Collaborative robots (cobots) are democratizing industrial automation with their user-friendly programming approaches. Nevertheless, the Blockly-like interfaces typically available on cobots still require the user to define the program logic flow. Recent advancements in robotics research provide the robotic system with the reasoning capabilities given by symbolic artificial intelligence. This way, the cobot can acquire a new skill from a user demonstration, understand its semantics, and use symbolic planning for grounding and sequencing. Such methodologies rely on a symbolic description of the scene that should adequately represent how the cobot’s actions modify the environment. The symbols employed in the literature, however, either lack descriptive accuracy or are too specific for the targeted task, resulting in the application of the proposed teaching methodologies only to simple scenarios. This paper addresses these issues by introducing a methodology for symbolically describing general-purpose spatial relations between entities in a workspace, enhancing the flexibility and the range of application of cobots symbolic reasoning for complex manipulation tasks. The proposed approach involves defining a tunable set of predicates for relative positions and orientations, enabling precise symbolic representations, necessary for real-world tasks. The adoption of these symbols into a Programming by Demonstration framework empowers non-expert users to teach skills and deploy cobots in complex industrial tasks without coding. Experimental results demonstrate the effectiveness of this method, showing that first-time users can deploy cobots for a complex machine tending task comprising parts reorientations. • Application of semantic-informed Programming by Demonstration for industrial tasks. • Definition of tunable, general-use quantitative symbols for relative positions. • Development of a strategy for representing relative orientations symbolically. • Validation through a user study on a machine-tending task with part reorientations.

Object manipulation without relying on complex fixtures remains a largely unresolved issue in industrial robotics, being generally limited to pick-and-place operations of easy to grasp objects. This work presents an adaptable manipulation planning algorithm for dual-arm robots, aiming to reorient an object from an initial position to a specified goal configuration without the need of external fixtures. Our approach integrates a precomputed regrasp graph with an online optimal handover planner that transforms the high-level sequence searched from the graph into executable grasp and handover poses. This approach reduces the overall graph complexity and enhances planning efficiency by merging high-level optimal sequence planning with the execution of predetermined motion primitives. The proposed algorithm is validated using different types of objects and a collaborative dual-arm robot. We also propose a comparison between our method and two benchmark approaches on a bin picking use case, to demonstrate how our pipeline improves the task efficiency.

In Human-Robot Collaboration, safety mechanisms such as Speed and Separation Monitoring and Power and Force Limitation dynamically adjust the robot’s speed based on human proximity. While essential for risk reduction, these mechanisms introduce slowdowns that makes cycle time estimation a hard task and impact job scheduling efficiency. Existing methods for estimating cycle times or designing schedulers often rely on predefined safety models, which may not accurately reflect real-world safety implementations, as these depend on case-specific risk assessments. In this paper, we propose a deep learning approach to predict the robot’s safety scaling factor directly from process execution data. We analyze multiple neural network architectures and demonstrate that a simple feed-forward network effectively estimates the robot’s slowdown. This capability is crucial for improving cycle time predictions and designing more effective scheduling algorithms in collaborative robotic environments.

This paper addresses the optimization of human-robot collaborative work-cells before their physical deployment. Most of the times, such environments are designed based on the experience of the system integrators, often leading to sub-optimal solutions. Accurate simulators of the robotic cell, accounting for the presence of the human as well, are available today and can be used in the pre-deployment. We propose an iterative optimization scheme where a digital model of the work-cell is updated based on a genetic algorithm. The methodology focuses on the layout optimization and task allocation, encoding both the problems simultaneously in the design variables handled by the genetic algorithm, while the task scheduling problem depends on the result of the upper-level one. The final solution balances conflicting objectives in the fitness function and is validated to show the impact of the objectives with respect to a baseline, which represents possible initial choices selected based on the human judgment.

Physics-based simulations and learning-based models are vital for complex robotics tasks like deformable object manipulation and liquid handling. However, these models often struggle with accuracy due to epistemic uncertainty or the sim-to-real gap. For instance, accurately pouring liquid from one container to another poses challenges, particularly when models are trained on limited demonstrations and may perform poorly in novel situations. This paper proposes an uncertainty-aware Monte Carlo Tree Search (MCTS) algorithm designed to mitigate these inaccuracies. By incorporating estimates of model uncertainty, the proposed MCTS strategy biases the search to-wards actions with lower predicted uncertainty. This approach enhances the reliability of planning under uncertain conditions. Applied to a liquid pouring task, our method demonstrates improved success rates even with models trained on minimal data, outperforming traditional methods and showcasing its potential for robust decision-making in robotics.

This article aims to design an assistive controller for physical Human-Robot Interaction (pHRI) based on Dynamic Cooperative Game Theory (DCGT). In particular, a distributed Model Predictive Control (dMPC) is formulated based on the DCGT principles (GT-dMPC). For proper implementation, one crucial piece of information regards human intention, which is defined as the desired trajectory that a human wants to follow over a finite rolling prediction horizon. To predict the desired human trajectory, a learning model is composed of cascaded Long-Short Term Memory (LSTM) and Fully Connected (FC) layers (RNN$+$FC). Iterative training and Transfer Learning (TL) techniques are proposed to adapt the model to different users. The behavior of the proposed GT-dMPC framework is thoroughly analyzed with simulations to understand its applicability and the tuning of its parameters for a pHRI assistive controller. Moreover, real-world experiments were carried out on a UR5 robotic arm equipped with a force sensor was installed. First, a brief validation of the RNN$+$FC model integrated with the GT-dMPC is proposed for the iterative procedure and the TL. Finally, an application scenario is proposed for co-manipulating two objects and comparing the obtained results with other controllers typically used in the pHRI. Results show that the proposed controller reduces the required force of the human in completing tasks, even in the presence of unknown and different loads and inertia. Moreover, the proposed controller allows for precise reaching of the target point and does not introduce any undesirable oscillations. Finally, a subjective questionnaire shows that the proposed controller is, in general, preferred by different users.Note to Practitioners—This work presents a method to design an assistive controller to help a human perform physically coupled shared tasks with a robot. The target applications of this work are co-handling tasks of large or heavy objects. Such tasks require two agents to be performed easily, and the proposed work aims to make the robot a companion for the human partner. The proposed approach also quickly adapts to new users or tasks, making it feasible for real production systems or daily scenarios. Another possible target application is the co-manipulating large flexible components such as carbon fiber plies. This application would require small modifications, particularly in how the force is exchanged. Some additional/different sensors should be used, such as vision to map object deformations with virtual forces. The present work does not directly consider these kinds of applications. Indeed, this work strictly relies on force measurements that are not reliable when dealing with flexible materials, at least in a compression state. Such an issue will be investigated in future works by using vision systems to measure a virtual force that allows this method to be applicable even in the case of flexible components.

The collaboration between humans and robots re-quires a paradigm shift not only in robot perception, reasoning, and action, but also in the design of the robotic cell. This paper proposes an optimization framework for designing collaborative robotics cells using a digital twin during the pre-deployment phase. This approach mitigates the limitations of experience-based sub-optimal designs by means of Bayesian optimization to find the optimal layout after a certain number of iterations. By integrating production KPIs into a black-box optimization frame-work, the digital twin supports data-driven decision-making, reduces the need for costly prototypes, and ensures continuous improvement thanks to the learning nature of the algorithm. The paper presents a case study with preliminary results that show how this methodology can be applied to obtain safer, more efficient, and adaptable human-robot collaborative environments.

This work demonstrates the utility of dual-arm robots with dual-wrist force-torque sensors in manipulating a Deformable Linear Object (DLO) within an unknown environment that imposes constraints on the DLO’s movement through contacts and fixtures. We propose a strategy to estimate the pose of unknown environmental contacts encountered during the manipulation of a DLO, classifying the induced constraints as unilateral, bilateral and fully constrained, exploiting the redundancy of force sensors. A semantic approach to define environmental constraints is introduced and incorporated into a graph-based model of the DLO. This model remains accurate as long as the DLO is under tension and is dynamically updated throughout the manipulation process, built by sequencing a set of primitives. The estimation strategy is validated through simulations and real-world experiments, demonstrating its potential in handling DLOs under various, possibly uncertain, constraints.

This paper tackles the complex task of routing elastic deformable linear objects (DLOs) characterized by considerable stiffness, such as cables or hoses, which are already constrained at both ends. Specifically, a single arm robot is controlled to slide along the unknown contour of the cable, performing collision-free contour following, and to insert specific DLO segments into intermediate known clips. The contour following motion is executed avoiding both collisions with static obstacles and excessive deformation of the manipulated DLO. In particular, the path is defined considering an artificial potential field that is updated after each sliding motion along the DLO. This field accounts for static obstacles, the local cable shape (reconstructed using tactile sensors on the gripper fingertips) and the estimation of the global DLO shape obtained from a dynamic model of the DLO, accounting for the constraints imposed by the clips and the gripper. The proposed method is experimentally validated on an industrial robot executing cable routing in several DLO configurations.

Today, intelligent robotic manufacturing systems are reshaping the production industry. Using robots as actuators, multi-source sensors for perception, and Artificial Intelligence (AI) as decision-making systems, they can perform routine manufacturing tasks, surpassing the capabilities of traditional hard-programmed Computer Numerical Control (CNC) machinery. One specific challenge in footwear manufacturing is sole deburring, traditionally done manually by skilled workers. This paper focuses on developing a robust path-planning pipeline, comprising vision-based and Learning from Demonstrations (LfD) modules for autonomous deburring of soles. The vision-based module exploits Deep Learning (DL) techniques to handle key challenges such as precise segmentation of different soles types across diverse scenarios despite potential occlusions. Additionally, a novel method for burrs identification has been developed leveraging image processing and optimization techniques. Determining the optimal cutting tool orientation during sole deburring relies on human experience. The LfD module aims to impart this knowledge to the robot from videos of expert demonstrations, requiring adaptability to every new incoming sole that needs deburring. Experimental results showcase the method’s performance and flexibility, underlining the potential to advance the field of the proposed approach.

Recent advancements in medical science and technology have led to a remarkable increase in the lifespan of the global elderly population. However, this demographic often struggles with mobility issues, leading to a sedentary lifestyle fueled by concerns over their physical capabilities. Traditional treadmill gait training, although beneficial, often becomes monotonous and lacks the real-world feedback necessary for engaging and effective rehabilitation. Addressing this gap, research into virtual walking systems utilizing avatar robots has gained traction. Despite the progress, several challenges remain where the systems prioritize visual feedback without considering the crucial need for alerting users to potential dangers and obstacles. This lack of comprehensive environmental awareness and feedback undermines both user engagement and safety. To address this problem, this paper proposes an algorithm that employs B-splines for precise free space detection, integrated with a safety stop mechanism for an avatar robot. This novel approach enhances user awareness of their surroundings through a sophisticated Graphic User Interface (GUI) that leverages Augmented Reality (AR) technology. By superimposing free space boundaries and warning messages directly onto a real-time camera feed, the system provides an intuitive and immersive navigation aid. The efficacy of our proposed GUI was rigorously tested across a series of realistic scenarios, comparing teleoperation control performance with and without the augmented interface. Our findings reveal that our GUI markedly enhances user safety and navigational effectiveness, fostering a deeper understanding of and interaction with the surrounding environment, thereby redefining user experience in virtual mobility assistance.

This paper proposes a set of tactile based skills to perform robotic cable routing operations for deformable linear objects (DLOs) characterized by considerable stiffness and constrained at both ends. In particular, tactile data are exploited to reconstruct the shape of the grasped portion of the DLO and to estimate the future local one. This information is exploited to obtain a grasping configuration aligned to the local shape of the DLO, starting from a rough initial grasping pose, and to follow the DLO's contour in the three-dimensional space. Taking into account the distance travelled along the arc length of the DLO, the robot can detect the cable segments that must be firmly grasped and inserted in intermediate clips, continuing then to slide along the contour until the next DLO's portion, that has to be clipped, is reached. The proposed skills are experimentally validated with an industrial robot on different DLOs in several configurations and on a cable routing use case.

This paper presents an effective approach to enable performance improvement in human-robot collaboration scenarios. The problem is tackled from the perspective of speed and separation monitoring principle, which stems from the recently instituted safety standard. The proposed approach attempts to seek for performance gains, measured by the speed-up of the production cycle, without compromising the safety constraints consistent with the standard. The approach is based on the notion of braking surface - an abstraction of the swept volume described by the manipulator during braking motion. We address two types of braking behavior: general and path-consistent. In both cases, the braking surface can be evaluated in a receding horizon manner. The robot velocity is continuously scaled such that, in case of a controlled stop, the corresponding volume spanned by the robot (braking surface) does not interfere with the surrounding obstacles. The approach is entirely kinematic and does not require the knowledge of the robot's dynamic model. Simulation study indicates that the pro-posed approach offers performance improvements compared to other state of the art methods. Moreover, the experiments demonstrate the real-time applicability of the method with the real robot in human-shared environment.

This paper deals with the challenging task of picking semi-deformable linear objects (SDLOs) from a bin. SDLOs are deformable elements, such as cables, joined to a rigid part as a connector. We propose a vision-based strategy to detect, classify and estimate the pose and the state (free or occluded) of connectors belonging to an unspecified number of SDLOs, arranged in an unknown configuration in the bin. The connectors can then be grasped and manipulated by a dual-arm robot through a set of manipulation primitives. In this way, a single SDLO can be extracted from the bin and laid on the worktable. A subsequent association between the connectors and the extracted SDLOs is performed, allowing to firmly grasp a SDLO at its ends to further manipulate it. The procedure is tested in bin picking operations with several kinds of SDLOs and is applied to a use case involving a collaborative wire harnesses assembly task.

This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a strawberry stem, using a robot tactile finger. The model is integrated into a Deep Functional Predictive Control (d-FPC) system to control the displacement of the stem on the tactile finger during pushes. Pushing an object with a robot finger along a desired trajectory in 3D is a highly nonlinear and complex physical robot interaction, especially when the object is not stably grasped. The proposed approach controls the stem movements on the tactile finger in a prediction horizon. The effectiveness of the proposed FPC is demonstrated in a series of tests involving a real robot pushing a strawberry in a cluster. The results indicate that the d-FPC controller can successfully control PRI in robotic manipulation tasks beyond the handling of strawberries. The proposed approach offers a promising direction for addressing the challenging PRI problem in robotic manipulation tasks.

This paper deals with safe human-robot collaboration in the context of speed and separation monitoring paradigm. The core of the approach is to continuously track the separation distance between the robot and the human. The robot speed is then adjusted according to the perceived distance so that it will be able to stop before eventually come into contact with the human. We present an approach that aims at maximizing the productivity of the robot, i.e., its speed, while keeping the prescribed safety requirements satisfied. The method is based on explicit representation of danger zones – regions around the robot, where safety requirements are violated. The motion is then generated such that the robot moves as fast as possible, while its danger zone still does not collide with human operators. The approach is validated within an experimental study. Note to Practitioners—This article was motivated by the problem of maximizing productivity of the robotic manipulator while ensuring the safety of human collaborator. The increase in productivity is achieved by a faster traversal of predefined paths without compromising the safety of the human, which is specifically defined by industrial standard. The approach requires limited knowledge on robot’s dynamical properties. More precisely, we only need the braking time as a “lumped” representation of robot’s inertia. The underlying optimization problem is conveniently resolved by introducing danger zones that allow for intuitive visualization and geometrical representation of the regions around the robot that must be avoided. On the other hand, the method assumes the representation of humans via typical geometric primitives, which can be obtained using of-the-shelf depth perception systems. The solution to the problem reduces to a repeated collision checking between danger zones and the human. Such an approach turns out to be suitable for real-time implementation due to availability of fast and efficient collision checking algorithms/libraries.

The perception and autonomous manipulation of clothes by robots is an ongoing research topic that is attracting a lot of contributions. We consider the application of handling garments for laundry in this work. A framework for loading a washing machine with clothes placed initially inside a box is presented. Our framework is created in a modular way to account for the sub-problems associated with the full process. We extend our grasping point estimation algorithm by finding multiple grasping points and defining a score to select one. Active contours segmentation is added to the algorithm as well for more robust clustering of the image. Model of the washing machine is used to create a motion plan for the robot to place the clothes inside the drum. A new module is added for detection of items fallen outside the drum so to plan corresponding corrective action. We use ROS, depth and 2D cameras and the Doosan A0509 robot for experiments.

This paper introduces a novel approach for whole-body motion planning and dynamic occlusion avoidance. The proposed approach reformulates the visibility constraint as a likelihood maximization of visibility probability. In this formulation, we augment the primary cost function of a whole-body model predictive control scheme through a relaxed log barrier function yielding a relaxed log-likelihood maximization formulation of visibility probability. The visibility probability is computed through a probabilistic shadow field that quantifies point light source occlusions. We provide the necessary algorithms to obtain such a field for both 2D and 3D cases. We demonstrate 2D implementations of this field in simulation and 3D implementations through real-time hardware experiments. We show that due to the linear complexity of our shadow field algorithm to the map size, we can achieve high update rates, which facilitates onboard execution on mobile platforms with limited computational power. Lastly, we evaluate the performance of the proposed MPC reformulation in simulation for a quadrupedal mobile manipulator.

In this work, we address two crucial issues that arise in the design of a human-robot collaborative station for the assembly of products: the optimal task allocation and the scheduling problem. We propose an offline method to solve in series the two mentioned issues, considering a static allocation and taking into account several features such as the minimization of postural discomfort, operation processing times, idle times and hence the total cycle time. Our methodology consists of a mixed approach that combines a capability-based method, where the agents' capabilities are tested against a list of predefined criteria, with optimization. In particular, we formulate a modified version of the Hungarian Algorithm to solve also unbalanced assignment problems, where the number of tasks is different from the number of agents. The scheduling policy is obtained by means of a Mixed Integer Linear Programming (MILP) formulation, with a multi-objective optimization. Moreover, the concepts of operation, assembly tree and precedence graph are formalized, since they represent the inputs to our method, together with the information on the workstation layout and on the selected kind of robot. Finally, the proposed solution is applied to a case study to define the optimal task allocation and scheduling for two different workstation layouts: the results are compared and the best layout is accordingly selected.

Dynamic Movement Primitives (DMPs) are a well-known tool for encoding robotic motions. Their popularity stems from invariance properties in time and space, the ability to describe complex coordinated motions in multiple degrees of freedom with a relatively small number of parameters, and the linearity in the parameters that describe the motion. The latter allows easily fitting a DMP to motions e.g. demonstrated by a human. DMPs are at their core second order autonomous differential equations. However, feedforward controls of robots with flexible joints are known to require reference trajectories up to the fourth derivative of position. Consequently, classical DMPs are mechanically not compatible with flexible joint robots. In this paper, we propose an extension of DMPs by introducing FlexDMPs. This concept retains the structural properties and benefits of classical DMPs but generates trajectories up to the fourth derivative that can theoretically be tracked ideally (i.e. with zero tracking error) by flexible joint robots. The concept is demonstrated on a high fidelity simulation model of an industrial robot and in experimental results on a collaborative manipulator.

The novel paradigm of collaborative automation, with machines and industrial robots that synergically share the same workspace with human workers, requires to rethink how activities are prioritized in order to account for possible variabilities in their durations. This article proposes a scheduling method for collaborative assembly tasks that allows to optimally plan assembly activities based on the knowledge acquired during runtime and so adapts to variations along the life cycle of a manufacturing process. The scheduler is based on time Petri nets and the output plan is optimized by minimizing the idle time of each agent. The experimental validation carried out on a realistic industrial use-case consisting of a small assembly line with two robots and a human operator confirms the effectiveness of the approach.

In manufacturing, kitting is the process of grouping separate items together to be supplied as one unit to the assembly line. This is a key logistic task, which is usually performed manually by human operators. However, picking objects from the warehouse implies a great repetitiveness in arm motion. Moreover, the weight and position of items may increase the physical strain and induce the development of work-related musculoskeletal disorders. The inclusion of a collaborative robot in the process may help to reduce the operator's effort and increase productivity. This paper introduces an online scheduling algorithm to guide the picking operations of the human and the robot. The proposed approach has been experimentally evaluated and compared with an offline scheduler, as well as with the baseline case of manual kitting.

In this paper we address variable admittance control for human-robot physical interaction in manual guidance applications. In the proposed solution, the parameters of the admittance filter can change not only as a function of the current state of motion (i.e. whether the human guiding the robot ia accelerating or decelerating) but also with reference to a predefined goal position. The human is in fact gently guided towards the goal along some curved paths, where the damping is conveniently scaled in order to accommodate the motion towards the goal position. The algorithm also allows the human to reach goals that he/she cannot directly see because for example the transported object is bulky and obstructs the worker view. The performance of the proposed controller are evaluated by means of point to point cooperative motions with multiple volunteers using an ABB IRB140 robot.

Prediction of the human behaviour is essential for allowing an efficient human-robot collaboration. This was confirmed recently showing how scheduling approaches can significantly increase the productivity of a robotic cell by planning the robotic actions in a way as much as possible compliant with the human predicted behaviour. This work proposes an innovative approach for human activity prediction, exploiting both a-priori information and knowledge revealed during operation. The resulting approach is proved to achieve good performance through both off-line simulated sequences and in a realistic co-assembly involving a human operator and a dual arm collaborative robot.

A crucial problem in human-robot collaboration is to achieve seamless coordination among the agents. Robots have to adapt to human behaviour, which is highly uncertain. In fact, humans can perform each task in many ways and with different speeds, occasional errors and short pauses. This paper offers a robust method to monitor the advancement of the current human activity in real-time in order to predict its duration. The algorithm learns online templates of new variants of the task and uses them as references for a Dynamic Time Warping-based algorithm. The proposed strategy has been tested within a realistic assembly task. Results show its ability to give accurate predictions also in case of peculiar variants, such as those associated with errors.

In a collaborative scenario, robots working side by side with humans might rely on vision sensors to monitor the activity of the other agent. When occlusions of the human body occur, both the safety of the cooperation and the performance of the team can be penalized, since the robot could receive incorrect information about the ongoing cooperation. In this work, we propose a novel particle filter algorithm that, by merging the data acquired through a RGB-D camera and a MR headset, estimates online the human wrist position. This algorithm allows to significantly reduce the uncertainty of the human pose estimation, in case of both static and dynamic occlusions. To this purpose, the proposed particle filter is integrated with a detailed virtual model of the real workspace. Moreover, additional constraints describing the boundaries of the motion of the human upper body are included in a virtualized framework. The results showed that the proposed technique entails significant improvements, determining a relevant reduction of the estimation error and of the uncertainty of the estimate.

In this paper, we approach the problem of ensuring safety requirements within human-robot collaborative scenarios. The safety requirements considered herein are consistent with the paradigm of speed and separation monitoring. In such a setup, safety guarantees for human operators usually imply limited robot velocities and/or significant distance margins, which in turn may have adverse effects regarding the productivity of the robot. In this paper, we propose a novel approach that minimally affects the productivity while being consistent with such a safety prescription. A comprehensive simulation study shows that our method outperforms the current state of the art algorithm.

This article presents a novel robust centralized controller for impedance control and reference tracking of redundant manipulators. The proposed approach takes advantage of the robustness properties of sliding mode control (SMC) and the prediction capabilities of model predictive control (MPC). SMC theory is employed to compensate unmodeled system dynamics and disturbances, ensuring accurate tracking and enforcement of a desired end-point impedance during interaction with the environment. Differently from other schemes, the sliding manifold is expressed directly in the task space and the approach is generalized to redundant manipulators by projection of the manifolds into joint space. Chattering attenuation is provided by a second-order integral sliding mode control law. These features are exploited by the MPC to guarantee motion and actuation constraint fulfillment based on the nominal feedback linearized robot model. A formal analysis of the control system is given along with the relevant proofs. The resulting model predictive sliding mode controller is able to cope with delays acting on the control input torque. The effectiveness of the approach is validated in simulation on a 4-DOF planar robot, and its viability on real platforms through experiments on a 7-DOF prototype ABB YuMi robot arm.

In this paper, the process of modelling and identification of a hydraulic actuator is discussed. In this framework a simple model based on the classical theory has been derived and a first experimental campaign has been performed on a test bench. These tests highlighted the presence of unmodeled phenomena (e.g. dead-zone, hysteresis, etc.), therefore a second and more extensive experimental campaign has been done. With the acquired knowledge an improved model has been developed and its parameters identified. Finally several experimental tests have been performed in order to validate the model.

The coexistence of humans and robots in the future production plants is one of the pillars of Industry 4.0. Humans and robots will collaborate to accomplish common tasks in order to mutually compensate their deficiencies. In recent years, many efforts have been spent to develop safe motion planning strategies, designed to prevent robots from injuring humans. Most of the previous techniques are classifiable as reactive, since the considered motion controllers impose some local corrective actions in order to dodge the space occupied by the human. In this paper, a proactive approach is adopted, optimizing robotic paths according to a prediction of the volume occupied by the human when collaborating with the robot. The validity of the approach is shown in a realistic use-case involving the collaboration of a human operator with a 7 degrees robotic arm, the ABB YuMi.

Letting humans and robots share a common space for collaboration is considered a consolidated practice. The trajectories followed by the robot must be safe for the human mate, especially when the robot holds dangerous tools or parts. At the same time, the productivity must be preserved, without imposing too restrictive limitations on the robot's movements. This article proposes the use of Gaussian Processes to predict the motion of an operator in a robotic cell, with the aim of controlling the robot speed and avoid collisions. An adaptive approach is proposed and the model for the human motion is persistently re-updated. The resulting approach will be demonstrated to be less conservative than previous ones, while at the same time to preserve the safety of the operator. Real experiments have been conducted on the 7 d.o.f. ABB YuMi robot.

Rapidly Random exploring Trees are popular algorithms in the field of motion planning. A feasible path connecting two different poses is found by incrementally building a tree data structure. They are powerful and flexible, but also computationally intense, requiring thousands of iterations before their termination. The aim of this article is to show the capabilities of MT-RRT, a general purpose library which exploits four different multithreading strategies to speed up the planning process of Rapidly Random exploring Trees. MT-RRT will be proved to significantly reduce the computation time on various benchmarks.

In collaborative robotics applications, human behaviour is a major source of uncertainty. Predicting the evolution of the current human activity might be beneficial to the effectiveness of task planning, as it enables a higher level of coordination of robot and human activities. This paper addresses the problem of monitoring the advancement of human tasks in real-time giving an estimate of their expected duration. The proposed method relies on dynamic time warping to align the current activity with a reference template. No training phase is required, as the prototypical execution is learnt online from previous instances of the same activity. The applicability and performance of the method within an industrial context have been verified on a realistic assembly task.

This paper presents a robust impedance shaping controller for teleoperation systems. An integral sliding mode control law (ISM) is employed together with standard robot inverse dynamics to reject disturbances and uncertainties acting on the robot model and obtain an ideal fully-decoupled system. Higher level optimization-based controllers are responsible for enforcing the desired end effector impedance on master and slave manipulators, as well as for solving possible kinematic redundancies and satisfying control constraints. A three-plus-one channel teleoperation architecture is proposed, with an in-depth analysis of its stability and transparency properties in presence of variable communication delays, based on Llewellyn' s absolute stability theorem. Impedance parameters tuning criteria are derived and the proposed scheme performance is compared in simulation with a time-domain passivity approach. The validation of the proposed controller is carried out on a ABB YuMi dual-arm redundant robot, with one arm employed as a master and the other one as a slave device.

Collaborative robotics refers to the cooperation between humans and machines and aims at improving productivity and at facilitating the worker in demanding tasks. The advantage of collaboration is to combine the superior cognitive and motor skills of the operator with the physical capabilities of the robots. This work presents a control strategy for the robotic manipulator to minimise the muscular fatigue of the human operator during the manipulation of bulky objects. The robot moves the workpiece so that the human is always operating close to his/her most natural and ergonomic posture. This way the risk of developing postures and movements inducing musculoskeletal disorders is minimised. A substantial reduction of the amplitude of the operator movements, without degrading the precision in fulfilling the task, has been registered in the experimental campaign.

It is widely agreed that future manufacturing environments will be populated by humans and robots sharing the same workspace. However, the real collaboration can be sporadic, especially in the case of assembly tasks, which might involve autonomous operations to be executed by either the robot or the human worker. In this scenario, it might be beneficial to predict the actions of the human in order to control the robot both safely and efficiently. In this paper, we propose a method to predict human activity patterns in order to early infer when a specific collaborative operation will be requested by the human and to allow the robot to perform alternative autonomous tasks in the meanwhile. The prediction algorithm is based on higher-order Markov chains and is experimentally verified in a realistic scenario involving a dual-arm robot employed in a small part collaborative assembly task.

Handling liquids with spilling avoidance is a topic of interest for a broad range of fields, both in industry and in service robotic applications. In this paper we present a new control architecture for motion planning of industrial robots, able to tackle the problem of liquid transfer with sloshing control. We do not focus on a complete sloshing suppression, but we show how to enforce an anti spilling constraint. This less conservative approach allows to impose higher accelerations, reducing motion time. A constraint-based approach, amenable to an Online implementation, has been developed. The proposed controller generates trajectories in real time, in order to follow a reference path, while being compliant to the spilling avoidance constraint. The approach has been validated on a 6 degree of freedom industrial ABB robot.

Collaborative robotics over the last few years has gained increasing interest in the industrial scenario. Co-bots can be equipped with vision sensors and cognitive software layers, allowing the robot to figure out human intentions. To make this level of perception possible, human pose estimation algorithms are required. Several techniques have been already proposed to tackle this problem, which however present some weaknesses in particular when occlusions occur. This work proposes an algorithm for human pose estimation in the situations of partial occlusion, based on particle filter techniques. We have proved its validity in a realistic human-robot coexistence scenario, where a human and a dual arm robot have to perform tasks in a shared workspace.

In human-robot collaboration, the robot is required to provide assistance to the user by facilitating task execution. However, due to stability requirements, a well-damped admittance behavior of the robot is necessary during interaction, thus inducing fatigue in the operator. While available schemes involve variable impedance controllers to mitigate this effect, here we propose an alternative approach entailing a proactive robot behavior that assists in the cooperative execution of trajectories towards desired goals, by estimating the user intention. To this end, we make use of Recurrent Neural Networks (RNNs) to predict and classify cooperative motions, on the basis of a set of predefined goals in the workspace and model-based generated data of human movements. Manual guidance validation experiments are conducted on a 6 d.o.f. ABB IRB140 industrial robot equipped with a force sensor.

In modern day industry, robots are indispensable for achieving high production rates and competitiveness. In small and medium scale enterprises, where the production may shift rapidly, it is vital to be able to reprogram robots quickly. Kinesthetic teaching, also known as lead-through programming (LTP), provides a fast approach for teaching a trajectory. In this approach, a trajectory is demonstrated by physical interaction with the robot, i.e., the user manually guides the manipulator. This paper presents a sensorless approach to LTP for redundant robots that eliminates the need for expensive force/torque sensors. The active implementation enhances the passive LTP by an admittance control in joint space based on the external forces applied by the user, estimated with a Kalman filter using the generalized momentum formulation. To improve the quality of the estimation and hence LTP, we use a dithering technique. The active LTP has been implemented on ABB YuMi robot and experimental comparison with an earlier passive LTP is presented.

Standard control design for robot implicit force control is a typical example of model-based regulator synthesis. This paper proposes a method to improve closed-loop performance of standard model-based controllers for robot implicit force control in terms of closed-loop model matching, between desired and achieved closed-loop behaviour. To this end, a data-driven controller design method, based on the Virtual Reference Feedback Tuning (VRFT) approach, is introduced. Advantages in terms of robustness with respect to unknown environment stiffness are discussed and demonstrated. The effectiveness of the proposed control strategy is experimentally validated on an industrial robot equipped with a force sensor.

The present paper exploits set invariance theory to address the problem of robot implicit force control in presence of stiffness uncertainty in the interaction model. A numerical approach is introduced to compute the invariance function for constraints with arbitrary relative degree. The method is then applied to robot force control, enhancing force regulation performance, in terms of steady state error and convergence speed, despite model mismatch and measurement noise. Its effectiveness is experimentally validated and compared to previous results of set invariance control on a hybrid force/position task performed with a 6 degrees of freedom industrial robot equipped with a force/torque sensor.

In this work a constraint-based control approach is proposed in order to perform a cap rotation task with a dual-arm robot. The method relies on the introduction of a robust specification for the constraint on the interaction force arising during the task, accounting for robot-environment contact model uncertainties, in addition to force measurement noise and surface uncertainties. Experiments have been performed on an ABB dual-arm prototype robot to validate the proposed approach in a cap assembly task, employing a model-based sensorless observer of the interaction forces.

Allowing a cobot to predict what the human operator is about to do can definitely enhance the effectiveness of human-robot collaboration. This paper addresses the problem of inferring the most likely reaching target of the human hand. The method allows the robot to promptly recognise the intention of the human to reach a certain position within the scene and can be thus used by the controller of the robot to take the optimal decision on what to do. A novel method based on Bayesian statistics has been developed in this work and its applicability in a realistic context has been verified within an industrial use case, consisting of a commercial collaborative robot and a human operator performing a collaborative assembly task.

Fast and sensorless peg-in-hole insertion is a challenging task for a robotic manipulator. In order to deal with the peg-in-hole insertion problem without any need of an external force/torque sensor, this paper proposes to actively accomplish compliance in the insertion task relying on an admittance based control. This is combined with a real-time trajectory generator, by means of constraint based optimization, where a model-based sensorless observer of interaction forces is exploited. Experiments have been performed on an ABB dual-arm 7-DOF lightweight prototype robot to validate the proposed approach, with an insertion speed comparable to human manual execution and in presence of geometric uncertainty.

Although force control algorithms have been studied for three decades, this technology is not largely exploited in industry yet. The present paper proposes a position-based adaptive force control strategy, that relies on a novel method for the on line estimation of the environment stiffness. The control design is targeted to industrial controller structures and it is theoretically proven to be robust to time varying estimation errors of the environment stiffness and joint friction disturbances. The estimation algorithm succeeds in identifying the environment stiffness even in presence of geometrical irregularities of the contact surface during motion. The identification and control approaches are experimentally validated on an industrial robot equipped with a force sensor.

Computer vision algorithms for object localization and tracking are largely used in industry and academic research. Nonetheless, the performance evaluation of these algorithms is still an open problem, because the common systems used as ground truth are expensive and often not available in industry or research labs. In this paper we propose a method to evaluate the performance of a visual odometry algorithm using an industrial robot. The parameters required for the comparison are estimated by reducing the problem to a Quadratically Constrained Quadratic Program. The method is applied to an experimental set up composed of a six degrees of freedom industrial robot and a commercial monocular camera.

This paper addresses time-optimal path-constrained trajectory planning. Given a geometric path for a manipulator, this paper focuses on the selection of the time law along the path. This law minimizes the time required to complete the path and at the same time is consistent with constraints, both at kinematic and dynamic levels. To obtain the optimal law a decision algorithm for the acceleration along the path has been developed. Remarkably, the algorithm is amenable to online implementation, thus allowing for path replanning. An experimental validation on an ABB IRB140 robot is shown.

Classical control approaches to robot force control have been extensively addressed by research in the last decades and are now considered a paradigm when dealing with force control for industrial robots. With this respect, the present paper exploits the capability of state-of-the-art Quadratic Programming (QP) solvers to specify a simple and intuitive constraint-based optimization strategy aiming at improving closed-loop performance of a classical force controller, such as the implicit force control with pure integral action for a position-controlled manipulator in contact with a compliant environment. The effectiveness of the proposed control strategy is experimentally validated on an industrial robot equipped with a force sensor.

Despite the availability in the literature of several constraint-based motion generation algorithms, modest attention has been paid to their robustness with respect to noise, and more in general, to unstructured uncertainties. Especially in the case of sensor-related constraints, the envisaged robustness properties are clearly crucial to enforce the correct and expected behaviour of these algorithms. This paper contributes with a method to explicitly account for different sources of uncertainty. We also suggest a computational efficient way to consistently modify the constraint specification in order to obtain such robustness. An experimental verification on a visual aided grasping task, where visibility of the object is to be maintained, enlightens the benefits of the proposed approach in terms of achieving the desired robustness.

New paradigms in industrial robotics no longer require physical separation between robotic manipulators and humans. Moreover, in order to optimize production, humans and robots are expected to collaborate to some extent. In this scenario, involving a shared environment between humans and robots, common motion generation algorithms might turn out to be inadequate for this purpose. This paper proposes a kinematic control strategy which enforces safety, while maintaining the maximum level of productivity of the robot. The resulting motion of the (possibly redundant) robot is obtained as an output of an optimization-based real-time algorithm in which safety is regarded as a hard constraint to be satisfied. The methodology is experimentally validated on a dual-arm concept robot with 7-DOF per arm performing a manipulation task.

To perform many common industrial robotic tasks, e.g. deburring a work-piece, in small and medium size companies where a model of the work-piece may not be available, building a geometrical model of how to perform the task from a data set of human demonstrations is highly demanded. In many cases, however, the human demonstrations may be sub-optimal and noisy solutions to the problem of performing a task. For example, an expert may not completely remove the burrs that result in deburring residuals on the work-piece. Hence, we present an iterative algorithm to estimate a noise-free geometrical model of a work-piece from a given dataset of profiles with deburring residuals. In a case study, we compare the profiles obtained with the proposed method, nonlinear principal component analysis and Gaussian mixture model/Gaussian mixture regression. The comparison illustrates the effectiveness of the proposed method, in terms of accuracy, to compute a noise-free profile model of a task.

In this work, we propose a constraint-based algorithm for combined trajectory generation and kinematic control for robotic manipulators. The main feature of the algorithm is to ease robot programming, shifting from an imperative paradigm, in which task constraints are semantically and uniquely mapped into a suitable end-effector velocity profile, towards a declarative motion programming, where such constraints are turned by the controller into motion commands only at run-time: The system embeds the capability of handling real-time events, such as updated sensor readings, with reduced pre-programmed control logics. An experimental case study based on a 7-DOF robot demonstrates the effectiveness of the approach.

In this paper, a controller based on constrained optimization for tracking problems in mobile manipulation is presented. A Model Predictive Control problem is set and solved online, allowing to deal with dynamic scenarios and unforeseen events. Besides acceleration, velocity and position constraints, collision avoidance constraints for the mobile base and the arm and Field-of-View constraints have been enforced and extended over the prediction horizon. Navigation performance has been improved by including an additional goal, derived from the classical vortex field approach, to the MPC problem. An experimental validation on a KUKA youBot mobile manipulator has been carried out, showing the online applicability of the presented approach.

In order to make humans feeling comfortable when working with robots, it is necessary for robots to be as much as possible “human-like” in both their appearance and movements. In redundant manipulators, it is possible to use the additional degrees of freedom in order to make the robot motion more human-like, thus increasing the quality of the human-robot interaction. In this work, a redundancy resolution method to address this issue is presented. Such a method considers the human musculoskeletal system and a biomechanical model of the human upper limbs in order to define a strategy to solve the redundancy for a dual-arm anthropomorphic manipulator as a human would do, then making the robot able to both perform the prescribed task and to assume human-like postures.

Enabling human-robot collaboration raises new challenges in safety-oriented robot design and control. Indices that quantitatively describe human injury due to a human-robot collision are needed to propose suitable pre-collision control strategies. This paper presents a novel model-based injury index built on the concept of dissipated kinetic energy in a potential inelastic impact. This quantity represents the fracture energy lost when a human-robot collision occurs, modeling both clamped and unclamped cases. It depends on the robot reflected mass and velocity in the impact direction. The proposed index is expressed in analytical form suitable to be integrated in a constraint-based pre-collision control strategy. The exploited control architecture allows to perform a given robot task while simultaneously bounding our injury assessment and minimizing the reflected mass in the direction of the impact. Experiments have been performed on a lightweight robot ABB FRIDA to validate the proposed injury index as well as the pre-collision control strategy.

In an industiral scenario the capability to detect and track human workers entering a robotic cell represents a fundamental requirement to enable safe and efficient human-robot cooperation. This paper proposes a new approach to the problem of Human Detection and Tracking based on low-cost commercial RGB surveillance cameras, image warping techniques, computer vision algorithms, efficient data structures such as k-dimensional trees and particle filtering. Results of several validation experiments are presented.

In the past few years the need for more flexibility in industrial production has implied a growing attention towards scenarios where humans work directly in touch with robots. In order to allow safe human-robot interaction, a methodology to evaluate the severity of an impact between a human worker and an industrial robot, based on related work on injury knowledge in human-robot contacts and relying on information coming from different exteroceptive sensors, has been developed in this paper. On the basis of this severity evaluation, the robot controller enforces a suitable safety-oriented strategy, ranging from on-path speed reduction to task-consistent evasive motion and protective stop. The safety evaluation methodology has been implemented in a dedicated software component, integrated with a video surveillance system and with the real time robot controller to obtain a complete HW/SW architecture named “Safety Controller”. The system has been validated on an ABB IRB140 robot.

This paper addresses the problem of collision avoidance in human-robot interaction. To this end, we introduce the concept of kinetostatic safety field, a novel safety assessment about the risk in the vicinity of a rigid body (including a robot link or a human body part). The safety field depends on the position and velocity of the body but it is also influenced by its real shape and size. Since all the computation can be performed in closed form, the safety field is suitable for real-time applications. Moreover, we present a safety-oriented control strategy for redundant manipulators, based on safety field and developed entirely on the kinematic level, where the kinematic redundancy is exploited for simultaneous task performance and collision avoidance, such as self-collision avoidance and human-robot coexistence. The proposed control strategy is validated through experiments performed on ABB's FRIDA dual arm robot.

The main purpose of this paper is to present an implicit force control scheme for 6 DoF industrial robots, whose compliance model takes into account both joint and link elasticities. The system composed by the robot and the environment is modeled by means of a simple equivalent elasticity at the end effector. A method to avoid limit cycles due to friction during force control applications is then proposed. The compliance model and the force control are experimentally validated on a industrial robot equipped with a force sensor.

Industrial robots are today separated from human workers by means of safety barriers, that protect humans from the risk of collisions. This separation has a clear negative influence on diffusion of robotic technology in shopfloors. On the other hand the removal of protective barriers gives rise to safety issues, that can be addressed with a combination of approaches, including sensor based reactive control. In this paper a distributed proximity sensor, to be mounted on the links of the manipulator, is presented. The optimal placement of the spots of such sensor is discussed, taking into account detection capabilities and safety enhancement. Experiments developed on an ABB IRB 140 robot using off-the-shelf infrared distance sensors as spots are presented.

Tight human-robot interaction and collaboration will characterize future robot tasks. Robot working environments will be increasingly unstructured, as safety barriers will be removed to allow a continuous cooperation of robotic and human workers. Such a working scenario calls for novel safety systems capable of combining productivity with workers' safety. In this paper, a method for the definition of a task-consistent collision avoidance safety strategy is presented. A classification of task constraints based on relevance for task completion is introduced. Control of task constraints enforcement is performed through a state machine. A template for such state machine is proposed. Experimental validation of the proposed safety system on a dual-arm industrial robot prototype is presented.

In order to improve production flexibility, it is widely agreed that future working environments will be populated by both humans and robot manipulators, sharing the same workspace. This scenario introduces a series of safety issues which are uncommon in industrial settings where physical separation of robot areas is typically enforced. While several approaches for safe human-robot interaction exist, none of them can be easily integrated with production constraints. This paper discusses the composition of safety constraints with production ones. An algorithm is derived in order to maximize productivity, while guaranteeing a safe separation distance of the robot from the human. Experimental results showing the effectiveness of the approach in a typical industrial setting are also discussed.

This paper presents a synergistic approach to danger assessment and safety-oriented control of articulated robots that are based on a quantity called danger field. This quantity captures the state of the robot as a whole and indicates how dangerous the current posture and velocity of the robot are to the objects in the environment. The field itself is invariant with respect to objects around the robot and can be computed in any given point of the robot's workspace using measurements from the proprioceptive sensors. Furthermore, the danger field can be expressed in the closed form, which enables its fast computation. Apart from being a pure safety assessment, the danger field provides a natural prelude to safety-oriented control strategy. Namely, the information about the danger field can easily be fed back to shape standard control schemes in order to make the motion of the robot safer to the environment. The proposed method is validated through simulations and experiments.

Dual-arm robotic systems have been intensively studied in the literature. However, in industrial robotics, the resolution of the kinematic redundancy allowed by the coordinated manipulation task is still an open issue. In fact, typical proprietary industrial robotic controllers do not allow the programmer to modify the inverse kinematics algorithm, and thus to solve redundancy following any specified criterion. In this paper a method to enforce an arbitrary redundancy resolution criterion on top of an industrial robot controller is discussed and applied to the execution of a coordinated manipulation task. The extra degrees of freedom are used to maximize the dynamic manipulability measure in order to reduce the needed torque. Simulations and experimental results achieved on an ABB IRC 5 industrial robot controller are presented.

This paper presents a new control law for robotic manipulators in unstructured environments which guarantees the achievement of the goal position without incurring in local minima. The passivity of the closed-loop system renders this control scheme well-suited for human-robot coexistence, especially when the robot is supposed to share its workspace with humans. The given control law has been implemented and experimentally tested in a realistic scenario, demonstrating the effectiveness in driving the robot to a given configuration in a cluttered environment without any offline planning phase.

Redundant robotic manipulators under kinematic control may exhibit unpredictable behaviors at the joint level. When the end effector describes a closed trajectory, the joint angles may not return to their initial values. Likewise, final configuration in the joint space may depend on the trajectory that is followed by the end effector. In this paper, a complete parameterization of holonomic redundancy resolution techniques that avoid these problems is proposed. The flexibility of the proposed approach is discussed. In particular, it is shown that the selection of the redundancy resolution criterion is totally decoupled from the implementation of a closed-loop inverse kinematics (CLIK) algorithm. Any user-defined redundancy resolution criterion can, thus, be enforced. Potentialities of this new methodology are experimentally verified on an industrial robot in a case study where functional redundancy occurs and is applied in simulation on a 7-degree-of-freedom (7-DOF) anthropomorphic manipulator.

Research in the field of human kinematic analysis has gained interest in recent years and has fostered new ideas and expectations. Next generation manipulators are expected to resemble a human-like behaviour at kinematic level, in order to avoid any unease or discomfort (like fear or shock) to the nearby humans. In this work, a kinematic experimental approach to study and synthesize the motion of the human arm is presented. In particular, the proposed scenario will be used to study how humans exploit the kinematic redundancy of their arms, for a future use in a robotic controller. A simple, yet accurate, method for human-like redundancy resolution in robotic manipulators is developed and verified.

A drilling task requires a mechanism with five degrees of freedom, in order to achieve the correct position and orientation of the drilling tool. When performed with a standard 6-axes industrial robot, this task leaves an extra degree of freedom that can be exploited in order to achieve any additional criterion. Unfortunately, typical industrial robotic control architectures do not allow the user to modify the inverse kinematics algorithm, and thus to solve task redundancy following any specified criterion. In this paper a method to enforce an arbitrary redundancy resolution criterion on top of an industrial robot controller is discussed and applied to the execution of a drilling task. The extra degree of freedom is used to perform a torque-effective drilling. Experimental results achieved on the ABB IRB 140 industrial robot are presented.

Removing the safety fences that separate humans and robots, to allow for an effective human-robot interaction, requires innovative safety control systems. An advanced functionality of a safety controller might be to detect the presence of humans entering the robotic cell and to estimate their intention, in order to enforce an effective safety reaction. This paper proposes advanced algorithms for cognitive vision, empowered by a dynamic model of human walking, for detection and tracking of humans. Intention estimation is then addressed as the problem of predicting online the trajectory of the human, given a set of trajectories of walking people learnt offline using an unsupervised classification algorithm. Results of the application of the presented approach to a large number of experiments on volunteers are also reported.

A novel method to obtain safe paths for robotic manipulators is presented. It emulates the probabilistic roadmap planner by trying to establish a collision-free path that connects the start and the goal configuration via samples that belong to a quasi-random, low discrepancy sequence. The path construction is guided by a search algorithm with a heuristic function that includes a suitably tailored safety estimation. The measure of safety is based on the danger field - a recently proposed safety assessment for human-robot interaction. Thus, the planner provides not only collision-free paths, but strives for safer ones. In order to decrease the expected number of collision checks, we propose a novel method for testing whether local paths are collision-free or not. The method combines the standard binary collision checking with the concept of bubbles of free configuration space.

Redundant robotic manipulators under kinematic control may exhibit unpredictable behaviours at joint level. When the end-effector describes a closed trajectory, the joint angles may not return to their initial values and final configuration in the joint space may depend on the trajectory followed by the end-effector. In this paper, a complete parameterization of holonomic local control strategies that avoid these problems is proposed. Only a basis of the null-space of the Jacobian matrix is required in order to design all the possible holonomic control strategies. The effectiveness of the proposed approach is verified on a simple case study and on a real industrial manipulator.

This paper presents a novel method for evaluating the danger within the environment of a robot manipulator. It is based on the introduced concept of kinetostatic danger field, a quantity that captures the complete state of the robot - its configuration and velocity. The field itself is invariant with respect to objects around the robot and can be computed in any given point of the workspace using measurements from the proprioceptive sensors. Moreover, all the computation can be performed in closed form, yielding compact algebraic expressions that allow for real time applications. The danger field is not only a meaningful indicator about the risk in the vicinity of the robot, but can also be fed back within control skills that implement some well known safety strategies like collision avoidance and virtual impedance control, provided that some environment perception is available in order to determine the points where the field should be computed. Kinematic redundancy for simultaneous task performance and danger minimization can be exploited. The methodology described in the paper is supported with simulation results.

We propose a novel method of path planning for robotic manipulators that is based on the tree expansion via bubbles of free configuration space. The algorithm is designed to yield collision-free paths that also tend to minimize a certain danger criterion. This is achieved by embedding a suitably tailored heuristics within the algorithm. For that purpose we use a recently proposed safety assessment based on the concept of the danger field - an easily computable quantity that captures the complete kinematic behavior of the manipulator. Under the assumption that a systematic graph search technique dictates the tree growth, we prove the algorithm's completeness.

Robust controllers for robot manipulators ensure stability of the closed-loop system, even if only partial knowledge of the dynamic model of the manipulator is available. Existing derivations of robust-control laws, while guaranteeing the stability result, present an undesired dependence of the robust-control term on the gains of the controller for the nominal system. This dependence forces larger robust-control terms when the nominal control gains are large. Based on a structured representation of the model uncertainty, this paper proposes a derivation of the robust-control law, where these limitations are removed. Experimental results on the COMAU SMART 3S industrial robot in a 3-degree-of-freedom (DOF) configuration confirm the advantages of the proposed controller.

Robust controllers for robot manipulators ensure stability properties of the closed loop system, even if only partial knowledge of the dynamic model of the manipulator is available. Existing derivations of robust control laws, while guaranteeing the stability result, present an undesired interaction between the gains of the controller of the nominal system and the robust control term. Based on a structured representation of the model uncertainty, this paper presents a derivation of the robust control law where these limitations are removed. A case study is discussed to show the benefits of the proposed approach. New insight in the robust control problem for more general mechanical systems might arise from structuring the model uncertainty as proposed in this paper.

Visual servoing of eye-in-hand flexible manipulators is addressed in this paper. Dynamic effects of both the rigid and the flexible motion of the manipulator are fully taken into account in a control solution where the two-time scale nature of the problem is exploited. The visual information is used in the "slow" subsystem for a task-space-oriented control law, where computationally expensive operations, such as inverse and time derivative of the Jacobian, are avoided. A constructive proof of stability of this control scheme, based on Lyapunov theory, is also presented. The effectiveness of the proposed controller is shown by means of a numerical simulation concerning a trajectory tracking problem. Some experimental results finally demonstrate the precision enhancement achieved by the proposed algorithm on a single-link flexible manipulator

Torque sensors in robotics are used in those applications where high precision positioning of the end effector is required. Technological difficulties often arise in the mechanical design as well as in the placement of the sensor. However, the use itself of the torque sensor for feedback control, in all those cases where elasticity of the transmission is an issue, is maybe not completely understood. The main goal of this paper is to put the control problem in a clear and correct setting, both from the steady state and from the dynamic standpoints. The role of the zero in the origin of the complex plane in the transfer function from motor torque to output torque is thoroughly discussed. It is also shown, through analysis and simulations on a detailed model of the space robotic manipulator DEXARM, that a correct design of the torque loop allows to overcome some performance limitations in the control of the load position, that arise in the case of simple positional control.

In this paper, an integral friction model is proposed, defining separately the Dahl, the Stribeck and the microviscous effects. The model is based on an integral closed form solution of the Dahl model. It is shown by simulation that the model is consistent with the LuGre model, while being computationally more efficient. A limit solution of the proposed model avoids the nonphysical drifts, a drawback of the LuGre model recently addressed in the literature.

The stability properties of an operational space controller applied to a space robotic manipulator are investigated in this paper. It is shown that a major potential source of instability is the drive train dynamics in the joints of the manipulator. An analysis carried out with all normalized parameters shows that a nominally stable position control system may turn out to be unstable in practice. Problems arise also for force control, if proportional controllers are used. The analysis is supported by simulations obtained on a detailed dynamic model of the SPIDER arm, to be used in a space program on the International Space Station.

An impedance controller for industrial manipulators is presented in this paper. Special attention has been paid to all the aspects that qualify an industrial robot, including decentralized proportional-integral-derivative position control, torsional flexibility, and friction at the joints. A discussion of a single-degree-of-freedom (DOF) case is first presented. The selection of the impedance parameters is based on the behavior in contact and makes reference to a new design parameter, the compliance bandwidth. The single-DOF analysis is then used in the design of the impedance controller for a complete manipulator. A rotational impedance based on a geometrically consistent representation of the orientation is used. Several experimental results obtained on a 6-DOF industrial robot, equipped with a force/torque sensor, are presented. The experiments show the effectiveness of the proposed approach in several conditions, including contact with an extremely stiff surface and noncontact/contact transition.

A new impedance controller is presented. Special attention has been paid to all those aspects that qualify an industrial robot, including decentralized PID positional control, torsional flexibility and friction at the joints. Discussion of a single degree of freedom case proved to be valuable in the design of the impedance controller for a complete manipulator. Several experimental results obtained on a 6 degrees-of-freedom industrial robot equipped with a force/torque sensor, are given.

Permanent magnet AC motors generate parasitic torque pulsations, owing to several electromagnetic phenomena. Excitation of the mechanical resonances on the load side is generally the worst consequence of these disturbances. Speed oscillations arise which may dramatically limit the performances in high precision applications. In the paper a control scheme which autonomously identifies the parameters of the disturbance model, through simple closed loop motion experiments, is outlined. Experiments carried out on an industrial robot demonstrate the effectiveness of the control scheme in suppressing oscillations, both on the motor and on the load side.

This paper describes the results of an experimental research on the disturbances acting on the measured force during contact motion of industrial robots. Two phenomena have been found to be mainly responsible for these disturbances, namely the arm structural elasticity due to the torsional flexibility of the joints and the torque ripple of the brushless motors. The structural elasticity is characterized by a resonance frequency function of the arm configuration, while the torque ripple has a principal harmonic of frequency proportional to the motor velocity. Though obtained on a particular experimental setup, these results should be of general interest, since both joint flexibility and torque ripple are common drawbacks of most industrial robots.

With the goal of filling the gap between theory and industrial applications, an implicit hybrid control scheme is proposed in this paper, designed to fit as much as possible the conventional industrial robot control architecture. The dynamic effects due to joint compliance, which are a major source of performance degradation in industrial robots, are fully taken into account. The scheme is based on a task description particularly suited for a direct integration in conventional robot programming tools and aims at exerting the force control action without affecting the trajectory tracking. Only the differential kinematic model (Jacobian) of the robot is needed in the design of the force control law, while the force control loop is charged with rejecting dynamic disturbances due to motion. A thorough experimental validation of the strategy, both in terms of force regulation and trajectory tracking capabilities, is discussed, based on experiments performed on an industrial robot, endowed with a six-axis wrist force/torque sensor and with a laser distance sensor.

In the original paper by J.K. Mills (ibid., vol.8, p. 119-26, 1992), a singularly perturbed model of an elastic-joint robotic manipulator has been presented. The author shows that the expression of the boundary layer subsystem is incorrect. The correct expression is given and it is shown that, for a control system designed for rigid robots, the uniform exponential stability of the boundary layer system, required for the application of Tikhonov's theorem, can still be proven.

Distributed flexibility of the links is a severe obstacle for the endpoint position control of lightweight manipulators. In order to accomplish with satisfactory performance certain tasks involving a controlled interaction of the tip of the robot with the worksurfaces, a combined control of the motion and the contact forces can provide some advantages. This paper presents a general and systematic model of a flexible robot interacting with a rigid environment. A force/position control scheme based on this model is also introduced. Results obtained simulating the constrained motion of an existing 2-DOF flexible arm are given.

PID control is the most widespread technique for the control of industrial robot arms. However, the adoption of PID control is not adequately supported by a theoretical basis, since the results presented in the literature are of dubious interpretation and difficult, when not impossible, to verify. Motivated by this lack of theoretical support, this paper presents a novel proof for the stability of rigid robot arms controlled by PID algorithms: the proof is based on a model of the robot where the nominal decoupled linear part is emphasized. The main result consists in a simple condition between the exponential stability degree of the nominal closed loop system and the parameters of a bound on the nonlinear terms in the dynamic model of the mechanical manipulator. Some considerations are also worked out on the relations between the eigenvalues of the nominal system and the extension of the stability region. The theoretical results are finally verified on a simple two DOF example.