This paper presents a three-dimensional (3D) control framework for bevel-tip steerable needles that combines model predictive control (MPC) with hierarchical supervisory logic. The MPC layer uses a reduced-order two-mode switching model to generate the desired control actions, while the supervisory logic adaptively prioritizes in-plane and out-of-plane corrections based on real-time error magnitudes. This hierarchical approach smoothly modulates the axial rotation commands to minimize abrupt needle flips, thereby reducing the so-called "drilling effect", a key source of tissue trauma. The simulation results show that the proposed approach reduces tissue trauma by more than 50% compared to conventional pulse-width-modulated sliding mode controllers while achieving mean absolute error and targeting errors in the submillimeter range.

The increasing interest in autonomous robots with a high number of degrees of freedom for industrial applications and service robotics demands control algorithms to handle multiple tasks as well as hard constraints efficiently. This article presents a general framework in which both kinematic (velocity- or acceleration-based) and dynamic (torque-based) control of redundant robots are handled in a unified fashion. The framework allows for the specification of redundancy resolution problems featuring a hierarchy of arbitrary (equality and inequality) constraints, arbitrary weighting of the control effort in the cost function and an additional input used to optimize possibly remaining redundancy. To solve such problems, a generalization of the saturation in the null space algorithm is introduced, which extends the original method according to the features required by our general control framework. Variants of the developed algorithm are presented, which ensure both efficient computation and optimality of the solution. Experiments on a KUKA LBRiiwa robotic arm, as well as simulations with a highly redundant mobile manipulator are reported.

Humans in contrast to robots are excellent in performing fine manipulation tasks owing to their remarkable dexterity and sensorimotor organization. Enabling robots to acquire such capabilities, necessitates a framework that not only replicates the human behaviour but also integrates the multi-sensory information for autonomous object interaction. To address such limitations, this research proposes to augment the previously developed kernelized synergies framework with visual perception to automatically adapt to the unknown objects. The kernelized synergies, inspired from humans, retain the same reduced subspace for object grasping and manipulation. To detect object in the scene, a simplified perception pipeline is used that leverages the RANSAC algorithm with Euclidean clustering and SVM for object segmentation and recognition respectively. Further, the comparative analysis of kernelized synergies with other state of art approaches is made to confirm their flexibility and effectiveness on the robotic manipulation tasks. The experiments conducted on the robot hand confirm the robustness of modified kernelized synergies framework against the uncertainties related to the perception of environment.

Today, physical human-robot interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, aerial physical interaction is also developing very fast. Nevertheless, pHRI with aerial vehicles has not been addressed so far. In this work, we present the study of one of the first systems in which a human is physically connected to an aerial vehicle by a cable. We want the robot to be able to pull the human toward a desired position (or along a path) only using forces as an indirect communication-channel. We propose an admittance-based approach with a controller, inspired by the literature on flexible manipulators, that computes the desired interaction forces that properly guide the human. The stability of the system is formally proved with a Lyapunov-based argument. The system is also shown to be passive, and thus robust to nonidealities like model and tracking errors, additional human forces, time-varying inputs, and other external disturbances. We also design a maneuver regulation policy to simplify the path following problem. The global method has been experimentally validated on a group of four subjects, showing a reliable and safe pHRI.

The field of soft robotics is grounded on the idea that, due to their inherent compliance, soft robots can safely interact with the environment. Thus, the development of effective planning and control pipelines for soft robots should incorporate reliable robot-environment interaction models. This strategy enables soft robots to effectively exploit contacts to autonomously navigate and accomplish tasks in the environment. However, for a class of soft robots, namely vine-inspired, tip-extending or "vine" robots, such interaction models and the resulting planning and control strategies do not exist. In this paper, we analyze the behavior of vine robots interacting with their environment and propose an obstacle-interaction model that characterizes the bending and wrinkling deformation induced by the environment. Starting from this, we devise a novel obstacle-interaction planning method for these robots. We show how obstacle interactions can be effectively leveraged to enlarge the set of reachable workspace for the robot tip, and verify our findings with both simulated and real experiments. Our work improves the capabilities of this new class of soft robot, helping to advance the field of soft robotics.

Modern robots act in dynamic and partially unknown environments where path replanning can be mandatory if changes in the environment are observed. Task-prioritized control strategies are well known and effective solutions to ensure local adaptation of robot behaviour. The highest priority in a stack of tasks is typically given to the management of correct robot operation or safe interaction with the environment such as obstacles or joint limits avoidance, that we can consider as constraints. If a constraint makes impossible achieving a certain task, such as tracking a Cartesian trajectory, a local control algorithm partially sacrifices the latter which is only accomplished to the best of the robot's ability to generate internal motions. In this control framework, problems may occur in some applications, like in the surgical domain, where it is not safe that some tasks are simply sacrificed without prior notice. The contribution of this work is to introduce a coordinate invariant index, that is used to provide a geometrical interpretation of task conflicts in a task-priority control framework and to develop a method for on-line detection of algorithmic singularities, with the goal of increasing safety and performances during robot operations.

Robot teleoperation is widely used for several hazardous applications. To increase teleoperator capabilities shared-control methods can be employed. In this paper, we present a passive task-prioritized shared-control method for remote telemanipulation of redundant robots. The proposed method fuses the task-prioritized control architecture with haptic guidance techniques to realize a shared-control framework for teleoperation systems. To preserve the semi-autonomous telerobotic system safety, passivity is analyzed and an energy-tanks passivity-based controller is developed. The proposed theoretical results are validated through experiments involving a real haptic device and a simulated slave robot.

Polyp dissection requires very accurate detection of the region of interest and high-precision cutting with adequate safety margins. Robot-assisted polyp dissection is a solution to accomplish high-quality intervention. This paper proposes a method to constrain the robot to follow an accurate dissection path based on Virtual Fixtures (VF). The VFs are created via specific control points obtained directly from images of the surgical scene and are updated by the vision algorithm. The VF constraints can autonomously adapt themselves to environment changing during the surgical intervention. The entire pipeline is validated through experiments on the da Vinci Research Kit (dVRK) robot.

During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re-grasp the needle using dual-arm movements. This yields an increased operator's cognitive load, time-to-completion and performance degradation. In this paper, we propose a haptic-guided shared control method for grasping the needle with the Patient Side Manipulator (PSM) of the da Vinci robot avoiding such issues. We suggest a cost function consisting of (i) the distance from robot joint limits and (ii) the task-oriented manipulability along the suturing trajectory. Evaluating the cost and its gradient on the needle grasping manifold allows us to obtain the optimal grasping pose for joint-limit and singularity free robot movements during suturing. We compute force cues and display them through the Master Tool Manipulator (MTM) to guide the surgeon towards the optimal grasp. As such, our system helps the operator to choose a grasping configuration that allows the robot to avoid joint limits and singularities during post-grasp suturing movements. We show the effectiveness of the proposed haptic-guided shared control method during suturing using both simulated and real experiments. The results illustrate that our approach significantly improves the performance in terms of needle re-grasping.

This paper presents a new procedure to design a control law using the classical interconnection and damping assignment technique within the passivity-based port-Hamiltonian framework. The sought goal is to reduce the complexity of solving the so-called matching equations. The proposed approach is applied to two case studies of planar rolling nonprehensile manipulation, namely, the ball-and-beam and the eccentric disk-on-disk. The performance of the resulting controllers is illustrated through both simulations and experimental results, showing the applicability of the design in a real setup.

This paper presents a method for tracking multiple interacting deformable objects undergoing rigid motions, elastic deformations and contacts, using image and point cloud data provided by an RGB-D sensor. A joint registration frame-work is proposed, based on physical Finite Element Method (FEM) elastic and interaction models. It first relies on a visual segmentation of the considered objects in the RGB images. The different segmented point clouds are then processed to estimate rigid transformations with on an ICP algorithm, and to determine geometrical point-to-point correspondences with the meshes. External forces resulting from these correspondences and between the current and the rigidly transformed mesh can then be derived. It provides both non-rigid and rigid data cues. A classical collision detection and response model is also integrated, giving contact forces between the objects. The deformations of the objects are estimated by solving a dynamic system balancing these external and contact forces with the internal or regularization forces computed through the FEM elastic model. This approach has been here tested on different scenarios involving two or three interacting deformable objects of various shapes, with promising results.

In Minimally Invasive Robotic Surgery (MIRS) a robot is interposed between the surgeon and the surgical site to increase the precision, dexterity, and to reduce surgeon's effort and cognitive load with respect to the standard laparoscopic interventions. However, the modern robotic systems for MIRS are still based on the traditional telemanipulation paradigm, e.g. the robot behaviour is fully under surgeon's control, and no autonomy or assistance is implemented. In this work, supervised and shared controllers have been developed in a vision-free, human-in-the-Ioop, control framework to help surgeon during a surgical suturing procedure. Experiments conducted on the da Vinci Research Kit robot proves the effectiveness of the method indicating also the guidelines for improving results.

Fuzzy Systems are an efficient instrument to create efficient and transparent models of the behavior of complex dynamic systems such as autonomous humanoid robots. The human interpretability of these models is particularly significant when it is applied to the cognitive robotics research, in which the models are designed to study the behaviors and produce a better understanding of the underlying processes of the cognitive development. From this research point of view, this paper presents a comparative study on training fuzzy based system to control the autonomous navigation and task execution of a humanoid robot controlled in a soccer scenario. Examples of sensor data are collected via a computer simulation, then we compare the performance of several fuzzy algorithms able to learn and optimize the humanoid robot's actions from the data.

A robotic system that is designed to coexist with humans has to adapt its behavioral and social interaction parameters not only with respect to the task it is supposed to accomplish, but also with respect to the human being it is interacting with by profiling her habits, preferences, and personality. This is particularly relevant in the domain of assistive robotics where the behavioral adaptability has been shown to enhance the users' acceptability of a robot. In this work, we propose a neuro-fuzzy-Bayesian system able to adapt the robot proxemics behavior with respect to the human users' personality and the action she is currently performing. The user's personality is evaluated according to the Big-Five factors model and the activity recognition is obtained by classifying data from a wearable device through the use of a Bayesian Network classifier. As shown by a statistical study, the proposed framework is capable of computing the most appropriate robot proxemics behavior in order to improve human feeling in interacting with artificial agents, such as robots.

International audience

Minimally invasive robotic surgery holds a fundamental role in modern surgery. However, one of its major limitations compared to classic laparoscopy is that the surgeon can only rely on visual perception, for the lack of haptic force feedback. A new solution for a force sensor placed at the end-tip of the trocar is presented here. This solution allows measuring the interaction forces between the surgical instrument and the environment without any changes to the instrument structure and with full adaptability to different robot platforms and surgical tools. A prototype of the sensor has been realized with 3D printed technology for a proof of concept. The static and dynamic characterization of the sensor is provided together with experimental validation.

The da Vinci Research Kit (DVRK) is a telerobotic surgical research platform endowed with an open controller that allows position, velocity and current control. We consider the problem of modelling and identification of both the Patient Side Manipulators (PSMs) and of the Master Tool Manipulators (MTMs) of the platform. This problem is relevant when realistic dynamic simulations have to be performed using standard software tools, but also for the design of model-based control laws, and for the implementation of sensorless strategies for collision detection or contact force estimation. A LMI-based approach is used for the identification of the robot dynamics in order to guarantee the physical feasibility of the parameters that is not ensured by standard least-squares methods. The identified models are validated experimentally.

In this paper, we present the design of a robust interconnection and damping assignment controller for a rolling-balancing system known as the disk-on-disk. The underactuation feature of this system hampers the control design, and since we consider matched disturbances, the problem becomes even more challenging. To overcome this difficulty, we propose to design first a controller to stabilize the desired equilibrium of the case where the disturbance is not present, and then we robustify this controller by adding a nonlinear PID outer loop that compensates the disturbance. Finally, we evaluate the practical applicability of the control design by implementing the controllers on a real hardware for the disk-on-disk system.

This paper presents an optimal trajectory planner for a robotic batting task . The specific case of a table tennis game performed by a robot is considered. Given an estimation of the trajectory of the ball during the free flight, the method addresses the determination of the paddle configuration (pose and velocity) to return the ball at a desired position with a desired spin. The implemented algorithm takes into account the hybrid dynamic model of the ball in free flight as well as the state transition at the impact (the reset map). An optimal trajectory that minimizes the acceleration functional is generated for the paddle to reach the desired impact position, velocity and orientation. Simulations of different case studies further bolster the approach along with a comparison with state-of-the-art methods.

One of the main problems in the prosthetic hand design is to provide the hand with a reliable system for force and slippage control. The real-time detection of an object slippage during grasping plays a fundamental role in performing a stable grasp. Aim of this paper is to propose and test (i) a system for object slippage detection during grasping, (ii) a control strategy for grasping force regulation and slippage prevention and (iii) a finger coordination method for replicating on the prosthesis system a human-like behaviour. The approach has been validated on a real prosthetic hand, i.e. the IH2 Azzurra (developed by Prensilia), on which tips force sensing resistors have been placed. The preliminary experimental results have demonstrated the feasibility of the methods for slipping detection and for force/slippage control and have shown that the prosthetic hand is able to perform a stable pinch grasp of different objects guaranteeing finger coordination.

In this work, we demonstrate how autonomous pizza tossing and catching can be achieved. Under the assumption that the pizza dough is grasped by a number of fingers with soft contact, we formulate the grasp constraints and use them to derive the individual and combined Euler-Lagrange dynamic equations of motion of the robotic manipulator and the dough. In particular, the dynamics of the dough is a modified version of the rigid-body dynamics, taking into account the change of inertia due to its deformation. Armed with these mathematical models, we tackle the two control problems of tossing and catching. For the tossing phase, we derive an exponentially convergent controller that stabilizes a desired velocity of the dough as it is let go. On the other hand, so as to catch the dough, we generate an optimal trajectory for the end-effector of the robotic manipulator. Finally, we derive control laws to make the optimal trajectory exponentially attractive. We demonstrate the developed theory with an elaborate simulation of the tossing and catching phases.

In this work, a synergy-based reinforcement learning algorithm has been developed to confer autonomous grasping capabilities to anthropomorphic hands. In the presence of high degrees of freedom, classical machine learning techniques require a number of iterations that increases with the size of the problem, thus convergence of the solution is not ensured. The use of postural synergies determines dimensionality reduction of the search space and allows recent learning techniques, such as Policy Improvement with Path Integrals, to become easily applicable. A key point is the adoption of a suitable reward function representing the goal of the task and ensuring one-step performance evaluation. Force-closure quality of the grasp in the synergies subspace has been chosen as a cost function for performance evaluation. The experiments conducted on the SCHUNK 5-Finger Hand demonstrate the effectiveness of the algorithm showing skills comparable to human capabilities in learning new grasps and in performing a wide variety from power to high precision grasps of very small objects.

This paper introduces a method able to track in real-time a 3D elastic deformable objects which undergo fractures, using the point cloud data provided by an RGB-D sensor. Our framework relies on a prior visual segmentation of the object in the image. The segmented point cloud is registered by non-rigidly fitting the mesh, based on the Finite Element Method to physically model elasticity, and on geometrical point-to-point correspondences to compute external forces exerted on the mesh. Fractures are handled by processing the stress tensors computed on the mesh of the FEM model, in order to detect fracturable nodes. Local remeshing around fracturable nodes is then performed to propagate the fracture. The real-time performance of the system is demonstrated on real data involving various deformations and fractures.

This paper proposes a new visual controller to endow flying manipulators with the capability of cooperatively and automatically positioning an assembly part on a visual target. We consider two VToL UAVs each equipped with a robot manipulator and a video camera. The two manipulators are rigidly connected to an assembly part, consisting of bar in this case study, that needs to be automatically placed on its target pose. The control system we propose uses the images relayed from the two onboard cameras to simultaneously control the motions of the UAVs and the manipulators to achieve the task: positioning the bar as well as respecting the closed-chain constraint. The controller evolves in the image space, thus inheriting robustness with respect to calibration errors. It takes into account the under-actuation inherent to rotary-wing aerial vehicles as well as the redundancy of the whole system. Furthermore, by leveraging image moments the 3D relative motion between the carried assembly part and the environment is controlled, thus yielding to natural and practically-relevant movements. Numerical simulations have been carried out to verify the validity of the proposed approach.

A multilayer architecture to control multirotor UAVs equipped with a servo robot arm is proposed in this paper. The main purpose is to control the aerial platform taking into account the presence of the moving manipulator. Three layers are considered in this work. First, a novel mechanism is proposed considering a moving battery to counterweight the statics of the robotic arm. Then, in order to overcome the mechanical limitations of the previous layer, the residual of the arm static effects on the UAV is computed and compensated through the given control thrust and torques. Finally, an estimator of external forces and moments acting on the aerial vehicle is considered and the estimations are fed back to the controller to compensate neglected aerodynamic effects and the arm dynamics. The performance of the proposed architecture has been experimentally evaluated.

In many real life scenarios, which span from domestic interactions to industrial manufacturing processes, the objects to be manipulated are non-rigid and deformable, hence, both the location of the object and its deformation have to be tracked. Different methodologies have been applied in literature, using different sensors and techniques for addressing this problem. The main contribution of this paper is to propose a Weightless Neural Network approach for non-rigid deformable object tracking. The proposed approach allows deploying an on-line training on the shape features of the object, to adapt in real-time to changes, and to partially cope with occlusions. Moreover, the use of parallel classifiers trained on the same set of images allows tracking the movements of the objects. In this work, we evaluate the filtering/segmentation performance that is a fundamental step for the correct operation of our approach, in the scenario of pizza making.

This paper presents a method to track in real-time a 3D textureless object which undergoes large deformations such as elastic ones, and rigid motions, using the point cloud data provided by an RGB-D sensor. This solution is expected to be useful for enhanced manipulation of humanoid robotic systems. Our framework relies on a prior visual segmentation of the object in the image. The segmented point cloud is registered first in a rigid manner and then by non-rigidly fitting the mesh, based on the Finite Element Method to model elasticity, and on geometrical point-to-point correspondences to compute external forces exerted on the mesh. The real-time performance of the system is demonstrated on synthetic and real data involving challenging deformations and motions.

This paper presents an experimental study on human-robot comanipulation in the presence of kinematic redundancy. The objective of the work is to enhance the performance during human-robot physical interaction by combining Cartesian impedance modulation and redundancy resolution. Cartesian impedance control is employed to achieve a compliant behavior of the robot's end effector in response to forces exerted by the human operator. Different impedance modulation strategies, which take into account the human's behavior during the interaction, are selected with the support of a simulation study and then experimentally tested on a 7-degree-of-freedom KUKA LWR4. A comparative study to establish the most effective redundancy resolution strategy has been made by evaluating different solutions compatible with the considered task. The experiments have shown that the redundancy, when used to ensure a decoupled apparent inertia at the end effector, allows enlarging the stability region in the impedance parameters space and improving the performance. On the other hand, the variable impedance with a suitable modulation strategy for parameters' tuning outperforms the constant impedance, in the sense that it enhances the comfort perceived by humans during manual guidance and allows reaching a favorable compromise between accuracy and execution time.

In this paper, we propose a nonlinear controller that stabilizes unmanned aerial vehicles in GPS-denied environments with respect to visual targets by using only onboard sensing. The translational velocity of the vehicle is estimated online with a nonlinear observer, which exploits spherical visual features as the main source of information. With the proposed solution, only four visual features have shown to be enough for the observer to operate in a real scenario. In addition, the observer is computationally light with constant numerical complexity, involving small-dimension matrices. The observer output is then exploited in a nonlinear controller designed with an integral backstepping approach, thus yielding a novel robust control system. By means of Lyapunov analysis, the stability of the closed-loop system is proved. Extensive simulation and experimental tests with a quadrotor are carried out to verify the validity and robustness of the proposed approach. The control system runs fully onboard on a standard processor, and only a low-cost sensing suite is employed. Tracking of a target whose speed exceeds 2 $\mathrm{m/s}$ is also considered in the real-hardware experiments.

This paper addresses the problem of controlling a robot arm executing a cooperative task with a human who guides the robot through direct physical interaction. This problem is tackled by allowing the end effector to comply according to an impedance control law defined in the Cartesian space. While, in principle, the robot's dynamics can be fully compensated and any impedance behaviour can be imposed by the control, the stability of the coupled human-robot system is not guaranteed for any value of the impedance parameters. Moreover, if the robot is kinematically or functionally redundant, the redundant degrees of freedom play an important role. The idea proposed here is to use redundancy to ensure a decoupled apparent inertia at the end effector. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we show that inertial decoupling enables a more flexible choice of the impedance parameters and improves the performance during manual guidance.

Manipulation tasks carried out with aerial platforms composed of a UAV and a robotic arm involve cross-coupled dynamics between these subsystems. This paper proposes a new controller for this class of aerial robotic systems that allows regulating on velocity commands generated by an outer image-based visual-servo scheme. The controller, that considers the full dynamics of the system, is designed based on the integral backstepping approach. Visual feedback provided by an onboard camera is employed into a new visual servo scheme to simultaneously generate velocity commands for the UAV and the manipulator so that a visual target is reached. The control system takes into account the under-actuation related to rotary-wing vehicles, while at the same time it exploits the functionality system redundancy to achieve the task. Simulation results validate the proposed control system, as well as its robustness to large modeling error and measurement noise.

In this paper, the problem of controlling a robot manipulator in task space, while guaranteeing a compliant behavior for the redundant degrees of freedom, is considered. This issue may arise in the case where the robot experiences an interaction on its body, especially in the presence of humans. The proposed approach guarantees correct task execution and compliance of the robot's body during intentional or accidental interaction in the null space of the main task, simultaneously. The asymptotic stability of the task-space error is ensured by using suitable observers to estimate and compensate the generalized forces acting on the task variables, without using joint torque measurements. Two different controller-observer algorithms are designed, and they are based on the task-space error and on the generalized momentum of the robot, respectively. The performance of the proposed algorithms is verified in experiments on a 7R lightweight robot arm.

The wrist plays a fundamental role in reaching and grasping actions, i.e. it guides the hand to the grasp position and adjusts its orientation on the basis of the grasping type and task. This paper proposes a novel, low-cost method for wrist pose estimation by using the Asus Xtion Pro Live motion sensing device and a robust marker-based tracking approach based on Unscented Kalman Filter (UKF). The hand palm kinematic model is also considered. The applicability of the approach to evaluate some interesting kinematics parameters, such as position, orientation, Range Of Motion, angular and linear velocity and trajectory has been proved. In particular, since the nature of the paper is to present a novel approach for wrist pose estimation, only initial validation for wrist kinematic measurement will be reported.

Motion estimation is an open research field in control and robotic applications. Sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical modelbased techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.

The computation of the grasping forces for a multiarm robotic manipulation system (e.g., an anthropomorphic bimanual system) is considered in this paper. This problem is formulated as a convex optimization problem, also considering joint torque constraints. An algorithmic solution suitable for online implementation is presented, which allows a substantial reduction in the computational load by adopting a compact formulation and dynamically decreasing the number of active torque constrains. Moreover, for the case of a bimanual manipulation system, a suboptimal single-hand optimization algorithm is proposed and compared with that providing the optimal solution. Finally, a new algorithm for a valid initial-point evaluation is proposed. The effectiveness of the described methods has been tested in a simulation case study where the grasping forces of a humanoid torso equipped with two five-finger robotic hands are modified online to handle a load with a time-varying mass.

In this paper, a method to derive the three predominant synergies and their temporal weights for planning grasps of the UB Hand IV (University of Bologna Hand, version IV) is proposed. The method adopted to define the postural synergies from experiments is based on the kinematic structure of the robotic hand and on the taxonomy of the grasps of common objects. The control strategy, exploiting postural synergies, that drives the hand during reach to grasp is further described. During prehension the hand moves continuously in a configuration space of highly reduced dimensionality with respect to its degrees of freedom. The experiments confirm that the UB Hand IV works efficiently in a synergy based framework for grasp planning and prehension control. It is shown that the introduction of the third predominant synergy significantly improves the grasping synthesis and performance, especially for the adduction/abduction motion of the thumb.

The problem of grasping force optimization for a robotic system equipped with multi-fingered hands is considered in this paper. This problem is cast as a convex optimization problem, considering also joint torque constraints. A solution suitable for on-line implementation is proposed, which allows a substantial reduction of the computational load by dynamically decreasing the number of active torque constraints. Moreover, for the case of a bimanual manipulation system, a sub-optimal single-hand optimization algorithm is presented and compared with the optimal one. The effectiveness of the described methods has been tested in a simulation case study.

In this paper an adaptive multi-priority nonlinear control algorithm for a redundant manipulator system is developed based on the Lyapunov like approach. The method considers the parametric uncertainties in the system and defines a proper filtered error signal to achieve asymptotic stability and convergence in tracking error, both for the main task and sub-tasks according to the allocated priority. The performance of the proposed method is studied by some numerical simulations.

An autonomous wall inspection control based on a stereo optical flow, suitable for unmanned aerial vehicles endowed with a stereo vision system, is proposed in this paper. The inspection task consists of simultaneously controlling the inspection velocity along the surface, the relative yaw angle between the vehicle and the observed plane, as well as the orthogonal distance. A virtual spherical camera is considered at the center of gravity of the vehicle. Then, a stereo optical flow, as if it had been acquired by the virtual camera, is generated from the visual data provided by the stereo vision system. The 3D visual measurements are also employed to estimate the relative position and orientation of the observed plane. Hence, the absolute vehicle velocity is estimated by using a robust translational average optical flow by integrating the total stereo flow. Finally, an inspection control and a hovering control are proposed. The effectiveness of the described approach has been demonstrated with a dynamic simulation in an environment composed of two adjacent walls.

In this paper a new approach for the null-space impedance control of a kinematically redundant robot is proposed. The approach is useful for the case where the robot experience an external interaction on the body, especially in the presence of humans. The proposed algorithm guarantees safe and dependable physical interaction of the robot body with the environment, thanks to the null-space impedance control. At the same time, the correct execution of the task assigned to the end effector is ensured by a disturbance observer. The algorithm does not require joint torque measurements. The performance of the proposed controller is verified through simulations on 7R KUKA lightweight robot arm.

In this paper, a new algorithm for online grasping force optimization (GFO) of a dextrous robotic hand is presented. The GFO problem is cast in a convex optimization problem, considering also torque joint constraints. The proposed formulation allows to simplify the computational complexity of the problem by dynamically reducing the number of active torque constraints. Moreover, differently from other approaches, it does not require the evaluation of a new initial point at the beginning of each iteration. The effectiveness and the performance of the proposed method have been tested in a simulation case study where the hand manipulates a load with time-varying mass.

In this paper, the postural synergies configuration subspace given by the fundamental eigengrasps of the UB Hand IV (University of Bologna Hand, version IV) is derived through experiments. This study is based on the kinematic structure of the robotic hand and on the taxonomy of the grasps of common objects. Experimental results show that it is possible to obtain grasp synthesis for a large set of objects both in the case of precision or power grasps by using only a very limited set of dominant eigengrasps. The tasks here presented are planned with an initial hold of the hand followed by reach and grasp phases, that are unique for each object/grasp combination, during which the robotic hand posture evolves continuously within a subset of the hand configuration space given by the two predominant eigenpostures. The paper reports the method adopted to define from experiments the postural synergies for the UB Hand IV and the results of the grasp tasks performed adopting the defined synergies.

This paper presents a dynamic level control algorithm to meet simultaneously multiple desired tasks based on allocated priorities for redundant robotic systems. It is shown that this algorithm can be treated as a general framework to achieve control over the whole body of the robot and some of the previously developed results are formalized using this approach. Null-space impedance control is proposed as one of the main results of using this method and is evaluated by means of computer simulation.

In this paper a method for fast visual grasp of unknown objects with a multi-fingered hand is presented. The algorithm is composed of an object surface reconstruction algorithm and a local grasp planner, evolving in parallel. The former uses an elastic reconstruction surface, whose dimensions are assigned initially by a preshaping process, and which shrinks toward the object until some parts of the surface intercept the object visual hull. The latter moves the fingertips on the current available reconstruction surface towards points which are optimal (in a local sense) with respect to a certain number of indices weighting both the grasp quality and the kinematic configuration of the hand. Experiments are presented, showing the effectiveness of the proposed algorithm.

In this paper, an approach to interaction control of a robot manipulator with a partially known environment is proposed. The environment is a rigid object of known geometry but of unknown and possibly time-varying position and orientation. An algorithm for online estimation of the object pose is adopted, based on visual data provided by a camera as well as on forces and moments measured during the interaction with the environment. This algorithm is embedded into an impedance control scheme, resulting in a position-based visual impedance control. Experimental results are presented for the case of an industrial robot manipulator in contact with a planar surface.

This paper deals with the problem of position-based visual servoing in a multiarm robotic cell equipped with a hybrid eye-in-hand/eye-to-hand multicamera system. The proposed approach is based on the real-time estimation of the pose of a target object by using the extended Kalman filter. The data provided by all the cameras are selected by a suitable algorithm on the basis of the prediction of the object self-occlusions, as well as of the mutual occlusions caused by the robot links and tools. Only an optimal subset of image features is considered for feature extraction, thus ensuring high estimation accuracy with a computational cost independent of the number of cameras. A salient feature of the paper is the implementation of the proposed approach to the case of a robotic cell composed of two industrial robot manipulators. Two different case studies are presented to test the effectiveness of the hybrid camera configuration and the robustness of the visual servoing algorithm with respect to the occurrence of occlusions

An algorithm for the estimation of the position and orientation of a moving object using a hybrid eye-in-hand/eye-to-hand multi-camera system is presented. Based on the extended Kalman filter, this approach exploits the data provided by all the cameras without "a priori" discrimination, allowing real-time estimation. The proposed formulation can be used with different kinds of image features and different representations of the object orientation. A simulation case study is reported to test the feasibility and the effectiveness of the proposed technique

The problem of interaction control of a robot manipulator with a partially known environment is considered. The environment is a rigid object of known geometry but of unknown and possibly time varying position and orientation. An algorithm for online estimation of the object pose is presented, based on visual data provided by a camera. The estimation accuracy is improved during the interaction by using also force and joint position measurements. The proposed method can be exploited to implement any kind of interaction control strategy. A simulation case study is considered for the case of impedance control of a robot manipulator in contact with a planar surface

In this paper the problem of automatic trajectory planning and inverse kinematics for a robotic system purposely designed to extinguish fires in road and railway tunnels is considered. The robot is composed by a self-cooling monorail vehicle carrying a fire fighting monitor. A fuzzy inference system is adopted for the automatic generation of the task-space trajectory for the robot and to solve the inverse kinematics problem in the presence of redundant degrees of freedom. Redundancy also allows assigning additional tasks besides the primary task. Simulation case studies are presented to test the performance of the whole system in a typical intervention scenario.

An environment for open real-time control of an industrial robotic cell is presented in this paper. The experimental setup is composed of two industrial robot manipulators equipped with force/torque sensors and pneumatic grippers, a vision system and a belt conveyor. The original industrial robot controllers have been replaced by a single PC with software running under a real-time variant of the Linux operative system. The new control environment allows advanced control schemes to be developed and tested for the single robots and for the dual-arm robotic cell, including force control and visual servoing tasks. An advanced user interface and a simulation environment have been developed, which permit fast, safe and reliable prototyping of planning and control algorithms.

In this paper a PD control with on-line gravity compensation is proposed for robot manipulators with elastic joints. The work extends the existing PD control with constant gravity compensation, where only the gravity torque needed at the desired configuration is used throughout motion. The control law requires measuring only position and velocity on the motor side of the elastic joints, and the on-line compensation scheme estimates the actual gravity torque using a biased measure of the motor position. It is proved via a Lyapunov argument that the control law globally stabilizes the desired robot configuration. Experimental results on an 8-d.o.f. robot manipulator with elastic joints show that this control scheme improves the transient behavior with respect to a PD controller with constant gravity compensation. In addition, it can be usefully applied in combination with a point-to-point interpolating trajectory leading to a reduction of final steady-state errors due to static friction and/or uncertainty in the gravity compensation.

An algorithm for the visual estimation of the pose of a moving object is presented in this paper. The algorithm exploits the prediction capability of the extended Kalman Filter to realize in real time a dynamic optimal selection of the object image features used for pose estimation. The robustness of the system with respect to the measurement noise and modelling errors is enhanced by using an adaptive scheme. Experimental case studies are presented to prove the effectiveness of the proposed approach.

The problem of tracking the position and orientation of a moving object using a stereo camera system is considered in this paper. A robust algorithm based on the extended Kalman filter is adopted, combined with an efficient selection technique of the object image features, based on Binary Space Partitioning tree geometric models. An experimental study is carried out using a vision system of two fixed cameras.

Neurophysiological studies show that humans possess the capability of generating appropriate motor behaviors to different uncertain environmental conditions by combining a forward action, produced by the internal forward dynamic model, and a feedback control, realising the transformation from sensory information to motor commands. To this regard, a control system based on the combination of a feedforward and a feedback control loop has been developed in order to provide a robot arm with human-like adaptation capabilities. The work analyses the role of biological coactivation in the mechanism of adjustable visco-elastic arm properties and proposes a function for the evaluation of the robot arm coactivation based on the measure of the position error and the interaction force. The coactivation function is used to update the proportional and derivative parameters of the feedback controller and, consequently, the arm visco-elasticity in unpredictable environmental conditions. Finally, experimental results on the evolution of the coactivation in the adaptation and de-adaptation phases are provided in the last section of the paper.

The problem of visual tracking of multiple objects is considered in this paper. Special emphasis is devoted to the case when two or more objects overlap with respect to the visual system causing occlusion. The algorithm is based on the Kalman filtering and binary space partition tree representations of the objects geometry. The real-time implementation of the algorithm is experimentally tested for the case of visual tracking of two objects using two cameras.

In this paper a computationally efficient algorithm for real time estimation of the position and orientation of moving objects from visual measurements of a system of fixed cameras is proposed. The algorithm is based on extended Kalman filtering of the measurements of the position of suitable feature points selected on the target objects. The effectiveness of the algorithm is improved by using a pre-selection method of the feature points which takes advantage of the Kalman filter prediction capability combined with a BSP tree modeling technique of the objects geometry. Computer simulations are presented to test the performance of the estimation process in the presence of noise, different types of lens geometric distortion, quantization and calibration errors.

This paper presents a research work on compliant control of an anthropomorphic robot arm used as a personal robot. In personal applications of robotics, human-robot interaction represents a critical factor for a robot design and introduces strict requirements on its behavior and control, which has to ensure safety and effectiveness. In this work, the problem of controlling the Dexter anthropomorphic robot arm with variable compliance has been investigated, not only to ensure safety in the interaction with humans, but especially to increase the robot functionality in tasks of physical interaction, performed in co-operation with humans. Two different control schemes have been formulated and implemented, to compare their performance experimentally. Both schemes aim at realising a self-controlled compliant behavior without using information from force/torque sensors. The experimental comparison outlines how the performance of the two control systems are inverted with respect to the theoretical considerations, based on the classical control theory, on their accuracy and effectiveness.

The problem of real-time pose estimation of moving objects using a stereo video camera system is considered in this paper. A computationally efficient algorithm is proposed based on Kalman filtering of the position measurements of suitable feature points selected on the target objects. The efficiency of the algorithm is improved by adopting a new pre-selection technique of the feature points, based on binary space partition (BSP) trees, which takes advantage of the Kalman filter prediction capability. Computer simulations are presented.

A research work on the interaction control of a cable-actuated robot arm, the Dexter arm, is presented in this paper. Firstly, general considerations on the cable-actuated structures and their application potential are provided and then the Dexter structure peculiarities are accurately analyzed in order to develop proper control solutions. Starting from the analysis of the limitations of the compliance control schemes in Cartesian space and in joint space, previously implemented and experimentally validated on the Dexter arm, a novel control strategy, named impedance-compliance controller, is developed. The proposed control strategy tries to combine the benefits of a compliance control scheme in Cartesian space with the benefits of an impedance control scheme in the operational space by compensating the dynamics of the sole proximal joints. The impedance-compliance controller is capable to achieve accurate smooth motions while guaranteeing functional control of the whole structure, even though a greater computational complexity is required The last section of the paper, dedicated to the experimental results, points out the differences with the previously experimented control solutions anti provides some proofs of the increased Dexter functionality.

Addresses kinematic control of a redundant space manipulator mounted on a free-floating spacecraft. Redundancy of the system with respect to the number of task variables for spacecraft attitude and manipulator end-effector pose is considered. Also, the problem of both spacecraft attitude and end-effector orientation representation is tackled. A nonminimal singularity-free representation of rigid body orientation is adopted: the unit quaternion; this allows avoiding representation singularities and formulating the inverse kinematics algorithms in terms of geometrically meaningful variables. Depending on the nature of the task for the spacecraft/manipulator system, a number of closed-loop inverse kinematics algorithms are proposed. Case studies are developed for a system of a spacecraft with a six-joint manipulator attached.

The problem of controlling force and position of a flexible link manipulator in contact with a compliant environment is considered in this paper. Using the singular perturbation theory, a slow subsystem associated with rigid motion dynamics and a fast sub-system associated with link flexible dynamics are identified. Hence, a two-time scale control strategy is adopted, consisting of a force and position control action for the slow sub-system and a stabilizing action for the fast subsystem. Both regulation and tracking problems are considered for the position, while the contact force is kept to a constant desired value. Simulations are presented for a two-link planar manipulator under gravity.

The research in the field of advanced robotics is turning its attention more and more to man and his assistance, by developing systems such as service robots, personal robots, and even humanoid robots. Interaction control of such robot manipulators is of paramount importance for an effective execution of manipulation and tracking and, over all, for a safe and effective interaction with the humans. The paper concerns the problem of the control of an 8 degree of freedom anthropomorphic arm named DEXTER, mounted on the mobile platform of the MOVAID System, a robotic system for household personal assistance. The goal is to realize a compliant control for this manipulator in tasks of assistance to disabled and elderly people. On the basis of the control theory applied to industrial robotics, a specific compliant control solution has been developed for the DEXTER peculiar mechanical structure and actuation system, which cause a coupled joint configuration. The solution provides the capability of regulating the robot compliance according to the level of stiffness of the interaction environment. The paper describes the theoretical model of the control system, the implementation on the MOVAID platform and the experimental results in the execution of a set of demonstration tasks.

The goal of the paper is the application of a geometrically consistent impedance concept to control interaction with the environment of a rigid object manipulated by a dual-robot system. A six-DOF impedance is specified at the object level to confer a compliant behavior for both the translational and the rotational motion when an external force and moment occurs at the contact. Geometric consistency is ensured thanks to the use of the unit quaternion to describe object frame orientation. The resulting object motion is decomposed into the equivalent motions at the end effectors of the two robots, via a task-oriented formulation. The control scheme is derived according to an inverse dynamics strategy with adoption of an inner motion loop providing robustness to unmodeled dynamics and disturbances. Experimental results on the two industrial robots available in the lab are discussed.

This work is focused on impedance control of redundant manipulators. A spatial impedance formulation is presented where general 6-DOF end-effector tasks can be handled. A singularity-free angle/axis representation of end-effector orientation is used which allows geometric task consistency to be preserved. An inverse dynamics control with a dynamically consistent inverse of the geometric Jacobian matrix is developed with the adoption of an inner loop acting on the end-effector position and orientation conferring robustness to unmodeled dynamics and external disturbances. Stabilization of null-space velocities is ensured and utilization of redundant degrees of mobility is carried out to optimize an additional task function. Experimental results on a seven-joint industrial robot with force/torque sensor are discussed.

A new approach to 6-DOF impedance control is proposed, where the end-effector orientation displacement is derived from the rotation matrix expressing the mutual orientation between the compliant frame and the desired frame. An alternative Euler angles-based description is proposed which mitigates the effects of representation singularities. Then, a class of angle/axis representations are considered to derive the dynamic equation for the rotational part of a 6-DOF impedance at the end effector, using an energy-based argument. The unit quaternion representation is selected to further analyze the properties of the rotational impedance. The resulting impedance controllers are designed according to an inverse dynamics strategy with contact force and moment measurements, where an inner loop acting on the end-effector position and orientation error is adopted to confer robustness to unmodeled dynamics and external disturbances. Experiments on an industrial robot were carried out, and the results of case studies are discussed.

The work is aimed at studying the problem of controlling the moment and the orientation of a robot manipulator whose end effector is in contact with a compliant environment. Differently from classical operational space formulations, a geometrical approach is pursued where orientation displacements are described in terms of unit quaternions. Regulation to a constant desired moment and tracking of a time-varying desired orientation trajectory is achieved, and the convergence of the closed-loop system is analyzed. Simulation results for a six-joint industrial robot are developed and an experimental test is carried out to demonstrate the effectiveness of the proposed approach in a practical contact task.

In the framework of parallel force/position control for a robot manipulator in contact with a compliant environment, a new scheme is proposed which is aimed at controlling the end-effector force in the face of uncertainty on the surface stiffness. The controller is of inverse dynamics type with a force feedforward action. Adaptation to unknown stiffness is achieved by resorting to a suitable estimate update law driven by the force error. Tracking of both position along the unconstrained directions and force along the constrained direction is ensured. Experimental results on an industrial robot with open control architecture are presented.

The problem of regulation of force and position for robot manipulators in contact with an elastically compliant plane is considered in this work. As an improvement over a recently proposed regulator, a new control scheme is presented which does not require velocity measurements due to a suitable filtering action. Local asymptotic stability is proven via a Lyapunov argument. A case study is developed to illustrate the performance of the scheme on an industrial robot.

The problem of designing a control scheme for robot manipulators in contact with a compliant surface is considered. A passivity-based force/position control scheme is designed. A keen choice of the reference vector used in the control law is proposed as a function of the end-effector position and velocity errors and the integral of the contact force error. It is demonstrated that the scheme ensures tracking of the unconstrained components of the desired end-effector trajectory with regulation of the desired contact force along the constrained direction. In the imperfect model compensation case, the scheme is made adaptive with respect to a set of dynamic parameters. Numerical case studies are presented for an industrial robot manipulator.>

A simple controller is presented for the regulation problem of robot arms with flexible links under gravity. It consists of a joint PD feedback plus a constant feedforward. Global asymptotic stability of the reference equilibrium state is shown under a structural assumption about link elasticity and a mild condition on the proportional gain. The result holds also in the absence of internal damping of the flexible arm. A numerical case study is presented.>

The manipulability ellipsoids are used in robotics as a measure of manipulators' performances achievable during task execution. The definition of these geometrical entities is based on the Jacobian of the manipulator, and the physical meaning given to the ellipsoids is related to the capability of the mechanism to apply forces with the end effector or to move the tool in some directions of the task-space. P. Chiacchio et al. (ibid., vol.7, p.678-685, 1991) have extended this concept to the case of cooperating manipulators. Nevertheless, some questions are still open, both with respect to the basic definition and use of manipulability ellipsoids. The commenter shows by simple examples that the use of manipulability ellipsoids for multiarm systems gives misleading results, and he disagrees with the above authors' definition of ellipsoids. The authors defend their work.>

Three schemes are developed which are aimed at achieving cooperative control of multiple arm systems manipulating a common object. The first scheme operates wholly on the object task space variables. The second scheme operates on the joint space variables that can be derived via a kinematic inversion from the cooperative task space variables. The third scheme combines the features of the two by solving the cooperation at the inverse kinematic level and acting the control at the object level. Simulation results are provided for a two-arm planar system to investigate the behavior of the controlled system in the case of inaccurate object modeling.>

New definitions of force and velocity manipulability ellipsoids for multiple-arm systems are given. A suitable kinetostatic formulation for multiple cooperating arms is adopted that allows a global task space description of external and internal forces as well as absolute and relative velocities at the object level. The concept of a force manipulability ellipsoid for a single arm is formally extended to the multi-arm case by regarding the whole system as a mechanical transformer from the extended joint space to the global task space. Kinetostatic duality properties are then exploited to derive velocity manipulability ellipsoids for the multiple-arm system. The proposed method is compared with other approaches using numerical examples.>

Closed-form equations of motion are presented for planar lightweight robot arms with multiple flexible links. The kinematic model is based on standard frame transformation matrices describing both rigid rotation and flexible displacement, under small deflection assumption. The Lagrangian approach is used to derive the dynamic model of the structure. Links are modeled as Euler-Bernoulli beams with proper clamped-mass boundary conditions. The assumed modes method is adopted in order to obtain a finite-dimensional model. Explicit equations of motion are detailed for two-link case assuming two modes of vibration for each link. The associated eigenvalue problem is discussed in relation with the problem of time-varying mass boundary conditions for the first link. The model is cast in a compact form that is linear with respect to a suitable set of constant parameters. Extensive simulation results that validate the theoretical derivation are included.>

A novel algorithmic approach to the reachable workspace determination is presented. The reachable workspace is that volume of space which the end effector can reach. The present work is restricted to special geometries which have a 2R or a 3R planar pair, but the resulting algorithm appears to be applicable to any manipulator geometry. Computation results are presented for a 2R and a 3R redundant planar pair.>

Based on a recently proposed algorithmic solution technique, the inverse kinematic problem for redundant manipulators is solved. The kinematics of the manipulator is appropriately augmented to include mentioned constraints; the result is an efficient, fast, closed-loop algorithm which only makes use of the direct kinematics of the manipulator. Simulation results illustrate the tracking performance for a given trajectory in the Cartesian space, while guaranteeing a collision-free trajectory and/or not violating a mechanical joint limit.>

The control of a flexible arm is the focus of this work. The dynamic model is obtained via an assumed mode method, and a singularly perturbed model of the system is then derived. The new feature of this model is that multiple boundary layers are considered, one for each assumed mode. A composite control strategy is then pursued. First a slow control is designed for the rigid system, then a number of fast controls (one for each layer) make the fast dynamics in the layers suitably damped. A case study is finally worked out and simulation results are presented.

Redundancy represents one key towards design and synthesis of more versatile manipulators. Obstacle avoidance and limited joint range constitute two kinds of constraints which can be potentially met by a kinematically redundant manipulator. The natural scenario is the inverse kinematic problem which is certainly a crucial point for robotic manipulator analysis and control. Based on a recently proposed dynamic solution technique, the inverse kinematic problem for redundant manipulators is solved in this paper. The kinematics of the manipulator is appropriately augmented in order to include the above mentioned constraints; the result is an efficient, fast dynamic algorithm which only makes use of the direct kinematics of the manipulator. Extensive simulation results illustrate the tracking performance for a given trajectory in the Cartesian space, while guaranteeing a collision-free trajectory and/or not violating a mechanical jointiimit.

One of the most important features of an advanced control system for articulated robots is the capability of transforming the work space coordinates, which naturally characterize any robot task, into the joint coordinates, on which control actions are developed (Inverse Kinematic Problem). While simple kinematical structures allow for closed form solutions, there is a class of robots for which this is not true. If the three axes of revolution at the end effector intersect two-by-two an exact solution seems not to exist. The goal of the paper is to establish a fairly different solution algorithm, as compared to the trigonometric approach, which yields solutions in the above case. The algorithm is shown to be convergent along any trajectory. It proves very fast since it is based only on direct kinematics. Numerical examples are finally developed.

One of the most important features of an advanced control system for articulated robots is the capability of transforming the work space coordinates, which naturally characterize any robot task, into the corresponding joint coordinates, on which control actions are developed. For each task, the coordinate transformation problem consists in calculating one trajectory in the joint space which corresponds to the end effector trajectory, usually given in the Cartesian space. While simple kinematical structures allow for closed-form solutions, there is a class of robots for which this is not true. Typical articulated robot structures have three revolute joints at the end effector; the geometric parameters of these joints actually determine the spatial configuration of the last axes of motion. The large majority of today's nonredundant structures have three intersecting axes at the end effector, and closed form solutions do exist in this case. If the axes intersect two-by-two, as in some rather common arm design, an exact solution seems not to exist. A quite different solution algorithm is established, as compared to the trigonometric approach widely adopted so far, which yields solutions in case of two-by-two intersecting axes. The convergence of the algorithm along any trajectory is proved. Effectiveness of the proposed technique can be argued by the fact that it only makes use of direct kinematics, thus resulting in a contained computational burden. A robot prototype of the kind described above is taken as a reference in order to discuss digital implementation and develop numerical examples.