sampling-based methods do not easily apply when motion is constrained in a zero-measure submanifold of the configuration space. Based on results on the functional controllability theory of dynamical systems, we obtain a description of the complementary spaces where rigid body motions can occur, and where interaction forces can be generated, respectively. Once this geometric setting is established, the motion planning problem can be greatly simplified. Indeed, we can relax the geometric constraint, i.e., replace the lower-dimensional constraint manifold with a full-dimensional boundary layer. This in turn allows us to plan motion using state-of-the-art methods, such as RRT*, on points within the boundary layer, which can be efficiently sampled. On the other hand, the same geometric approach enables the design of a completely decoupled control scheme for interaction forces, so that they can be regulated to zero (or any other desired value) without interacting with the motion plan execution. A distinguishing feature of our method is that it does not use projection of sampled points on the constraint manifold, thus largely saving in computational time, and guaranteeing accurate execution of the motion plan. An explanatory example is presented, along with an experimental implementation of the method on a bimanual manipulation workstation.

An effective robotic wrist represents a key enabling element in robotic manipulation, especially in prosthetics. In this paper, we propose an under-actuated wrist system, which is also adaptable and allows to implement different under-actuation schemes. Our approach leverages upon the idea of soft synergies — in particular the design method of adaptive synergies — as it derives from the field of robot hand design. First we introduce the design principle and its implementation and function in a configurable test bench prototype, which can be used to demonstrate the feasibility of our idea. Furthermore, we report on results from preliminary experiments with humans, aiming to identify the most probable wrist pose during the pre-grasp phase in activities of daily living. Based on these outcomes, we calibrate our wrist prototype accordingly and demonstrate its effectiveness to accomplish grasping and manipulation tasks.

Tele-operation in the presence of environmental constraints is a well-studied problem, where the difficulties of the transparency-stability trade-off have been elucidated by several important studies. While at the state-of-art, passivity-based stabilizers appear to provide the best insight and command over this problem, recent work by our group has proposed an alternative approach, which consists in measuring and replicating the master's limb endpoint impedance on the slave robot in real-time. Tele-impedance control offers advantages in certain conditions, e.g. where master-slave communications are low quality. However, force feedback remains necessary when visual feedback is impaired or transparency and telepresence in the remote environment is of major concern. In this paper, we propose a novel framework to achieve the Tele-Impedance with Force Feedback (TIFF) so as to have a seamless control scheme that subsumes the performance advantages of both, while still guaranteeing stability and transparency. Experimental results illustrate the potential of the proposed technique in addressing the drawbacks of the two concepts.

Sensing contact forces can be a key enabler for higher order dexterous manipulation in robotic hands. To sense the full range of contact pressure distribution would provide the best solution, but it is in practice unfeasible when considering very deformable and adaptable hands. This paper proposes an approach to estimate the contact forces acting on an under-actuated adaptable hand by combining the compliance model of the hand with the geometric configuration of the hand itself. This is done by introducing reasonable assumptions about the net contact force on each phalanx. The proposed method is introduced and experimentally validated on two fingers of the Pisa/IIT SoftHand.

In humanoids and other redundant robots interacting with the environment, one can often choose between different configurations and control parameters to achieve a given task. A classic tool to describe specifications of the desired force/displacement behavior in such problems is the stiffness ellipsoid, whose geometry is affected by the choice of parameters in both joint control and redundancy resolution-namely, gains and angles. As is well known, impedance control techniques can regulate gains to realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector, so that robot geometry selection could appear secondary. However, humans do not use this possibility: To control the stiffness of our arms, we predominantly use arm configurations. Why is that, and does it makes sense to do the same in robots? To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of compliant redundant arms to illustrate why the arm geometry plays a dominant role in interaction capabilities of robots. We introduce the notion of allowable Cartesian force/displacement (“stiffness feasibility”) regions (SFR) for compliant robots with given torque boundaries. We show that different robot configurations modify such regions and explore the role of robot geometry in achieving an appropriate SFR for the task at hand. The novel concepts and definitions are first illustrated in simulations. Experimental results are then provided to verify the effectiveness of the proposed Cartesian force and stiffness control.

In this work, to guarantee the Pisa/IIT SoftHand's grasp robustness against slippage, three reflex control modes, namely Current, Pose and Impedance, are implemented and experimentally evaluated. Towards this objective, ThimbleSense fingertip sensors are designed and integrated into the thumb and middle fingers of the SoftHand for real-time detection and control of the slippage. Current reflex regulates the restoring grasp forces of the hand by modulating the motor's current profile according to an update law. Pose and Impedance reflex modes instead replicate this behaviour by implementing an impedance control scheme. The difference between the two latter is that the stiffness gain in Impedance reflex mode is being varied in addition to the hand pose, as a function of the slippage on the fingertips. Experimental setup also includes a seven degrees-of-freedom robotic arm to realize consistent trajectories (e.g. lifting) among three control modes for the sake of comparison. Different test objects are considered to evaluate the efficacy of the proposed reflex modes in our experimental setup. Results suggest that task-appropriate restoring forces can be achieved using Impedance reflex due to its capability in demonstrating instantaneous and rather smooth reflexive behaviour during slippage. Preliminary experiments on five healthy human subjects provide evidence on the similarity of the control concepts exploited by the humans and the one realized by the Impedance reflex, highlighting its potential in prosthetic applications.

The deployment of robots to assist in environments hostile for humans during emergency scenarios require robots to demonstrate enhanced physical performance, that includes adequate power, adaptability and robustness to physical interactions and efficient operation. This work presents the design and development of the lower body of the new high performance humanoid WALK-MAN, a robot developed recently to assist in disaster response scenarios. The paper introduces the details of the WALK-MAN lower-body, highlighting the innovative design optimization features considered to maximize the leg performance. Starting from the general lower body specifications the objectives of the design and how they were addressed are introduced, including the selection of the leg kinematics, the arrangement of the actuators and their integration with the leg structure to maximize the range of motion, reduce the leg mass and inertia, and shape the leg mass distribution for better dynamic performance. Physical robustness is ensured with the integration of elastic transmission and impact energy absorbing covers. Experimental walking trials demonstrate the correct operation of the legs while executing a walking gait.

State of the art of hand prosthetics is divided between simple and reliable gripper-like systems and sophisticate hi-tech poly-articular hands which tend to be complex both in their design and for the patient to operate. In this paper, we introduce the idea of decoding different movement intentions of the patient using the dynamic frequency content of the control signals in a natural way. We move a step further showing how this idea can be embedded in the mechanics of an underactuated soft hand by using only passive damping components. In particular we devise a method to design the hand hardware to obtain a given desired motion. This method, that we call of the dynamic synergies, builds on the theory of linear descriptor systems, and is based on the division of the hand movement in a slow and a fast components. We use this method to evolve the design of the Pisa/IIT SoftHand in a prototype prosthesis which, while still having 19 degrees of freedom and just one motor, can move along two different synergistic directions of motion (and combinations of the two), to perform either a pinch or a power grasp. Preliminary experimental results are presented, demonstrating the effectiveness of the proposed design.

In this paper a quasi-static framework for optimally controlling the contact force distribution is experimentally verified with the full-size compliant humanoid robot Walk-Man. The proposed approach is general enough to cope with multi-contact scenarios, i.e. robot-environment interactions occurring on feet and hands, up to the more general case of whole-body loco-manipulation, in which the robot is in contact with the environment also with the internal limbs, with a consequent loss of contact force controllability. Experimental tests were conducted with the Walk-Man robot (i) standing on flat terrain, (ii) standing on uneven terrain and (iii) interacting with the environment with both feet and a hand touching a vertical wall. Moreover, the influence of unmodeled weight on the robot, and the combination with a higher priority Cartesian tasks are shown. Results are presented also in the attached video.

The tutor-tutee hand-in-hand teaching may be the most effective approach for a tutee to acquire new motor skills. Repetitive nature of such procedures in a group setting usually results in a high labour cost and time inefficiency. Potential solution can be utilizing robotic platforms playing the role of tutors for demonstrating and transferring the required skills. This requires an appropriate guidance scheme to integrate the tutor's motor functionalities into the robot's control architecture. For instance, for hand-in-hand supervision of the writing task, the tutor's corrections can be applied when necessary, while a very compliant motion can be achieved if no errors are detected. Inspired by this behavior, we develop a teaching interface using a dual-arm robotic platform. In our setup, one arm is connected to the tutees arm providing guidance through a variable stiffness control approach, and the other to the tutor to capture the motion and to feedback the tutees performance in a haptic manner. The reference stiffness for the tutors arm stiffness is estimated in real-time and replicated by the tutees robotic arm. Comparative experiments have been carried out on a dual-arm Baxter robot. The results imply that the human tutor is able to intuitively transfer writing skills to the tutee and also show superior learning performance over over some conventional teaching by demonstration techniques.

Haptic interfaces are special robots that interact with people to convey touch-related information. In addition to such a discriminative aspect, touch is also a highly emotion-related sense. However, while a lot of effort has been spent to investigate the perceptual mechanisms of discriminative touch and to suitably replicate them through haptic systems in human robot interaction (HRI), there is still a lot of work to do in order to take into account also the emotional aspects of tactual experience (i.e., the so-called affective haptics), for a more naturalistic human-robot communication. In this paper, we report evidences on how a haptic device designed to convey caress-like stimuli can influence physiological measures related to the autonomous nervous system (ANS), which is intimately connected to evoked emotions in humans. Specifically, a discriminant role of electrodermal response and heart rate variability can be associated to two different caressing velocities, which can also be linked to two different levels of pleasantness. Finally, we discuss how the results from this study could be profitably employed and generalized to pave the path towards a novel generation of robotic devices for HRI.

Softness and texture high-frequency information represent fundamental haptic properties for every day life activities and environment tactual exploration. While several displays have been produced to convey either softness or high-frequency information, there is no or little evidence of systems that are able to reproduce both these properties in an integrated fashion. This aspect is especially crucial in medical tele-operated procedures, where roughness and stiffness of human tissues are both important to correctly identify given pathologies through palpation (e.g. in tele-dermatology). This work presents a fabric yielding display (FYD-pad), a fabric-based tactile display for softness and texture rendering. The system exploits the control of two motors to modify both the stretching state of the elastic fabric for softness rendering and to convey texture information on the basis of accelerometer-based data. At the same time, the measurement of the contact area can be used to control remote or virtual robots. In this paper, we discuss the architecture of FYD-pad and the techniques used for softness and texture reproduction as well as for synthesizing probe-surface interactions from real data. Tele-operation examples and preliminary experiments with humans are reported, which show the effectiveness of the device in delivering both softness and texture information.

The computation of skin forces and deformations for tactile rendering requires an accurate model of the extremely nonlinear behavior of the skin. In this work, we investigate the characterization of finger mechanics with the goal of designing accurate nonlinear models for tactile rendering. First, we describe a measurement setup that enables the acquisition of contact force and contact area in the context of controlled finger indentation experiments. Second, we describe an optimization procedure that estimates the parameters of strain-limiting deformation models that match best the acquired data. We show that the acquisition setup allows the measurement of force and area information with high repeatability, and the estimation method reaches nonlinear models that match the measured data with high accuracy.

Modern approaches to the design of robots with increasing amounts of embodied intelligence affect human-robot interaction paradigms. The physical structure of robots is evolving from traditional rigid, heavy industrial machines into soft bodies exhibiting new levels of versatility, adaptability, safety, elasticity, dynamism and energy efficiency. New challenges and opportunities arise for the control of soft robots: for instance, carefully planning for collision avoidance may no longer be a dominating concern, being on the contrary physical interaction with the environment not only allowed, but even desirable to solve complex tasks. To address these challenges, it is often useful to look at how humans use their own bodies in similar tasks, and even in some cases have a direct dialogue between the natural and artificial counterparts

The stiffness ellipsoid, i.e. the locus of task-space forces obtained corresponding to a deformation of unit norm in different directions, has been extensively used as a powerful representation of robot interaction capabilities. The size and shape of the stiffness ellipsoid at a given end-effector posture are influenced by both joint control parameters and - for redundant manipulators - by the chosen redundancy resolution configuration. As is well known, impedance control techniques ideally provide control parameters which realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector in an arbitrary non-singular configuration, so that arm geometry selection could appear secondary. This definitely contrasts with observations on how humans control their arm stiffness, who in fact appear to predominantly use arm configurations to shape the stiffness ellipsoid. To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of redundant arms, which explains why arm geometry also plays a fundamental role in interaction capabilities of a torque controlled robot. We show that stiffness control of realistic robot models with bounds on joint torques can't indeed achieve arbitrary stiffness ellipsoids at any given arm configuration. We first introduce the notion of maximum allowable Cartesian force/displacement (“stiffness feasibility”) regions for a compliant robot. We show that different robot configurations modify such regions, and explore the role of different configurations in defining the performance limits of Cartesian stiffness controllers. On these bases, we design a stiffness control method that suitably exploits both joint control parameters and redundancy resolution to achieve desired task-space interaction behavior.

Random sampling-based methods for motion planning of constrained robot manipulators have been widely studied in recent years. The main problem to deal with is the lack of an explicit parametrization of the non linear submanifold in the Configuration Space (CS) imposed by the constraints in the system. Most of the proposed planning methods use projections to generate valid configurations of the system slowing the planning process. Recently, new robot mechanism includes compliance either in the structure or in the controllers. In this kind of robot most of the times the planned trajectories are not executed exactly due to uncertainties and interactions with the environment. Indeed, controller references are generated such that the constraint is violated to indirectly generate forces during interactions. With the purpose of avoiding projections, in this paper we take advantage of the compliance of systems to relax the geometric constraints imposed by closed kinematic chains. The relaxed constraint is then used in a state-of-the-art suboptimal random sampling based technique to generate paths for constrained robot manipulators. As a consequence of relaxation, arising contact forces acting on the constraint change from configuration to configuration during the planned path. Those forces can be regulated using a proper controller that takes advantage of the geometric decoupling of the subspaces describing constrained rigid-body motions of the mechanism and the controllable forces.

The term whole-body loco-manipulation refers to the case in which a humanoid robot exploits contacts with the environment, both with the end-effectors and with its internal limbs, in order to balance, move and/or manipulate the environment. In such a situation, high degree of redundancy may not be sufficient to completely control the robot movements and/or the forces applied on the environment. This problem is tackled in this work by means of quasi-static analysis tools. The reduction of mobility and manipulability is studied introducing the Fundamental Loco-Manipulation Matrix (FLMM) and its canonical form (cFLMM). Relevant information on the system can be extracted from those, obtaining, e.g., the space of the controllable contact forces, and the controllable displacements of the center of mass. Furthermore, the best contact force distribution able to meet the friction cone constraints is demonstrated to be the solution of a convex optimization problem. The validity of the proposed methods is verified in two numerical examples, where internal contacts affects the controllability of both forces and displacements. Numerical results show that is crucial to consider the correlations between contact forces in order to exert target actions on the environment while coping with friction limits on the whole set of contacts.

In this paper, we present a method to reconstruct the configurations of kinematic trees of rigid bodies not using measurements of relative angles (such as, e.g. rotary encoders at joints) but absolute posture sensors (such as IMUs) along with suitable filter algorithms. We argue that the relatively larger inaccuracies shown by absolute sensors can be compensated by suitable processing, such as a passive complementary filters exploiting the Mahony-Hamel formulation. The proposed method is applicable to systems where measurements of relative angles is not feasible or convenient, or where the joint kinematics are not lower pairs: for example, human body parts or soft robotic devices. In the paper, we make explicit reference to the reconstruction of posture of the compliant, underactuated Pisa/IIT SoftHand. Quantitative comparisons with ground truth data in grasping tests are used to validate the proposed method. The resulting hardware design is mechanically robust, cheap and can be easily adapted to robotic hands with different structures, as well as to sensorizing gloves for studying human grasping strategies.

Effective and stable execution of a remote manipulation task in an uncertain environment requires that the task force and position trajectories of the slave robot be appropriately commanded. To achieve this goal, in teleimpedance control, a reference command which consists of the stiffness and position profiles of the master is computed and realized by the compliant slave robot in real-time. This highlights the need for a suitable and computationally efficient tracking of the human limb stiffness profile in real-time. In this direction, based on the observations in human neuromotor control which give evidence on the predominant use of the arm configuration in directional adjustments of the endpoint stiffness profile, and the role of muscular co-activations which contribute to a coordinated regulation of the task stiffness in all directions, we propose a novel and computationally efficient model of the arm endpoint stiffness behaviour. Real-time tracking of the human arm kinematics is achieved using an arm triangle monitored by three markers placed at the shoulder, elbow and wrist level. In addition, a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. Calibration and identification of the model parameters are carried out experimentally, using perturbation-based arm endpoint stiffness measurements in different arm configurations and co-contraction levels of the chosen muscles. Results of this study suggest that the proposed model enables the master to naturally execute a remote task by modulating the direction of the major axes of the endpoint stiffness and its volume using arm configuration and the co-activation of the involved muscles, respectively.

In this paper, we present a method to plan grasps for soft hands. Considering that soft hands can easily conform to the shape an the object, with preference to certain types of basic geometries and dimensions, we decompose the object into one type of these geometries, particularly into Minimal Volume Bounding Boxes (MVBBs), which are proved to be efficiently graspable by the hand we use. A set of hand poses are then generated using geometric information extracted from such MVBBs. All hand postures are used in a dynamic simulator of the PISA/IIT Soft Hand and put on a test to evaluate if a proposed hand posture leads to a successful grasp. We show, through a set of numerical simulations, that the probability of success of the hand poses generated with the proposed algorithm is very good and represents an evident improvement with respect to our previous results published in [1].

Rendering forces to the user is one of the main goals of haptic technology. While most force-feedback interfaces are robotic manipulators, attached to a fixed frame and designed to exert forces on the users while being moved, more recent haptic research introduced two novel important ideas. On one side, cutaneous stimulation aims at rendering haptic stimuli at the level of the skin, with a distributed, rather than, concentrated approach. On the other side, wearable haptics focuses on highly portable and mobile devices, which can be carried and worn by the user as the haptic equivalent of an mp3 player. This paper presents a light and simple wearable device (CUFF) for the distributed mechano-tactile stimulation of the user's arm skin with pressure and stretch cues, related to normal and tangential forces, respectively. The working principle and the mechanical and control implementation of the CUFF device are presented. Then, after a basic functional validation, a first application of the device is shown, where it is used to render the grasping force of a robotic hand (the Pisa/IIT SoftHand). Preliminary results show that the device is capable to deliver in a reliable manner grasping force information, thus eliciting a good softness discrimination in users and enhancing the overall grasping experience.

In this paper a model-free approach for damping control of Variable Stiffness Actuators is proposed. The idea is to take advantage of the possibility to change the stiffness of the actuators in controlling the damping. The problem of minimizing the terminal energy for a one degree of freedom spring-mass model with controlled stiffness is first considered. The optimal bang-bang control law uses a maximum stiffness when the link gets away from the desired position, i.e. the link velocity is decreasing, and a minimum one when the link is going towards it, i.e. the link velocity is increasing. Based on Lyapunov stability theorems the obtained law has been proved to be stable for a multi-DoF system. Finally, the proposed control law has been tested and validated through experimental tests.

Two leading qualities of skeletal muscle that produce good performance in uncertain environments are damage tolerance and the ability to modulate impedance. For this reason, robotics researchers are greatly interested in discovering the key characteristics of muscles that give them these properties and replicating them in actuators for robotic devices. This paper describes a method to harness the redundancy present in muscle-like actuation systems composed of multiple motor units and shows that they have these same two qualities. By carefully choosing which motor units are recruited, the impedance viewed from the environment can be modulated while maintaining the same overall activation level. The degree to which the impedance can be controlled varies with total activation level and actuator length. Discretizing the actuation effort into multiple parts that work together, inspired by the way muscle fibers work in the human body, produces damage-tolerant behavior. This paper shows that this not only produces reasonably good resolutions without inordinate numbers of units, but gives the control system the ability to set the impedance along with the drive effort to the load.

In this work we tackle the problem of planning grasps for an underactuated gripper which enable it to retrieve target objects from a cluttered environment. Furthermore, we investigate how additional manipulation capabilities of the gripping device, provided by active surfaces on the inside of the fingers, can lead to performance improvement in the grasp execution process. To this end, we employ a simple strategy, in which the target object is `pulled-in' towards the palm during grasping which results in firm enveloping grasps. We show the effectiveness of the suggested methods by means of experiments conducted in a real-world scenario.

Incorporation of human motor control principles in the motion control architectures for humanoid robots or assistive and prosthesis devices will permit these systems not only to look anthropomorphic and natural at the body ware level but also to generate natural motion profiles resembling those executed by humans during manipulation and locomotion. In this work, relying on the observations on human bimanual coordination, a novel realtime motion control strategy is proposed to regulate the desired Cartesian stiffness profile during the execution of bimanual tasks. The novelty of the proposed control scheme relies on the use of common mode stiffness (CMS) and configuration dependent stiffness (CDS) to regulate the size and directionality of the task space stiffness ellipsoid. Thanks to the CDS control, the proposed scheme is not only proved to be effective in regulating the desired stiffness ellipsoid but also permits to resolve the manipulator redundancy in a natural manner. The effectiveness of the controller is evaluated in an experimental setup in which two cooperating robotic arms are executing an assembly task. Experimental results demonstrate that the proposed dual-arm CDS-CMS controller is effective in tracking the desired stiffness ellipsoids as well as in producing human-like natural motions for the two robotic arms.

Measuring contact forces applied by a hand to a grasped object is a necessary step to understand the mysteries that still hide in the unparalleled human grasping ability. Nevertheless, simultaneous collection of information about the position of contacts and about the magnitude and direction of forces is still an elusive task. In this paper we introduce a wearable device that addresses this problem, and can be used to measure generalized forces during grasping. By assembling two supports around a commercial 6-axis force/torque sensor we obtain a thimble that can be easily positioned on a fingertip. The device is used in conjunction with an active marker-based motion capture system to simultaneously obtain absolute position and orientation of the thimbles, without requiring any assumptions on the kinematics of the hand. Finally, using the contact centroid algorithm, introduced in [1], position of contact points during grasping are determined. This paper shows the design and implementation of the device, as well as some preliminary experimental validation.

This work presents an approach for exploring unknown surfaces with discontinuities using only force/torque information. The motivation is to build an information map of an unknown object or environment by performing a fully-autonomous haptic exploration. Examples of discontinuities considered here are contours with sharp turns (such as wall corners) and abrupt dips (such as cliffs). Compliant motion control using force information has the ability to conform to unknown, smooth surfaces but not to discontinuous surfaces. This paper investigates solutions to address the limitation in compliant motion control over discontinuities while maintaining a desired normal force along the surface. We propose two methods to address the problem: (1) superposition of motion and force control and (2) rotation of axes for force and motion control. The theoretical principles are discussed and experimental results with a KUKA lightweight arm moving in 2D space are presented. Both approaches successfully negotiate objects with sharp 90-degree and 120-degree turns while still maintaining good tracking of the desired force.

One interesting field of robotics technology is related to the entertainment industry. Performing a musical piece using a robot is a difficult task because music presents many features like melody, rhythm, tone, harmony and so on. Addressing these tasks with a robot is not trivial to implement. Most of approaches which related to this specific field lacks of quality to perform in front of human audience. Implementation of human-like motions can not be properly achieved with a conventional robot actuator. Consequently, we exploit a new type of actuator which simplifies the drawbacks of a conventional one. We used Variable Stiffness Actuator(VSA) instead of using conventional actuator. We can control position, force, and stiffness, simultaneously by using VSA. The most important novel feature is its controllable stiffness. When the stiffness of the actuator is changed, the characteristics of the actuator's response also changes. We implemented the specific stroke which is called “double stroke” using one of variable stiffness actuator. Although the double stroke is known as a special stroke which could be performed by human only, double stroke is successfully implemented by stiffness variation.

This work proposes a representation that comprises both shape and friction, as well as the exploration strategy to gather them from an object. The representation is developed under a common probabilistic framework, particularly it uses a Gaussian Process to approximate the distribution of the friction coefficient over the surface, also represented as a Gaussian Process. The surface model is exploited to compute straight lines (geodesic flows) that guide the exploration. The exploration follows these flows by employing an impedance controller in pursuance of safety, shape accommodation and contact enforcement, while measuring the necessary data to estimate the friction coefficient. The exploratory probes consist of an RGBD camera and an Intrinsic Tactile sensor (ITs) mounted on a robotic arm. Experimental results give evidence for the effectiveness of the algorithm in the friction coefficient gathering and enrichment of the object representation.

In this paper we report on the development of a modular multi-DoF F/T sensor and its use in the implementation of a sensorized object capable of multi-touch detection. The sensor is composed of six 6-axis F/T sensors spatially organized on the faces of a cube. Different calibration methods are presented to directly tackle the coupling phenomena inherent to the spatial organization of the faces and the lightweight construction of the sensor which would have, otherwise, degraded its accuracy. To assess the performances of the calibration methods, a comparison is reported with respect to the measurements obtained with a commercial force/torque sensor considered as ground truth (ATI Delta). Thanks to the modular design and the possibility to cover the sensitive faces with surface patches of different geometry, a variety of sensorized objects with different shapes can be realized. The peculiar feature that all the components of the contact wrench can be measured on each face with high accuracy, renders it a unique tool in the study of grasp force distribution in humans, with envisioned use both in neuroscience investigations and robotic applications.

Safe physical human-robot interaction, conservation of energy, and adaptability are the main robotic applications that prompted the development of a number of variable stiffness actuators (VSAs). Implemented in a variety of ways, they use various technologies and feature the most diverse mechanical solutions, all of which share a fundamentally unavoidable nonlinear behavior. The control schemes proposed for these actuators typically aim at independent control of the position of the link and its stiffness. Although effective feedback control schemes using position and force sensors are commonplace in robotics, control of stiffness is at present completely open loop: The stiffness is inferred from the mathematical model of the actuator. We consider here the problem of estimating the nonlinear stiffness of VSA in agonistic-antagonistic configuration. We propose an algorithm based on modulating functions that allow us to avoid the need for numerical derivative and for which the tuning is then very simple. An analysis of the error demonstrates the convergence. Simulations are provided, and the algorithm is validated on experimental data.

Humans incorporate and switch between learnt neuromotor strategies while performing complex tasks. Towards this purpose, kinematic redundancy is exploited in order to achieve optimized performance. Inspired by the superior motor skills of humans, in this paper, we investigate a combined free motion and interaction controller in a certain class of robotic manipulation. In this bimodal controller, kinematic degrees of redundancy are adapted according to task-suitable dynamic costs. The proposed algorithm attributes high priority to minimum-effort controller while performing point to point free space movements. Once the robot comes in contact with the environment, the Tele-Impedance, common mode and configuration dependent stiffness (CMS-CDS) controller will replicate the human's estimated endpoint stiffness and measured equilibrium position profiles in the slave robotic arm, in real-time. Results of the proposed controller in contact with the environment are compared with the ones derived from Tele-Impedance implemented using torque based classical Cartesian stiffness control. The minimum-effort and interaction performance achieved highlights the possibility of adopting human-like and sophisticated strategies in humanoid robots or the ones with adequate degrees of redundancy, in order to accomplish tasks in a certain class of robotic manipulation.

The CompAct™ actuator features a clutch mechanism placed in parallel with its passive series elastic transmission element and can therefore benefit from the advantages of both series elastic actuators (SEA) and rigid actuators. The actuator is capable of effectively managing the storage and release of the potential energy of the compliant element by the appropriate control of the clutch subsystem. Controlling the timing of the energy storage/release in the elastic element is exploited for improving motion control in this research. This paper analyses how this class of actuation systems can be used to maximize the link velocity of the joint. The dynamic model of the joint is derived and an optimal control strategy is proposed to identify optimal input reference profiles for the actuator (motor position/velocity and clutch activation timing) which permit the link velocity maximization. The effect of compliance of the joint on the performance of the system is studied and the optimal stiffness is analyzed.

A properly designed elastic actuation can increase the jumping height that a legged robot can reach. In this paper we compare the two most popular conceptual soft actuator designs, parallel elastic (PEA) and series elastic (SEA), in the task of maximizing the jumping height. Such task is translated into an optimal control problem. For a simplified version of the problem an analytical solution is provided, while a problem with more realistic constraints (e.g. the linear torque-speed motor characteristic is taken into account) is stated as a convex optimization problem and numerically solved. The results show that: (i) given the power of the motor there exists an optimal constant stiffness that maximizes the performance for both the SEA and the PEA; (ii) the optimal stiffness depends on the task terminal time, the inertial parameters of the system and the reduction ratio of the motor; (iii) in the condition considered the SEA behaves better than the PEA.

In this paper, a knee exoskeleton device and its Tele-Impedance based assistive control scheme is presented. The exoskeleton device is an inherently compliant actuated system that was implemented based on the series elastic actuation (SEA) to provide improved and intrinsically soft interaction behaviour. Details of the exoskeleton design are presented. A detailed musculoskeletal model was developed and experimentally identified in order to map electromyographic signals to the antagonistic muscle torques, acting on the human knee joint. The estimated muscle torques are used in order to determine the user's intent and joint stiffness trend. These reference signals are exploited by a novel Tele-Impedance controller which is applied to a knee exoskeleton device to provide assistance and stiffness augmentation to the user's knee joint. Experimental trials of a standing-up motion task were carried out for evaluation of the proposed control strategy. The results indicate that the proposed knee exoskeleton device and control scheme can effectively generate assistive actions that are intrinsically and naturally controlled by the user muscle activity.

SynGrasp is a MATLAB toolbox developed for the analysis of grasping, suitable both for robotic and human hands. It includes functions for the definition of hand kinematic structure and of the contact points with a grasped object. The coupling between joints induced by an underactuated control can be modeled. The hand modeling allows to define compliance at the contact, joint and actuator levels. The provided analysis functions can be used to investigate the main grasp properties: controllable forces and object displacement, manipulability analysis, grasp quality measures. Functions for the graphical representation of the hand, the object and the main analysis results are provided.

We consider the problem of estimating the time-varying stiffness in real-time of a Variable Stiffness Actuator in an agonistic-antagonistic configuration. The estimation of the stiffness is done in two steps. First, we use operational calculus which provides a relation between the positions/velocities of the motors and the link, the torques of the motors and the stiffness. Second, we combine the obtained relation with a polynomial approximation of the stiffness and a recursive least square algorithm to fit the data. Simulations and experimental results are provided and demonstrate the effectiveness of the proposed approach.

Since the introduction of the first prototypes of robotic end-effectors showing manipulation capabilities, much research focused on the design and control of robot hand and grippers. While many studies focus on enhancing the sensing capabilities and motion agility, a less explored topic is the engineering of the surfaces that enable the hand to contact the object. In this paper we present the prototype of the Velvet Fingers smart gripper, a novel concept of end-effector combining the simple mechanics and control of under-actuated devices together with high manipulation possibilities, usually offered only by dexterous robotic hands. This enhancement is obtained thanks to active surfaces, i.e. engineered contact surfaces able to emulate different levels of friction and to apply tangential thrusts to the contacted object. Through the paper particular attention is dedicated to the mechanical implementation, sense drive and control electronics of the device; some analysis on the control algorithms are reported. Finally, the capabilities of the prototype are showed through preliminary grasps and manipulation experiments.

In this paper, a novel synergy driven teleimpedance controller for the Pisa-IIT SoftHand is presented. Towards the development of an efficient, robust, and low-cost hand prothesis, the Pisa-IIT SoftHand is built on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of objects of various shapes using only a single motor. In this work, the hand grasping motion is regulated with an impedance controller which incorporates the user's postural and stiffness synergy profiles in realtime. In addition, a disturbance observer is realized which estimates the grasping contact force. The estimated force is then fedback to the user via a vibration motor. Grasp robustness and transparency improvements were evaluated on two healthy subjects while grasping different objects. Implementation of the proposed teleimpedance controller led to the execution of stable grasps by controlling the grasping forces, via modulation of hand compliance. In addition, utilization of the vibrotactile feedback resulted in reduced physical load on the user. While these results need to be validated with amputees, they provide evidence that a low-cost, robust hand employing hardware-based synergies is a viable alternative to traditional myoelectric prostheses.

In this paper, we propose a general method to achieve a desired grasp compliance acting both on the joint stiffness values and on the hand configuration, also in the presence of restrictions caused by synergistic underactuation. The approach is based on the iterative exploration of the equilibrium manifold of the system and the quasi-static analysis of the governing equations. As a result, the method can cope with large commanded variations of the grasp stiffness with respect to an initial configuration. Two numerical examples are illustrated. In the first one, a simple 2D hand is analyzed so that the obtained results can be easily verified and discussed. In the second one, to show the method at work in a more realistic scenario, we model grasp compliance regulation for a DLR/HIT hand II grasping a ball.

In this paper a fabric yielding softness display (FYD-2) is proposed, where the stretching state is controlled using two motors, while the contact area is measured in real-time. In previous works, authors proposed a fabric-based device, with embedded contact area measurement system, which was proved to provide subjects with a compelling and naturalistic softness perception. Compared to it, FYD-2 exhibits reduced dimensions, a more accurate sensorization scheme and an increased actuation velocity, which allows to implement fast changes in the stretching state levels. These changes are mandatory, for example, to properly track typical quadratic force/area curves of real materials. Furthermore, FYD-2 is endowed with an additional degree of freedom that can be used to convey supplementary haptic cues, such as directional cues, which can be exploited to produce more immersive haptic interactions. In this work we describe the mechanical design and the mathematical model of the device. The reliability in real-time tracking of stiffness and force-area curves of real objects is also demonstrated.

Industrial grippers are often used for grasping, while in-hand re-orientation and positioning are dealt with by other means. Contact surface engineering has been recently proposed as a possible mean to introduce dexterity in simple grippers, as in the Velvet Fingers smart gripper, a novel concept of end-effector combining simple under-actuated mechanics and high manipulation possibilities, thanks to conveyors which are built in the finger pads. This paper undergoes the modeling and control of the active conveyors of the Velvet Fingers gripper which are rendered able to emulate different levels of friction and to apply tangential thrusts to the contacted objects. Through the paper particular attention is dedicated to the mechanical implementation, sense drive and control electronics of the device. The capabilities of the prototype are showed in some grasping and manipulation experiments.

To adapt to many different objects and tasks, hands are very complex systems with many degrees of freedom (DoFs), sensors, and actuators. In robotics, such complexity comes at the cost of size and weight of the hardware of devices, but it strongly affects also the ease of their programming. A possible approach to simplification consists in coupling some of the DOFs, thus affording a reduction of the number of effective inputs, and eventually leading to more efficient, simpler, and reliable designs. Such coupling can be at the software level, to achieve faster, more intuitive programmability or at the hardware level, through either rigid or compliant physical couplings between joints. Physical coupling between actuators and simplification of control through the reduction of independent inputs is also an often-reported interpretation of human hand movement data, where studies have demonstrated that few “postural synergies” explain most of the variance in hand configurations used to grasp different objects. Together with beneficial simplifications, the reduction of the number of independent inputs to a few coupled motions or “synergies” has also an impact on the ability of the hand to dexterously control grasp forces and in-hand manipulation. This paper aims to develop tools that establish how many synergies should be involved in a grasp to guarantee stability and efficiency, depending on the task and on the hand embodiment. Through the analysis of a quasi-static model, grasp structural properties related to contact force and object motion controllability are defined. Different compliant sources are considered, for a generalization of the discussion. In particular, a compliant model for synergies assumed, referred to as “soft synergies,” is discussed. The controllable internal forces and motions of the grasped object are related to the actuated inputs. This paper investigates to what extent a hand with many joints can exploit postural synergies to control force and motion of the grasped object.

This work presents the novel concept of Tele-Impedance as a method for controlling/teleoperating a robotic arm while performing tasks which require significant dynamics variation. As an alternative method to bilateral force-reflecting teleoperation control approach, which uses a position/velocity command combined with force feedback from the robot side, Tele-Impedance enriches the command sent to the slave robot by combining the position reference with a stiffness (or full impedance) reference estimated from the arm of the human operator. We propose a new method to estimate the stiffness of the human arm based on the agonist-antagonist muscular co activations. The concept of the Tele-Impedance is demonstrated using the KUKA light weight robotic arm as the slave manipulator in a ball reception experiment. The performance of Tele-Impedance control method is assessed by comparing the results obtained while receiving the ball, with the slave arm under i) constant low stiffness, ii) constant high stiffness or iii) under Tele-Impedance control. Performance indexes are defined and used for the comparative study of the ball reception performances under the different endpoint elastic profiles. The experimental results demonstrate the effectiveness of the task-related Tele-Impedance control method and highlight its potential use to execute tasks which require significant dynamics variation.

Recent robotic research recognized the advantages that Variable Impedance Actuators would yield to a new generation of robots, rendering them adapt to many different tasks of everyday life.

In dexterous grasping, the development of simple but practical hands with reduced number of actuators, designed to perform some manipulation tasks, is both attractive and challenging. To carefully synthesize inter- and intra-finger couplings a rigorous way to establish grasping and manipulation properties of an underactuated hand is of paramount importance. In this paper, we propose a general approach to characterize the structural properties of underactuated hands focusing on their kinematic and force analysis. A complete kinostatic characterization of a given grasp (pure squeeze, spurious squeeze, kinematic grasp displacements and so on) is introduced. The analysis is quasi-static but it is not limited to rigid-body motions, encompassing also essential elastic motions, statically indeterminate configurations, and pre-loaded initial conditions. The introduction of generalized compliance at contacts and in the actuation mechanism is included, as it is an essential feature of safe and dependable modern hands. Efficient algorithms to characterize the system behavior are presented and applied in two different numerical examples.

The importance of Variable Stiffness Actuators (VSA) in safety and performance of robots has been extensively discussed in the last decade. It has also been shown recently that a VSA brings performance advantages with respect to common actuators. For instance, the solution of the optimal control problem of maximizing the speed of a VSA for impact maximization at a given position with free final time is achieved by applying a control policy that synchronizes stiffness changes with link speed and acceleration. This problem can be regarded as the formalization of the performance of a soccer player's free kick.

This work presents an example of the application of passive impedance control of a variable stiffness manipulator, which shows the actual benefits of variable stiffness in rejecting disturbances without resorting to the closure of a high level feedback loop. In the experiment a 4-DOF manipulator arm, built with the VSA-CubeBot platform, is controlled to hold a pen and draw a circle on an uneven surface. The control is designed calculating joint and stiffness trajectories with a Cartesian approach to the problem, thus designing the optimal workspace stiffness at first. Then, the joint stiffness yielding the closest workspace stiffness is searched for. Experimental results are reported, which agree with the theoretical outcomes, showing that the sub-optimal joints stiffness settings allow the arm to follow the circular trajectory on the uneven surface at best.

This work proposes a solution to the grasp synthesis problem, which consist of finding the best hand configuration to grasp a given object for a specific manipulation task while satisfying all the necessary constraints. This problem had been divided into sequential sub-problems, including contact region determination, hand inverse kinematics and force distribution, with the particular constraints of each step tackled independently. This may lead to unnecessary effort, such as when one of the problems has no solution given the output of the previous step as input. To overcome this issue, we present a kinestatic formulation of the grasp synthesis problem that introduces compliance both at the joints and the contacts. This provides a proper framework to synthesize a feasible and prehensile grasp by considering simultaneously the necessary grasping constraints, including contact reachability, object restraint, and force controllability. As a consequence, a solution of the proposed model results in a set of hand configurations that allows to execute the grasp using only a position controller. The approach is illustrated with experiments on a simple planar hand using two fingers and an anthropomorphic robotic hand using three fingers.

This paper investigates the optimal design of low-cost gloves for hand pose sensing. This problem becomes particularly relevant when limits on the production costs of sensing gloves are taken into account. These cost constraints may limit both the number and the quality of sensors used as well as the technology adopted. For this reason, an optimal distribution of sensors on the glove during the design phase is mandatory in order to obtain good hand pose reconstruction. In this paper, by exploiting the knowledge on how humans most frequently use their hands in grasping tasks, we study the problem of how and where to place sensors on the glove in order to get the maximum information about the actual hand posture, and hence minimize in average the reconstruction error. Simulations and experiments of reconstruction performance are reported to validate the proposed optimal design of sensing devices.

To match the richness and complexity of the sensory and motor functionalities of a human hand with a robust and economically reasonable robotic device remains one of the hardest challenges in the field. Previous work has explored the possibility to exploit insight from neuroscientific results on postural correlation patterns (synergies) taming the sensorimotor complexity of hands. The postural synergy model has been recently extended to account for grasp force control through a model of “soft synergies” which incorporate hand compliance. In this paper we propose a first translation of such principles in the design of a robot hand. It so turns out that the implementation of the soft synergy model in an effective design is not obvious. The solution proposed in this paper rests on ideas coming from under-actuated hand design. We give a synthesis method to realize a desired set of soft synergies through the principled design of adaptive under-actuated mechanisms, which we call the method of adaptive synergies. This approach leads to the design and implementation of a prototype modular hand capable of accommodating an arbitrary number of synergies. The effectiveness of the design is shown in grasping simulations and experiments.

Enabling robots to safely interact with humans is an essential goal of robotics research. The developments achieved over the last years in mechanical design and control made it possible to have active cooperation between humans and robots in rather complex situations. In these terms, safe behavior of the robot even under worst-case situations is crucial and forms also a basis for higher level decisional aspects. In order to quantify what safe behavior really means, the definition of injury, as well as understanding its general dynamics are essential. This insight can then be applied to design and control robots such that injury due to robot-human impacts is explicitly taken into account. In this paper we approach the problem from a medical injury analysis point of view in order to formulate the relation between robot mass, velocity, impact geometry, and resulting injury qualified in medical terms. We transform these insights into processable representations and propose a motion supervisor that utilizes injury knowledge for generating safe robot motions. The algorithm takes into account the reflected inertia, velocity, and geometry at possible impact locations. The proposed framework forms a basis for generating truly safe velocity bounds that explicitely consider the dynamic properties of the manipulator and human injury.

The design of grasping and manipulation systems is one of the most investigated topics in recent robotic and automation engineering. It is a process that has to take into account many development possibilities and to face different trade offs, as that between application possibilities and design complexity. In this work we present the design of a novel end-effector that merges the essential mechanics and control simplicity of underactuated devices, together with the high levels of manipulability usually featured in dexterous robotic hands. To obtain this enhancement, the proposed gripper considers the possibility offered by active surfaces, i.e. engineered contact surfaces able to simulate different levels of friction and to apply tangential thrust to the contacted object. The actual dexterity enhancement is evaluated by an analytical manipulability analysis and some examples of in hand manipulations and grasps are taken into account. A mechanical solution is presented, which implements the proposed idea through the adoption of one DoF active surfaces mounted on the fingers. The proposed solution presents a manipulability index one order of magnitude higher than common grippers.

Most of today's robots have rigid structures and actuators requiring complex software control algorithms and sophisticated sensor systems in order to behave in a compliant and safe way adapted to contact with unknown environments and humans. By studying and constructing variable impedance actuators and their control, we contribute to the development of actuation units which can match the intrinsic safety, motion performance and energy efficiency of biological systems and in particular the human. As such, this may lead to a new generation of robots that can co-exist and co-operate with people and get closer to the human manipulation and locomotion performance than is possible with current robots.

We propose a prototype of a Variable Stiffness Actuator (VSA) conceived with low cost as its first goal. This approach was scarcely covered in past literature. Many recent works introduced a large number of actuators with adjustable stiffness, optimized for a wide set of applications. They cover a broad range of design possibilities, but their availability is still limited to small quantities. This work presents the design and implementation of a modular servo-VSA multi-unit system, called VSA-CubeBot. It offers a customizable platform for the realization and test of variable stiffness robotic structures with many degrees of freedom. We present solutions relative to the variable stiffness mechanism, embedded electronics, mechanical and electrical interconnections. Characteristics, both theoretic and experimental, of the single actuator are reported and, finally, five units are interconnected to form a single arm, to give an example of the many possible applications of this modular VSA actuation unit.

Biomorphic structures for robotic manipulation based on tendon-driven mechanisms have been considered in robotic design for several decades, since they provide lightweight end-effectors with high dynamics. Following this trend, many new robot designs have being proposed based on tendon driven systems. Quite noticeably, the most advanced ones include also higher kinematic pairs and unilateral types of constraints. In this paper, we present a general framework for modeling the above class of mechanical systems for robotic manipulation. Such systems, including biomorphically designed devices, consist of articulated limbs with redundant tendinous actuation and unilateral rolling constraints. Methods based on convex analysis are applied to attack this broader class of mechanisms, and are shown to provide a basis for the dynamic control of co-contraction and internal forces that guarantee the correct operation of the system, despite limited friction between contacting surfaces or object fragility. An algorithm is described and tested that integrates a computed torque law, and allows to control tendon actuators to "optimally" comply with the prescribed constraints.

We consider the problem of estimating non-linear time-varying stiffness of a mechanical system based only on force and position measurements. A recent work presented a non-parametric stiffness observer, which converges to within an Uniformly Ultimately Bounded neighborhood of the real stiffness value. The method provides excellent results for applications where the system is persistently excited. In this paper, we provide a parametric identification method that complements the previous solution in that it can provide, after a sufficiently long learning period, a complete model of the nonlinear stiffness, which can be applied henceforth even in the absence of excitation. Convergence conditions for the proposed method are discussed. Simulation and experimental results are provided, illustrating the performance of the proposed algorithm.

This paper addresses the classification problem for a set of autonomous robots that interact with each other. The objective is to classify agents that "behave" in "different way", due to their own physical dynamics or to the interaction protocol they are obeying to, as belonging to different "species". This paper describes a technique that allows a decentralized classification system to be built in a systematic way, once the hybrid models describing the behavior of the different species are given. This technique is based on a decentralized identification mechanism, by which every agent classifies its neighbors using only local information. By endowing every agent with such a local classifier, the overall system is enhanced with the ability to run behaviors involving individuals of the same species as well as of different ones. The mechanism can also be used to measure the level of cooperativeness of neighbors and to discover possible intruders among them. General applicability of the proposed solution is shown through examples of multiagent systems from Biology and from Robotics.

We present a complete characterization of shortest paths to a goal position for a vehicle with unicycle kinematics and a limited range sensor, constantly keeping a given landmark in sight. Previous work on this subject studied the optimal paths in case of a frontal, symmetrically limited Field-Of-View (FOV). In this paper we provide a generalization to the case of arbitrary FOVs, including the case that the direction of motion is not an axis of symmetry for the FOV, and even that it is not contained in the FOV. The provided solution is of particular relevance to applications using side-scanning, such as e.g. in underwater sonar-based surveying and navigation.

This paper focuses on the estimation of the impedance for a Variable Impedance Actuator (VIA) through torque and position measurements. Despite the recent development of several VIA, impedance control is not yet implemented in closed loop because of the difficulty of obtaining in real-time measurements of time-varying impedance. The estimation algorithm is proposed as an alternative approach to the standard procedures of impedance identification, to robustly tolerate the variability of the mechanical stiffness due, for example, to model uncertainties. The impedance estimator is therefore implemented on the Actuator with Adjustable Stiffness (AwAS). The effectiveness of the proposed estimator is proved through simulation and experimental results.

The control of a robot's mechanical impedance is attracting increasing attention of the robotics community. Recent research in Robotics has recognized the importance of Variable Stiffness Actuators (VSA) in safety and performance of robots. An important step in using VSA for safety has been to understand the optimality principles that regulate the synchronized variation of stiffness and velocity when moving in the shortest time while limiting possible impact forces (the safe brachistochrone problem). In this paper, we follow a similar program of understanding the use of VSA in performance enhancement, looking at very dynamic tasks where impacts are maximized. To this purpose we address a new optimization problem that consists in choosing the inputs for maximizing the velocity of a link at a given final position, such as, e.g., for maximizing the effect of a hammer impact. We first study the problem with fixed stiffness, and show that, under realistic modeling assumptions, there does exist an optimal linear spring for the given inertia and motor. We then study optimal control of VSA and show that varying the spring stiffness during the execution of the hammering task improves the final performance substantially. The optimal control law is obtained analytically, thus providing insight in the optimality principles underpinning general VSA control. Finally, we show the practicality of our theoretical results with experimental tests.

This paper presents an optimal feedback control scheme to drive a vehicle equipped with a limited Field-Of-View (FOV) camera towards a desired position following the shortest path and keeping a given landmark in sight. Based on the shortest path synthesis available from previous works, feedback control laws are defined for any point on the motion plane exploiting geometric properties of the synthesis itself. Moreover, by using a slightly generalized stability analysis setting, which is that of stability on a manifold, a proof of stability is given. Reported simulations demonstrate the effectiveness of the proposed technique.

This paper presents a systematic enumeration and performance analysis of Variable Stiffness Actuators (VSAs). VSAs are becoming more and more popular in robotics, and many different prototypes have been recently proposed and built in the research community. In comparison with conventional geared motors, actuators with variable stiffness introduce the need for new specifications, requirements, and performance criteria, concerning e.g. the range of achievable stiffness, and the response time to stiffness reference changes. On the other hand, the mechanical construction of VSAs is also more complex. To address the problem of harnessing the increased complexity of VSA design, we consider in this article the enumeration of all possible arrangements of two prime movers (elementary motors), two harmonic-drive gears, the output shaft, and the interconnections (either rigid or elastic) between these elements. We propose an automated algorithm to search the large combinatorics of such enumeration, and present a reduced number of feasible basic designs which accomplish the objectives of VS actuation. Furthermore, we propose a quasi-static model of VS actuators which can be used for an analysis of their performance and we conclude by presenting some preliminary characteristics of one of the selected designs.

This paper focuses on casting manipulation and presents an innovative mechanism that allows objects placed in a three-dimensional space to be reached with a relatively small robot. Casting manipulation is a technique by which a robotic end-effector is thrown and its ballistic flight is controlled through a tether cable so as to reach a target object. Previous work presented a solution that is viable only when the position of the target object is perfectly known or aligned with the throwing plane. Our work extends the technique by use of a novel mechanical design of the arm, and a suitable control scheme for the flight of the end-effector, which makes it applicable for objects placed at generic positions in a three-dimensional environment. Effectiveness of the casting robot was shown during the first ESA Lunar Robotics Challenge, where our team came in the second place.

This paper studies the controllability of pairs of identical nonholonomic vehicles maintaining a constant distance. The study provides controllability results for the five most common types of robot vehicles: Dubins, Reeds-Shepp, differential drive, car-like and convexified Reeds-Shepp. The challenge of achieving controllability of such systems is that their admissible control domains depend on configuration variables. A theorem of controllability specifical for such systems has been obtained based on known controllability theorems. As a result, we show that pairs of the latter three types are completely controllable, i.e. can be steered between any two arbitrary configurations. The same does not hold for pairs of Dubins or Reeds-Shepp vehicles, and a description of the reachable sets in these cases is provided. Finally, as direct extension of controllability results of pairs of identical vehicles, the controllability results for two kinds of formation of n identical vehicles are presented.

In this paper we show that for an observer moving in the plane with no other information than the measurement of relative bearing to three known landmarks, it is possible to completely reconstruct its position and velocity. In particular this applies to the case where no model of the vehicle, nor odometry or acceleration measurements are available. Furthermore, in the same hypotheses, the position of any further landmark can be reconstructed from its bearing only. These results are more general than what is currently known on nonlinear observability of the SLAM problem, which relies on known observer velocities. Our results are also more general than the 2D version of known structure-from-motion observability results, which assume unknown but constant velocities. The proposed method is used to build a nonlinear observer directly applicable to a range of problems from computer vision to autonomous visual navigation.

This paper presents design, implementation and performance of a new Variable Stiffness Actuator (VSA) based on Harmonic Drives (VSA-HD), which is an improvement over past work reported in. While previous prototypes have been developed to demonstrate the effectiveness of the variable stiffness actuation principle and the possibility to develop a compact and reliable actuator, the VSA-HD has been obtained by exploring the performance of the enumeration of all VSA made out a basic components set (i.e. two prime movers, two harmonic-drive gears, and the output shaft) and all the feasible interconnections between them as presented in. Along this enumeration the VSA-HD conceptual layout has been selected as being good trade-off between mechanical complexity and overall performance. This paper discusses in depth the actuator mechanical layout, highlighting the main characteristics of the new design. A model for the actuator is introduced and validated by experimental results.

In this paper, we address decision making problems, depending on a set of input events, with networks of dynamic agents that have partial visibility of such events. Previous work by the authors proposed so-called logical consensus approach, by which a network of agents, that can exchange binary values representing their local estimates of the events, is able to reach a unique and consistent decision. The approach therein proposed is based on the construction of an iterative map, whose computation is centralized and guaranteed under suitable conditions on the input visibility and graph connectivity. Under the same conditions, we extend the approach in this work by allowing the construction of a logical linear consensus system that is globally stable in a fully distributed way. The effectiveness of the proposed method is showed through the real implementation of a wireless sensor network as a framework for the surveillance of an urban area.

In this paper, we present a methodology for designing embedded controllers based on the so-called anytime control paradigm. A control law is split into a sequence of subroutine calls, each one fulfilling a control goal and refining the result produced by the previous one. We propose a design methodology to define a feedback controller structured in accordance with this paradigm and show how a switching policy of selecting the controller subroutines can be designed that provides stability guarantees for the closed-loop system. The cornerstone of this construction is a stochastic model describing the probability of executing, in each activation of the controller, the different subroutines. We show how this model can be constructed for realistic real-time task sets and provide an experimental validation of the approach.

This paper presents a complete characterization of shortest paths to a goal position for a robot with unicycle kinematics and an on-board camera with limited field-of-view (FOV), which must keep a given feature in sight. Previous work on this subject has shown that the search for a shortest path can be limited to simple families of trajectories. In this paper, we provide a complete optimal synthesis for the problem, i.e., a language of optimal control words, and a global partition of the motion plane induced by shortest paths, such that a word in the optimal language is univocally associated with a region and completely describes the shortest path from any starting point in that region to the goal point. An efficient algorithm to determine the region in which the robot is at any time is also provided.

In this paper we present a methodology for designing embedded controllers with a variable accuracy. The adopted paradigm is the so called any-time control, which derives from the computing paradigm known as "imprecise computation". The most relevant contributions of the paper are a procedure for designing an incremental control law, whose different pieces cater for increasingly aggressive control requirements, and a modelling technique for the execution platform that allows us to design provably correct switching policies for the controllers. The methodology is validated by both simulations and experimental results.

A general technique to build a dynamic and distributed intrusion detector for a class of multi-agent systems is proposed in this paper, by which misbehavior in the motion of one or more agents can be discovered. Previous work from the authors has focused on how to distinguish the behavior of a misbehaving agent in a completely distributed way, by developing a solution where agents act as local monitors of their neighbors and use locally sensed information as well as data received from other monitors at a particular time. In this work, we improve the system detection capability by allowing monitors to use information collected at different instants and thus realizing a dynamic state observer that is valid for any system in the considered class. Finally, we show through simulations the effectiveness of the proposed solution for a case study.

In this paper we discuss the integration of active and passive approaches to robotic safety in an overall scheme for real-time manipulator control. The active control approach is based on the use of a supervisory visual system, which detects the presence and position of humans in the vicinity of the robot arm, and generates motion references. The passive control approach uses variable joint impedance which combines with velocity control to guarantee safety in worst-case conditions, i.e. unforeseen impacts. The implementation of these techniques in a 3-dof, variable impedance arm is described, and the effectiveness of their functional integration is demonstrated through experiments.

This paper describes the design and implementation of DAVID, a lunar vehicle developed for the European Space Agency (ESA) Lunar Robotics Challenge, presenting severe terrain negotiation and sample acquisition challenges. We discuss in some detail two of the main innovative aspects of our entry to the challenge, i.e. the locomotion system and the sample acquisition system. Motivated by the challenge specifications, a range of different locomotion systems were considered, among which we chose a simple, rugged and effective wheeled system. We provide an account of the choice of five different types of wheels, which were designed, analyzed and experimentally tested in conditions similar to the challenge. The system eventually turned out to be very effective in negotiating 89% slopes of volcanic terrain on the challenge site, Mount Teide in Tenerife. To reduce the distance to be traveled on the difficult terrain and avoid risks in reaching the lowest parts of a crater, the vehicle was endowed with an innovative sample acquisition system, i.e. a casting manipulator. Casting manipulation is a technique in which the end-effector is thrown, the sample material is acquired, and the end-effector is retrieved using a light tether that acts as a "fishing line". The casting manipulator developed for DAVID uses an innovative sling-like technique, capable to obtain longer and more precise casts than previous oscillating versions. The analysis and experimental verification of DAVID's robot sling are reported, demonstrating its effectiveness. Finally, we give a brief account of the outcomes of the ESA Lunar Robotics Challenge, where our team came in second over other 8 teams that passed the final qualification phase.

Variable stiffness actuation (VSA) devices are being used to jointly address the issues of safety and performance in physical human-robot interaction. With reference to the VSA-II prototype, we present a feedback linearization approach that allows the simultaneous decoupling and accurate tracking of motion and stiffness reference profiles. The operative condition that avoids control singularities is characterized. Moreover, a momentum-based collision detection scheme is introduced, which does not require joint torque sensing nor information on the time-varying stiffness of the device. Based on the residual signal, a collision reaction strategy is presented that takes advantage of the proposed nonlinear control to rapidly let the arm bounce away after detecting the impact, while limiting contact forces through a sudden reduction of the stiffness. Simulations results are reported to illustrate the performance and robustness of the overall approach. Extensions to the multidof case of robot manipulators equipped with VSA-II devices are also considered.

This paper addresses a security problem in robotic multi-agent systems, where agents are supposed to cooperate according to a shared protocol. A distributed Intrusion Detection System (IDS) is proposed here, that detects possible non-cooperative agents. Previous work by the authors showed how single monitors embedded on-board the agents can detect non- cooperative behavior, using only locally available information. In this paper, we allow such monitors to share the collected information in order to overcome their sensing limitation. In this perspective, we show how an agreement on the type of behavior of a target-robot may be reached by the monitors, through execution of a suitable consensus algorithm. After formulating a consensus problem over non-scalar quantities, and with a generic update function, we provide conditions for the consensus convergence and an upper bound to its transient duration. Effectiveness of the proposed solution is finally shown through simulation of a case study.

This paper presents design and performance of a novel joint based actuator for a robot run by variable stiffness actuation, meant for systems physically interacting with humans. This new actuator prototype (VSA-II) is developed as an improvement over our previously developed one reported in [9], where an optimal mechanical-control co-design principle established in [7] is followed as well. While the first version was built in a way to demonstrate effectiveness of variable impedance actuation (VIA), it had limitations in torque capacities, life cycle and implementability in a real robot. VSA-II overcomes the problem of implementability with higher capacities and robustness in design for longer life. The paper discusses design and stiffness behaviour of VSA-II in theory and experiments. A comparison of stiffness characteristics between the two actuator is discussed, highlighting the advantages of the new design. A simple, but effective PD scheme is employed to independently control joint-stiffness and joint-position of a 1-link arm. Finally, results from performed impact tests of 1- link arm are reported, showing the effectiveness of stiffness variation in controlling value of a safety metric.

This paper presents a correct solution to the optimal visual feedback control for a nonholonomic vehicle with limited field-of-view. Previous work on this subject has shown that the search for a shortest path can be limited to simple families of trajectories. We preliminarily provide an extension of the alphabet of optimal control words, to cover some regions of the vehicle plane where the synthesis of turns out to be suboptimal. The main contribution of this paper is an algorithm to translate the optimal synthesis to the image plane, thus enabling a purely image-based optimal control scheme. This allows better performance and increases the robustness of the overall process, avoiding the need of slowly-converging and error-prone parameter estimation algorithms. Simulations and experiments are reported which demonstrate the effectiveness of the proposed technique.

In this paper we introduce an analysis of dependability of an elementary yet critical component of robotic systems designed to operate in environments shared with humans, i.e., the joint-level actuation system. We consider robot joints that implement the variable impedance actuation (VIA) paradigm. The VIA has been demonstrated to be an effective mean to achieve high performance while constantly keeping injury risks to humans by accidental impacts below a given threshold. The paper describe possible implementations of the VIA concept which use the Antagonistic Actuation (AA) in three different arrangements. This study follows a previously reported paper dealing with safety. Here a detailed comparative dependability and performability analysis in front of possible specific failure modes is conducted, whose results provide additional and useful guidelines for design of safe and dependable actuation systems for physical human-robot interaction.

In this paper we deal with the optimal feedback synthesis problem for robotic vehicles with trailers which can be modeled by differential equations in chained-form. With respect to classical methods for numerical evolution of optimal feedback synthesis via dynamic programming which are based on both input and state discretization, our method exploits the lattice structure naturally imposed on the reachable set by input quantization. A generalized Dijkstra algorithm can be used to obtain sub-optimal (optimal up to the lattice resolution) feedback laws, for chained-form vehicles with n-trailers, in an effective way.

In this paper we investigate the possibility of processing the tactile perception by using a novel biomimetic approach for the pattern recognition module. The goal is to enhance the perception in complex virtual environments deriving from haptic displays mimicking human tactile discrimination. To do this we explored a Minimally Invasive Surgery application where the tactile information are strictly limited. In fact, this promising technique suffers from some evident limitations due to the surgeon loss of tactile perception during palpation of internal organs. This is basically due to the mechanical transmission of the elongated tools used during operation. We propose to integrate an Artificial Neural Network in an electronic board capable of processing data provided by a sensorized laparoscopic tool. The capabilities of several pattern recognition techniques present in literature, the Principal Component Analysis (PCA), a Multilayer Perceptron (MLP) and a Kohonen Self-Organising Map (KSOM) are investigated. The results are compared with that obtained psychophysically on five viscoelastic materials.

In this paper, we propose a novel policy for steering multiple vehicles between assigned start and goal configurations, ensuring collision avoidance. The policy rests on the assumption that all agents are cooperating by implementing the same traffic rules. However, the policy is completely decentralized, as each agent decides its own motion by applying those rules only on the locally available information, and scalable, in the sense that the amount of information processed by each agent and the computational complexity of the algorithms do not increase with the number of agents in the scenario. The proposed policy applies to systems in which new vehicles may enter the scene and start interacting with existing ones at any time, while others may leave. Under mild conditions on the initial configurations, the policy is shown to be safe, i.e., it guarantees collision avoidance throughout the system evolution. In the paper, conditions are discussed on the desired configurations of agents, under which the ultimate convergence of all vehicles to their goals can also be guaranteed. To show that such conditions are actually necessary and sufficient, which turns out to be a challenging liveness-verification problem for a complex hybrid automaton, we employ a probabilistic verification method. The paper finally presents and discusses simulations for systems of several tens of vehicles, and reports on some experimental implementation showing the practicality of the approach.

In this paper we address the problem of generating input plans to steer complex dynamical systems in an obstacle-free environment. Plans considered admit a finite description length and are constructed by words on an alphabet of input symbols, which could be e.g. transmitted through a limited capacity channel to a remote system, where they can be decoded in suitable control actions. We show that, by suitable choice of the control encoding, finite plans can be efficiently built for a wide class of dynamical systems, computing arbitrarily close approximations of a desired equilibrium in polynomial time. Moreover, we illustrate by simulations the power of the proposed method, solving the steering problem for an example in the class of underactuated systems, which have attracted wide attention in the recent literature

Functional brain exploration methodologies such as functional magnetic resonance imaging (fMRI) are critical tools to study perceptual and cognitive processes. In order to develop complex and well-controlled fMRI paradigms, researchers are interested in using active interfaces with electrically powered actuators and sensors. Due to the particularity of the MR environment, safety and compatibility criteria have to be strictly followed to avoid risks to the subject under test, the operators or the environment, as well as to prevent artifacts in the images. This paper describes the design of an fMRI compatible mechatronic interface based on MR compatibility tests of materials and actuators. In particular, a statistical test is introduced to evaluate the presence of artifacts in the image sequences that could negatively affect the fMRI studies. The device with two degrees of freedom, allowing one translation with position-feedback along a horizontal axis and one rotation about a vertical axis linked to the translation, was realized to investigate the brain mechanisms of dynamic tactile perception tasks. It can be used to move and orient various objects below the finger for controlled tactile stimulation. The MR compatibility of the complete interface is shown using the statistical test as well as a functional study with a human subject

In this paper, we consider a decentralized cooperative control policy proposed recently for steering multiple nonholonomic vehicles between assigned start and goal configurations while avoiding collisions. The policy is known to ensure safety (i.e., collision avoidance) for an arbitrarily large number of vehicles, if initial configurations satisfy certain conditions. The method is highly scalable, and effective solutions can be obtained for several tens of autonomous agents. On the other hand, the liveness properties of the policy, i.e. the capability of negotiating a solution in finite time, are not completely understood yet. In this paper, we introduce a condition on the final vehicle configurations, which we conjecture to be necessary and sufficient for guaranteeing liveness. We prove the necessity by a constructive method. Because of the overwhelming complexity of proving the sufficiency of such condition, we assess the correctness of the conjecture in probability through the analysis of the results of a large number of randomized experiments

In this paper we report on a new improved free-hand haptic interface based on magnetorheological fluids (MRFs). MRFs are smart materials which change their rheology according to an external magnetic field. The new architecture here proposed results from the development and improvement of earlier prototypes. The innovative idea behind this device is to allow subjects interacting directly with an object, whose rheology is rapidly and easily changeable, freely moving their hands without rigid mechanical linkages. Numerical advanced simulation tests using algorithms based on finite element methods have been implemented, in order to analyze and predict the spatial distribution of the magnetic field. A special focus was laid on investigating on how the magnetic filed profile is altered by the introduction of the hand. Possible solutions were proposed to overcome this perturbation. Finally some preliminary psychophysical tests in order to assess the performance of the device are reported and discussed

In this paper, we present a method to control casting manipulation by means of real-time visual feedback. Casting manipulation is a technique to deploy a robotic end-effector at large distances from the robot’s base, by throwing the end-effector and controlling its ballistic flight using forces transmitted through a light tether connected to the end-effector itself. The tether cable can also be used to retrieve the end-effector and exert forces on the robot’s environment. Previous work has shown that casting manipulation is able to catch objects at a large distance, proving it viable for applications such as sample acquisition and return, rescue, etc. In previous experiments, the position of the target object was known exactly. In this paper, we present a first attempt at closing a real-time control loop on casting manipulation using visual feedback of moving targets. As accurate planning methods developed for off-line open-loop planning cannot be used in real-time, we develop a simplified model and control algorithm, whose effectiveness is demonstrated through experiments.

This paper is concerned with the design and control of actuators for machines and robots physically interacting with humans, implementing criteria established in our previous work [1] on optimal mechanical-control co-design for intrinsically safe, yet performant machines. In our Variable Impedance Actuation (VIA) approach, actuators control in real-time both the reference position and the mechanical impedance of the moving parts in the machine in such a way to optimize performance while intrinsically guaranteeing safety. In this paper we describe an implementation of such concepts, consisting of a novel electromechanical Variable Stiffness Actuation (VSA) motor. The design and the functioning principle of the VSA are reported, along with the analysis of its dynamic behavior. A novel scheme for feedback control of this device is presented, along with experimental results showing performance and safety of a one-link arm actuated by the VSA motor.

In this paper we propose a new approach to motion planning, based on the introduction of a lattice structure in the workspace of the robot, leading to efficient computations of plans for rather complex vehicles, and allowing for the implementation of optimization procedures in a rather straightforward way. The basic idea is the purposeful restriction of the set of possible input functions to the vehicle to a finite set of symbols, or control quanta, which, under suitable conditions, generate a regular lattice of reachable points. Once the lattice is generated and a convenient description computed, standard techniques in integer linear programming can be used to find a plan very efficiently. We also provide a correct and complete algorithm to the problem of finding an optimized plan (with respect e.g. to length minimization) consisting in a sequence of graph searches.

Robotic manipulation by rolling contacts is an appealing method for achieving dexterity with relatively simple hardware. While there exist techniques for planning motions of rigid bodies in rolling contact under nominal conditions, an inescapable challenge is the design of robust controllers of provable performance in the presence of model perturbations. As a preliminary step in this direction, we present in this paper an iterative robust planner of arbitrary accuracy for the plate-ball manipulation system subject to perturbations on the sphere radius. The basic tool is an exact geometric planner for the nominal system, whose repeated application guarantees the desired robustness property on the basis of the iterative steering paradigm. Simulation results under perturbed conditions show the effectiveness of the method.

In this paper we propose an innovative prototype of a haptic display for whole-hand immersive exploration. We envision a new concept of haptic display, the Haptic Black Box, which can be imagined as a box where the operator can poke his/her bare hand, and interact with the virtual object by freely moving the hand without mechanical constraints. In this way sensory receptors on the whole operator's hand would be excited, rather than restricting to just one or few fingertips or phalanges. To progress towards such a challenging goal, magnetorheological (MR) fluids represent a very interesting and completely innovative technology. These fluids are composed of micronsized, magnetizable particles immersed in a synthetic oil. Exposure to an external magnetic field induces in the fluid a change in rheological behaviour turning it into a near-solid in few milliseconds. By removing the magnetic field, the fluid quickly returns to its liquid state. We briefly report on the design of this device, describe psychophysical experiments to assess performance for softness and shape exploration, and report on the experimental results.

In this paper, we consider planning motions of objects of regular shape rolling on a plane among obstacles. Theoretical foundations and applications of this type of operations in robotic manipulation and locomotion have been discussed elsewhere. In this paper, we propose a novel algorithm that improves upon existing techniques in that: i) it is finitely computable and predictable (an upper bound on the computations necessary to reach a given goal within a tolerance can be given), and ii) it possesses a topological (local-local) property which enables obstacles and workspace limitations to be dealt with in an effective way.

In this paper, an independent joint position and stiffness adaptive control for a robot arm actuated by McKibben artificial muscles is reported. In particular, muscular and dynamic parameters of the system are supposed unknown. Adaptive control performance is tested in a one degree of freedom experimental setup and compared with PID control performance. The adaptive control scheme is then applied to a robot arm that is conceived to perform tasks in anthropic environments. The adaptive control developed is such that performance of the robot arm is very similar to human arm performance. Experimental results are reported.

This paper considers the problem of solving conflicts arising among several aircraft that are assumed to move in a shared airspace. Aircraft can not get closer to each other than a given safety distance in order to avoid possible conflicts between different airplanes. For such system of multiple aircraft, we consider the path planning problem among given waypoints avoiding all possible conflicts. In particular we are interested in optimal paths, i.e., we want to minimize the total flight time. We propose two different formulations of the multiaircraft conflict avoidance problem as a mixed-integer linear program: in the first case only velocity changes are admissible maneuvers, in the second one only heading angle changes are allowed. Due to the linear formulation of the two problems, solutions may be obtained quickly with standard optimization software, allowing our approach to be implemented in real time.

In this paper we describe work being done at our Department to make the Robotics laboratory accessible to students and colleagues, to execute and watch real-time experiments at any time and from anywhere. We describe few different installations, and highlight the underlying philosophy, which is aimed at enlarging the lab in all the dimensions of space, time, and available resources, through the use of Internet technologies.

We consider the problem of efficiently simulating large interconnected mechanical systems. For applications such as haptic rendering of large, complex virtual environments, dynamic simulation software and hardware is still too slow to afford accurate performance in real-time. In particular, mechanisms with closed kinematic chains require solutions for a set of differential equations with algebraic constraints (DAEs) that are often too complex to be computed in real-time by present-day single-processor machines. On the other hand, the structure of most state-of-the-art algorithms does not easily lend itself to parallelization. In this paper, we propose and experimentally verify a technique for DAE simulation that profitably uses a degree of randomization to achieve efficient parallelization.

We consider the problem of maximizing the localization accuracy of a mobile vehicle, based on triangulation measurements derived from optical data. The problem is intrinsically nonlinear, as the linear approximation of the system is not observable. This implies that the choice of inputs (i.e., the path followed) may affect the quality of observations made, and ultimately the localization accuracy. We consider the problem of finding the most informative exploratory path of the given length for a rover (modeled as a point in the plane) with optical triangulation information.

We describe some initial results of a project aiming at the development of a programmable compliance, inherently safe robot arm for applications in anthropic environments. In order to obtain safety in spite of worst-case situations (such as unexpected delays in teleoperation, or even controller failure), we propose an approach to achieving the compliance by mechanical rather than by control design. We first describe some of the control problems encountered in a typical, large, possibly unknown mechanical compliance, and present the result that shows the possibility to cope with these uncertainties in an adaptive way. Next, we describe the initial development of a new prototype arm under construction in our laboratory. The arm is designed to achieve arbitrary position tracking in 3D with controlled effective compliance at the joints.

In this paper, we survey the field of robotic grasping and the work that has been done in this area over the last two decades, with a slight bias toward the development of the theoretical framework and analytical results in this area.

In this paper we consider a particular technique for achieving dexterity in manipulation with robot hands, which intentionally exploits rolling contacts. We report in some detail on modelling rolling contacts, and provide a result on the analysis of controllability of rolling pairs of bodies, which serves as a theoretical basis for the exploitation of rolling for dexterity enhancement. We conclude by illustrating some aspects of the problem that could not be touched upon in the paper, and open problems that still remain to be solved.

A nonhonolomic vehicle is stabilized to a desired pose through a visual servoing technique. The vision-based regulation of the nonholonomic vehicle proposed is built through a discontinuous change of coordinates and Lyapunov-based design, which ensure asymptotic stability of the closed-loop visual system. A dynamic estimation procedure, based on the optical flow equations, is also presented to deal with uncertainties in the obsereved environment. Simulations results on an autonomous mobile robot are reported, that show the practicality of the proposed approach.

We consider the kinematic model of a vehicle moving forward with a lower bounded turning radius. This model can be used to describe the kinematics of road vehicles as well as aircraft cruising at constant altitude, or sea vessels. We consider the problem of minimizing the length travelled by the vehicle starting from a generic configuration to connect to a specified route. A feedback law is proposed, such that straight routes can be approached optimally, while system is asymptotically stabilized. Experimental results are reported showing real-time feasibility of the approach.

We consider a complete dynamic model for the "Sphericle", a spherical vehicle that has been designed and realized in our laboratory. The Sphericle is able to roll on the floor of the laboratory and reach arbitrary positions and orientations, through the use of only two motors placed within the rolling sphere. In this paper, we report on the derivation of the kinematic model of the Sphericle, which incorporates two types of nonholonomic constraints and its dynamic model.

We consider optimal resolution of air traffic (AT) conflicts. Aircraft are assumed to cruise within a given altitude layer and are modeled as a kinematic system with constant velocity and curvature bounds. Aircraft cannot get closer to each other than a predefined safety distance. For such a system of multiple aircraft, we consider the problem of planning optimal paths among given waypoints. Necessary conditions for optimality of solutions are derived and used to devise a parametrization of possible trajectories that turns into efficient numerical solutions to the problem. Simulation results for a realistic aircraft conflict scenario are provided. A decentralized implementation of the optimal conflict resolution scheme is introduced that may allow free-flight coordination in a cooperative airspace management scheme. Impact of decentralization on performance and safety is finally discussed with the help of extensive simulations.

In this paper, an attempt at summarizing the evolution and the state of the art in the field of robot hands is made. In such exposition, a critical evaluation of what in the author's view are the leading ideas and emerging trends is privileged with respect to exhaustiveness of citations. The survey is focused mainly on three types of functional requirements a machine hand can be assigned in an artificial system, namely, manipulative dexterity, grasp robustness, and human operability. A basic distinction is made between hands designed for mimicking the human anatomy and physiology,and hands designed to meet restricted, practical requirements. In the latter domain, arguments are presented in favor of a -minimalistic" attitude in the design of hands for practical applications, i.e., use the least number of actuators, the simplest set of sensors, etc., for a given task. To achieve this rather obvious engineering goal is a challenge to our community. The paper illustrates some of the new sometimes difficult, problems that are brought about by building and controlling simpler, more practical devices.

We study the differential kinematics and the kineto-static manipulability indices of multiple cooperating robot arms, including active and passive joints. The kinematic manipulability indices are derived extending previous results on cooperating robots without passive joints. The force manipulability analysis for cooperative robot systems cannot be straightforwardly derived by "duality" arguments as it can with conventional arms, rather a distinction between active and passive force manipulability must be introduced. Results in the paper apply directly to the analysis of cooperating robots, parallel robots, dextrous robotic hands and legged vehicles, and, in general, to closed kinematic chains.

Many applications in teleoperation and virtual reality call for the implementation of effective means of displaying to the human operator information on the softness and other mechanical properties of objects being touched. The ability of humans to detect softness of different objects by tactual exploration is intimately related to both kinesthetic and cutaneous perception, and haptic displays should be designed so as to address such multimodal perceptual channel. In this paper, we investigate the possibility of surrogating detailed tactile information for softness discrimination, with information on the rate of spread of the contact area between the finger and the specimen as the contact force increases. Devices for implementing such a perceptual channel are described, and a practical application to a mini-invasive surgery tool is presented. Psychophysical test results are reported, validating the effectiveness and practicality of the proposed approach.

Many applications in teleoperation and virtual reality call for the implementation of effective means of displaying to the human operator information on the softness and other mechanical properties of objects being touched. The ability of humans to detect softness if different objects by tactual exploration is intimately related to both kinesthetic and cutaneous perception, and haptic displays should be designed so as to address such multimodal perceptual channel. In this paper we investigate the possibility of surrogating detailed tactile information for softness discrimination, with information on the rate of spread of the contact area between the finger and the specimen. Devices for implementing this new perceptual channel are described, and some preliminary psychophysical test results reported, validating the effectiveness and practicality of the proposed approach.

The nonholonomy exhibited by kinematic systems consisting of bodies rolling on top of each other can be used for the purpose of building dexterous mechanisms with a minimum hardware complication. Such a desirable engineering feature can be fully exploited, however, only if the capability of planning and controlling the rolling motions of arbitrary objects is achieved. In this paper we present recent advances of both theoretical and experimental natures towards realizing a robot gripper for manipulation of objects whose shape is not known a priori, but is reconstructed as manipulation proceeds.

We study the differential kinematics and the kineto-static manipulability indices of multiple cooperating robot arms, including active and passive joints. The kinetic manipulability indices are derived as a simple extension of previous results on cooperating robots without passive joints. The force manipulability analysis for cooperative robot systems can not be derived by "duality" arguments as it can with conventional arms, rather a distinction between active and passive force manipulability is necessary. Results in the paper apply directly to the analysis of simply closed kinematic chains, and can be extended to multiply closed kinematic chains.

We present a geometric approach to the dynamic analysis of manipulation systems of a rather general class, including some important types of manipulators as, e.g., cooperating, super-articulated, and whole-arm manipulators. The focus is in particular on simple industry-oriented devices, for which a minimalistic design approach requires a clear understanding of mobility and graspability properties in the presence of kinematic defectivity. The paper discusses the dynamics of these systems, and considers how their structural properties (in the classical system-theoretic sense, i.e., stability, controllability, observability, etc.) are related to frequently used concepts in robotics such as "redundancy", "graspability", "mobility", and "indeterminacy". Less common or novel concepts, such as those of "defectivity," "hyperstaticity," and "dynamic graspability", are elicited and/or enlightened by this study. Some important practical consequences of the limited control possibilities of defective systems are thus put into evidence. Finally, a standard form of the dynamics of general manipulation systems is provided as a compact and readable synopsis of the dynamic structure. The form is a valuable tool for synthesizing dynamic controllers for such systems, especially suited to geometric control design methods.

In this paper we describe an experimental apparatus developed in our laboratory for research and advanced teaching purposes. The device consists of an untethered spherical vehicle that autonomously rolls on the laboratory floor, and can reach arbitrary positions and orientations in the environment. The kinematics of the vehicle are nonholonomic and result from the combination of the kinematics of two classical nonholonomic systems, namely, a unicycle and a plate-ball system. The "SPHERICLE" introduces features that are new with respect to the two systems.

The nonholonomy exhibited by kinematic systems consisting of bodies rolling on top of each other can be used to the purpose of building dexterous mechanism with a minimum hardware complication. Previous work concentrated on manipulation of objects possessing a regular surface. On the other hand, industrial parts are most often irregular, possessing vertices and edges. In this paper we present some results on the description of the set of positions and orientations that polyhedral objects can reach when manipulated by rolling without slipping. An algorithm for planning the manipulation of a polyhedral part from a given configuration to another reachable one, is also presented.

This paper presents an interactive environment available on the WorldWide Web intended to allow fair and thorough comparison of different techniques to solve a basic problem in nonholonomic motion planning. By connecting to the server, the user, potentially unaware of the technical subtleties of the planning problem, but well conscious of his application needs, can design the benchmark problem that is most significant to his purposes. The user can then obtain different solutions from several algorithm providers, and compare them both qualitatively (by graphic display), and quantitatively. Providers implement their own algorithms at their sites, with wide freedom of choice in programming language, computational architecture, etc., while complying with few simple protocol conventions. It is believed that similar usage of the Web, easily extendable to other domains, can usefully contribute to the fair comparison of results among researchers, as well as to the diffusion of advanced research results towards application oriented users.

The theory of force and dynamic manipulability for general systems of multiple co-operating robot manipulators is developed. Manipulability analysis refers to the study of the performance of the system regarding to the mechanical transformation of inputs (forces and torques at actuated joints) into outputs (forces and torques exchanged with the environment or accelerations of a reference member), in relation to different configurations of the system and different directions in the input and output spaces. For this purpose, the concept of manipulability ellipsoids for single robot arms is generalized so as to encompass multi-limb co-operating systems with general kinematic structure.

The problem of driving a Dubin's car (1957) along a given path is considered. In order to model a realistic road-following problem, the car is supposed to move forward only and to have bounds on the turning radius (Dubin's car). We propose a discontinuous control scheme on the angular velocity of the vehicle, based on the theory of sliding modes, that achieves the goal of tracking an unknown path relying on measurements of the current distance from the path and of the heading angle error.

Nowadays, the surgeon who is using minimally invasive tools loses almost completely the haptic perception of the manipulated tissue. In particular, he or she loses the perception of the tissue elastic properties. It is possible to modify the actual mini-invasive surgical tools in such a way that they may give a reliable estimation of the manipulated tissue properties for recognition and characterization purpose. In this paper we present a first attempt to realize a prototype of sensor-based surgical tool using a modified commercial tool. Experimental tests have shown that using such a tool could enhance surgeon's haptic perception of the manipulated tissue.

We focus on the problem of controlling a manipulator so as to trade a desired object trajectory, while guaranteeing that contact forces comply with contact constraints (friction bounds, etc.). When dealing with kinematically defective systems, it is not possible in general to assign arbitrary trajectories of object motions and contact forces. To understand what restrictions position and force reference trajectories should exhibit in order to be feasible by a given system, is the central issue of this work.

This paper describes a technique for path planning in environments cluttered with obstacles for mobile robots with nonholonomic kinematics and bounded trajectory curvature (i.e., limited turning radius). The method is inspired by the results of Reeds and Shepp (1990) regarding shortest paths of bounded curvature in absence of obstacles. It is proved that, under suitable assumptions, the proposed technique provides the shortest path of bounded curvature among polygonal objects for a particular class of vehicles (circular unicycles of radius h and minimum turning radius /spl rho//sub min//spl les/h). Although the class of vehicles this theoretical result is restricted to is rather narrow, the proposed planner can be satisfactorily applied to other nonholonomic vehicles yielding good practical results.

Considers the structural properties of the dynamics of robotic manipulation systems of a rather general class, including multiple cooperating, possibly whole-arm limbs, interacting with a manipulated object by means of contacts. A geometric approach to the analysis of the linearized dynamics of such systems is presented, which provides much insight in some of their intrinsic characteristics in the light of classical system-theoretic concepts such as controllability, observability, and canonical forms.

Nonholonomic constraints in robotic systems are the source of some difficulties in planning and control; however, they also introduce interesting properties that can be practically exploited. In this paper we consider the design of a robot hand that achieves dexterity (i.e. the ability to arbitrarily locate and reorient manipulated objects) through rolling. Some interesting issues arising in planning and controlling motions of such device are considered, including exact planning for a spherical object and approximate planning for general objects. An experimental prototype of a three-plus-one degree of freedom hand achieving dexterous manipulation capabilities is described along with experimental results from manipulation.

In this paper, the analysis of the differential kinematics and manipulability measures of robotic systems comprised of multiple cooperating limbs is considered. The goals of this study can be articulated in four points: 1) to enumerate the degrees of freedom of the manipulation system; 2) to describe analytically all possible first-order differential motions of the system at a given configuration; 3) to evaluate in the velocity domain the functionality of a manipulation system, with respect to the task it is required to perform; and 4) to calculate the bounds for the velocities achievable by the system, given bounds on the capabilities of joint actuators. The assumptions made on the robotic system are quite general, so that many complex devices (e.g., dextrous hands, legged vehicles, whole-arm manipulators, etc.) can be dealt with in a unified and convenient framework.>

We present a general framework for the modelling of a class of mechanical systems for robotic manipulation, consisting of articulated limbs with redundant tendinous actuation and unilateral constraints. Such systems, that include biomorphically designed devices, are regarded as a collection of rigid bodies, interacting through connections that model both joints and contacts with virtual springs. Methods previously developed for the analysis of force distribution in multiple whole-limb manipulation are generalised to this broader class of mechanisms, and are shown to provide a basis for the control of co-contraction and internal forces that guarantee proper operation of the system.>

The coordinator of the movements of multiple robot arms manipulating a common object is considered. In order to provide a general framework for the study of such cooperating systems as common industrial arms, multifingered hands, and legged vehicles, the analysis does not rely on the assumption of full mobility for each cooperating arm, which is otherwise common in related literature. The aim of the present work is to provide a systematic method to characterize the mobility and differential kinematics of general cooperating systems. The proposed analysis and algorithms provide an insight into the structure of the input (joint)-output (task) relationship of such systems.>

The problem of grasping objects with a robotic hand is considered. The possibility that some or all of the fingers are not able to arbitrarily control interactions with the grasped objects is taken into account. Such defective manipulation systems can still be cooperatively coordinated so as to perform usefully. In fact, having defective arms is the norm rather than an exception in many manipulation operations, such as power grasps with a hand, or whole arm manipulation of large objects. The paper attempts to solve the problem of optimizing contact forces in the power grasp of an object. To do so, a basis of the subspace of grasp forces that are available for grasp optimization is firstly established. This analysis is instrumental for subsequent optimization strategies, incorporating the quest for an extremum of some quality criterion. A control algorithm is presented that guarantees the asymptotic convergence to the grasp force configuration that minimizes the risk of slippage while maintaining bounded contact forces between the fingers and the object.>

The basic mathematic relationships of intrinsic (or force based) contact sensing are discussed. While conventional tactile sensing devices are designed to provide information about local phenomena caused by contact, intrinsic contact sensing detects a few global quantities relating to the interactions of two bodies in contact. The author addresses the geometric-mathematical problem of detecting these quantities starting from force/torque measurements and from the geometric description of one of the contacting surfaces. Two methods for solving the intrinsic contact sensing problem are discussed. The first method is able to give exact results for contacts of the hard-finger type, while it is shown to be only approximate for soft-finger contacts. A formula for estimating the extent of such approximation error is provided. A second, novel solution method is presented, which applies to soft fingers with ellipsoidal surface and is capable of yielding exact solutions to the problem. Some implementation issues and applications of intrinsic tactile sensing to fine manipulation operations are reviewed.>

The authors discuss the application of intrinsic tactile sensing (ITS) to grasp and manipulation control. A brief description of ITS, i.e., contact sensing based on force/torque measurements at fingertips, is provided. A method for using sensory feedback in the control of grasp forces to augment grasp robustness against slippage is discussed with respect to a simple grasp type; simulation and experimental data are provided. The possible generalization of this sensor-driven approach to the control of optimal grasp force in complex grasp configurations is addressed.>

To investigate basic issues related to tactile sensing for robots, a sensorized scenario has been devised which comprises a multisensor static platform and a tendon actuated, 4 degree-of-freedom exploratory finger. Multiple sensory information is fed to the finger control unit: most significant is that obtained through a composite, skinlike tactile sensor, developed in our laboratory and based on the technology of ferroelectric polymers. In this paper we discuss the design and describe some components of our sensorized scenario. The main features of the articulated exploratory finger are presented and a hybrid type of control, purposely devised for object exploration with tactile feedback, is outlined. Emphasis is also given to the discussion of design criteria for skin-like tactile sensors and to the description of the fingertip multifunctional ferroelectric polymer tactile sensor. Finally, some preliminary experimental results are presented.