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In robot-aided surgery, during the execution of typical bimanual procedures such as dissection, surgical tools can collide and create serious damage to the robot or tissues. The da Vinci robot is one of the most advanced and certainly the most widespread robotic system dedicated to minimally invasive surgery. Although the procedures performed by da Vinci-like surgical robots are teleoperated, potential collisions between surgical tools are a very sensitive issue declared by surgeons. Shared control techniques based on Virtual Fixtures (VF) can be an effective way to help the surgeon prevent tools collision. This letter presents a surgical tools collision avoidance method that uses Forbidden Region Virtual Fixtures. Tool clashing is avoided by rendering a repulsive force to the surgeon. To ensure the correct definition of the VF, a marker-less tool tracking method, using deep neural network architecture for tool segmentation, is adopted. The use of direct kinematics for tools collision avoidance is affected by tools position error introduced by robot component elasticity during tools interaction with the environment. On the other hand, kinematics information can help in case of occlusions of the camera. Therefore, this work proposes an Extended Kalman Filter (EKF) for pose estimation which ensures a more robust application of VF on the tool, coupling vision and kinematics information. The entire pipeline is tested in different tasks using the da Vinci Research Kit system.

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 paper 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 (SNS) 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., Accepted to Transactions on Robotics (T-RO). Final version in preparaton. 20 pages, 19 figures

Mathematical modeling of soft robots is complicated by the description of the continuously deformable three-dimensional shape that they assume when subjected to external loads. In this article we present the deformation space formulation for soft robots dynamics, developed using a finite element approach. Starting from the Cosserat rod theory formulated on a Lie group, we derive a discrete model using a helicoidal shape function for the spatial discretization and a geometric scheme for the time integration of the robot shape configuration. The main motivation behind this work is the derivation of accurate and computational efficient models for soft robots. The model takes into account bending, torsion, shear, and axial deformations due to general external loading conditions. It is validated through analytic and experimental benchmark. The results demonstrate that the model matches experimental positions with errors

Nowadays haptic devices have lots of applications in virtual reality systems. While using a haptic device, one of the main requirements is the stable behavior of the system. An unstable behavior of a haptic device may damage itself and even may hurt its operator. Stability of haptic devices in the presence of inevitable time delay in addition to a suitable zero-order hold is studied in the presented paper, using two different methods. Both presented methods are based on Lyapunov-Krazuvskii functional. In the first method, a model transform is performed to determine the stability boundary, while the second approach is based on Free Weighing Matrices (FWMs). Delay-dependent stability criteria are determined by solving Linear Matrix Inequalities (LMIs). Results of these two methods are compared with each other and verified by simulations as well as experiments on a KUKA Light Weight Robot 4 (LWR4). It is concluded that using free weighing matrices leads to more unknown parameters and needs more calculation, but its results are less conservative.

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.

With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications.

International audience; Today, physical Human-Robot Interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, Aerial Physical Interaction (APhI) 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 that makes pHRI safe. A controller, inspired by the literature on flexible manipulators, 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 non-idealities like 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.

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.

The COVID-19 pandemic is forcing a rethink in robotics. In the form it is known today, robotics has been the prerogative of a broad community of insiders. But now, in the wreckage left behind by COVID-19, a new era is beginning. What does it hold? During the pandemic, increasing numbers of people had manifested the hope that robotics might bring novel solutions. And this interest has emerged beyond the usual boundaries of the experts or technology enthusiasts. This provides an opportunity to reinforce the community of people involved in the process of innovation. By involving citizens, the community becomes more comprehensive (that is, plural and diverse). This broadening will involve more practical knowledge and therefore produce better robots of many shapes and functions. If progress is possible in the industry, why not in the hospitals, shopping malls, restaurants, hotels, and schools? What is more, the approach endorsed by the Italian robotics community during the lockdown has established a new cooperation among those who labor with robots, and the professionals who work in hospitals, which is bound to last a long time. As a major impact, this experience will enable an improvement on science's relationship with and for society. This may entail a further shift: to value more scientific knowledge and scientific literacy.

International audience; Sharing the control of a robotic system with an autonomous controller allows a human to reduce his/her cognitive and physical workload during the execution of a task. In recent years, the development of inference and learning techniques has widened the spectrum of applications of shared control (SC) approaches, leading to robotic systems that are capable of seamless adaptation of their autonomy level. In this perspective, shared autonomy (SA) can be defined as the design paradigm that enables this adapting behavior of the robotic system. This letter collects the latest results achieved by the research community in the field of SC and SA with special emphasis on physical human-robot interaction (pHRI). Architectures and methods developed for SC and SA are discussed throughout the paper, highlighting the key aspects of each methodology. A discussion about open issues concludes this letter.

Mathematical modeling of hybrid soft robots is complicated by the description of the complex shape that they undergone when subject to actuation and external loads. It might be noticed that several approaches have been used so far in robotics, and the problem is not yet fully solved. This short paper aims at presenting an overview of modeling and simulation approaches for soft robots based on finite element methods. Benefits and perspectives of future directions are also discussed.

Manipulation in contrast to grasping is a trajectorial task that needs to use dexterous hands. Improving the dexterity of robot hands, increases the controller complexity and thus requires to use the concept of postural synergies. Inspired from postural synergies, this research proposes a new framework called kernelized synergies that focuses on the re-usability of same subspace for precision grasping and dexterous manipulation. In this work, the computed subspace of postural synergies is parameterized by kernelized movement primitives to preserve its grasping and manipulation characteristics and allows its reuse for new objects. The grasp stability of proposed framework is assessed with the force closure quality index, as a cost function. For performance evaluation, the proposed framework is initially tested on two different simulated robot hand models using the Syngrasp toolbox and experimentally, four complex grasping and manipulation tasks are performed and reported. Results confirm the hand agnostic approach of proposed framework and its generalization to distinct objects irrespective of their dimensions.

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.

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.

During robot-aided surgical interventions, the surgeon can be benefitted from the application of virtual fixtures (VFs). Though very effective, this technique is very often not practicable in unstructured surgical environments. In order to comply with the environmental deformation, both the VF geometry and the constraint enforcement parameters need to be online defined/adapted. This letter proposes a strategy for an effective use of VF assistance in minimally invasive robotic surgical tasks. An online VF generation technique based on the interaction force measurements is presented. Pose and geometry adaptations of the VF are considered. Passivity of the overall system is guaranteed by using energy tanks passivity-based control. The proposed method is validated through experiments on the da Vinci Research Kit.

The goal of this article is to disseminate the planning and control strategies for robotic nonprehensile manipulation results achieved so far with the Robotic Dynamic Manipulation (RoDyMan) project. The goal of the project is to advance the state of the art of nonprehensile dynamic manipulation of rigid and deformable objects to enhance the possibility of employing robots in anthropic environments. RoDyMan project's final demonstration will be acting as an autonomous pizza maker. This article highlights the lessons learned and paves the way toward critical discussions and future research.

In laparoscopic minimally invasive robotic surgery, a teleoperated robot is interposed between the patient and the surgeon. Despite the robot aid, the manipulation capabilities of surgical instruments are far from those of the human hand. In this letter, we want to make a step forward toward robotic solutions that can improve manipulation capabilities of the surgical instruments. A new concept of needle-driver tool is presented, which takes inspiration from the human hand model. The idea is to modify a standard laparoscopic tool by introducing an additional degree of freedom, which allows in-hand reorientation of the suturing needle. A 3D printed prototype has been built to validate the tool design. The improved manipulation capabilities have been assessed quantitatively by evaluating a weighted dexterity index along a single stitch trajectory. Moreover, a comparison between our tool and a standard needle driver has been done in terms of time required for the execution of a complete suturing sequence.

Purpose The purpose of this paper is to present a comparison study of cooperative object manipulation control algorithms. To this end, a full comprehensive survey of the existing control algorithms in this field is presented. Design/methodology/approach Cooperative manipulation occurs when manipulators are mechanically coupled to the object being manipulated, and the manipulators may not be treated as an isolated system. The most important and basic impedance control (IC) strategies for an assumed cooperative object manipulation task are the Augmented Object Model (AOM) control and the multiple impedance control (MIC) which are found based on the IC, where the former is designed based on the object movement, and the latter is designed based on the whole robot movement. Thus, the basis of these two algorithms are fully studied. Findings The results are fully analyzed, and it is practically verified that the MIC algorithm has the better performance. In fact, the results reveal that the MIC system could successfully perform the object manipulation task, as opposed to the AOM controller: for the same controller gains, the MIC strategy showed better performance than the AOM strategy. This means that because there is no control on the robot base with the AOM algorithm, the object manipulation task cannot be satisfactorily performed whenever the desired path is not within the robot work space. On the other hand, with the MIC algorithm, satisfactory object manipulation is achieved for a mobile robotic system in which the robot base, the manipulator endpoints and the manipulated object shall be moved. Practical implications A simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs. Originality/value The basis of these two aspects or these two algorithms is fully studied and compared which is the foundation of this paper. For this purpose, a case study is considered, in which a space free-flying robotic system, which contains two 2-degrees of freedom planar cooperative manipulators, is simulated to manipulate an object using the above control strategies. The system also includes a rotating antenna and camera as its third and fourth arm. Finally, a simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

Integrating a robotic system into the depalletizing process of a supermarket demands a high level of autonomy, based on strong perceptive capabilities. This letter presents a system for detection, recognition, and localization of heterogeneous cases in a depalletizing robotic cell, using a single RGB-D camera. Such a system integrates apriori information on the content of the pallet with data from the RGB-D camera, exploiting a sequence of 2D and 3D model-based computer-vision algorithms. The effectiveness of the proposed methodology is assessed in an experiment where multiple cases and pallet configurations are considered. Finally, a complete depalletizing process is shown.

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.

This paper addresses the problem of 3D registration of outdoor environments combining heterogeneous datasets acquired from unmanned aerial (UAV) and ground (UGV) vehicles. In order to solve this problem, we introduced a novel Scale Invariant Registration Method (SIRM) for semi-automated registration of 3D point clouds. The method is capable of coping with an arbitrary scale difference between the point clouds, without any information about their initial position and orientation. Furthermore, the SIRM does not require having a good initial overlap between two heterogeneous datasets. Our method strikes an elegant balance between the existing fully automated 3D registration systems (which often fail in the case of heterogeneous datasets and harsh outdoor environments) and fully manual registration approaches (which are labour-intensive). The experimental validation of the proposed 3D heterogeneous registration system was performed on large-scale datasets representing unstructured and harsh outdoor environments, demonstrating the potential and benefits of the proposed 3D registration system in real-world environments.

Although substantial progresses have been made in building prosthetic hands, lack of mechanical robustness still restrains wide adoption of robotic hand prostheses. This paper presents the design and evaluation of the MERO hand, which is a MEchanically RObust anthropomorphic prosthetic hand using novel COmpliant Rolling-contact Element (CORE) joints. The proposed CORE joint, which has a simple structure, exhibits compliance in multiple directions. Its structural parameters were designed, to form underactuated finger designs that can perform adaptive finger motion during grasping. Experiments showed that the hand could withstand severe disarticulation and violent impact. The hand could perform various adaptive grasps and also in-hand manipulation, suggesting that the proposed design might be a viable solution for robust prosthetic hand.

International audience; 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 hap-tic 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.

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, fatigue 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 over the suturing trajectory. We evaluate the cost and its gradient on the needle grasping manifold that allows us to obtain the optimal grasping pose for joint-limit and singularity free movements of the needle during suturing. Then, we compute force cues that are applied to the Master Tool Manipulator (MTM) of the da Vinci to guide the operator towards the optimal grasp. As such, our system helps the operator to choose a grasping configuration allowing the robot to avoid joint limits and singularities during post-grasp suturing movements. We show the effectiveness of our 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.

Background Keyhole surgery is characterized by loss of dexterity of surgeon's movements because of the limited workspace, nonintuitive motor skills of the surgical systems, and loss of tactile sensation that may lead to tissue damage and bad execution of the tasks. Methods In this paper, a three-fingered underactuated miniature tool for robot-aided laparoscopic surgery is presented. The design is conceived to realize a closed-hand configuration allowing the insertion of the tool into the abdominal cavity through the trocar in one step and to reach different grasping as well as pushing/holding configurations once in the cavity. Results Aiming to replicate human hand dexterity and versatility, different solutions for the kinematic structure of the hand are analyzed using quality indices to evaluate the manipulability and stability of the grasp. Furthermore, a first prototype of fingertip force sensor based on fiber Bragg grating (FBG) technology has been realized and tested. The design choices of the prototype are described and discussed with the aid of experiments. Conclusions The whole concept and the need for such anthropomorphic tool are discussed with surgeons to highlight constraints and potentials in surgical tasks. The feedback by expert surgeons is used to provide specifications and improvements to the kinematics and mechanical design. The investigations of different designs allow identifying the optimal solution to improve grasping and manipulation capabilities. The tests on FBG sensors led to the conclusion that this technology guarantees good performance and can be a good solution for applications in surgical robotics.

In this work, the problem of grasping novel objects with an anthropomorphic hand-arm robotic system is considered. In particular, an algorithm for learning stable grasps of unknown objects has been developed based on an object shape classification and on the extraction of some associated geometric features. Different concepts, coming from fields such as machine learning, computer vision, and robot control, have been integrated together in a modular framework to achieve a flexible solution suitable for different applications. The results presented in this work confirm that the combination of learning from demonstration and reinforcement learning can be an interesting solution for complex tasks, such as grasping with anthropomorphic hands. The imitation learning provides the robot with a good base to start the learning process that improves its abilities through trial and error. The learning process occurs in a reduced dimension subspace learned upstream from human observation during typical grasping tasks. Furthermore, the integration of a synergy-based control module allows reducing the number of trials owing to the synergistic approach.

A method to reconfigure in a nonprehensile way the pose (position and orientation) of a sphere rolling on a plate is proposed in this letter. The nonholonomic nature of the task is first solved at a planning level, where a geometric technique is employed to derive a Cartesian path to steer the sphere towards the arbitrarily desired pose. Then, an integral passivity-based control is designed to track the planned trajectory. The port-Hamiltonian formalism is employed to model the whole dynamics. Two approaches to move the plate are addressed in this paper, showing that only one of them allows the full controllability of the system. A humanoid-like robot is employed to bolster the proposed method experimentally.

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.

A new closed-form equation for stability analysis of a haptic device is presented using frequency response function analysis via continuous time model of the system. Desired impedance of the virtual environment (VE), time delay, and zero-order hold are considered, beside the continuous model of the haptic device. The developed equations provide critical virtual damping and stiffness of the VE versus the mass and viscous friction of the haptic device, sampling time, and time delay. Unlike prior work in this field, the developed equations are valid without any limitation on the values of the time delay and virtual damping. It is shown that they cover available well-known equations in literature for special cases of small values of virtual damping and time delay. The resulting equations are of practical usefulness in many fields, such as surgery simulation for avoiding instability during virtual tool interaction with high-stiffness VEs. The proposed analytical derivation can also be used for studying the effect of operator, sensors, actuator dynamics, and velocity filtering. Simulation and experimental results on the KUKA light weight robot show that the proposed criterion can accurately predict the stability boundaries.

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.

In this work we present a V-REP simulator for the da Vinci Research Kit (dVRK). The simulator contains a full robot kinematic model and integrated sensors. A robot operating system (ROS) interface has been created for easy use and development of common software components. Moreover, several scenes have been implemented to illustrate the performance and potentiality of the developed simulator. Both the simulator and the example scenes are available to the community as an open source software.

MANUFACTURING, Edited by Galip Ulsoy Manufacturing Systems and Their Design Principles, M.G. Mehrabi, A.G. Ulsoy, and Y.Koren Computer-Aided Process Planning For Machining, Derek Yip-Hoi Discrete Event Control of Manufacturing Systems, D.M. Tilbury and P.P. Khargonekar Machine Tool Vibrations, Yusuf Altintas Machine Tool Monitoring and Control, Kourosh Danai Process Monitoring and Control of Machining Operations, Robert G. Landers, A. Galip Ulso, and Richard J. Furness Forming Processes: Monitoring and Control, David E. Hardt Assembly and Welding Processes and their Monitoring and Control, S. Jack Hu and Elijah Kannatey-Asibu, Jr. Control of Polymer Processing, David Kazmer and Kourosh Danai Precision Manufacturing, Thomas R. Kurfess VIBRATION CONTROL, Edited by Nejat Olgac Active Damping of Large Trusses, Preumont, F. Bossens, and N. Loix Semi-Active Suspension Systems, Nader Jalili Semi-Active Suspension Systems II, Michael Valasek and Willi Kortum Active Vibration and Absorption and Delayed Feedback Tuning, Nejat Olgac and Martin Hosek Vibration Suppression Utilizing Piezoelectric Networks, K.W. Wang Vibration Reduction via the Boundary Control Method, Siddharth P. Nagarkatti and Darren M. Dawson DYNAMICS AND CONTROL OF AEROSPACE SYSTEMS: AN INTRODUCTION TO TENSEGRITY STRUCTURES, Edited by Robert E. Skelton An Introduction to the Mechanics of Tensegrity Structures, Robert E. Skelton, J. William Helton, Rajesh Adhikari, Jean-Paul P. Pinaud, and Waileung Chan The Dynamics of the Class 1 Shell Tensegrity Structure, Robert E. Skelton and Jean-Paul Pinaud, D.L. Mingori ROBOTICS, Edited by Miomir Vukobratovic Robot Kinematics, Bruno Siciliano Robot Dynamics ,Miomir Vukobratovic and V. Potkonjak Actuators and Computer-Aided Design of Robots, Miomir Vukobratovic, V. Potkonjak, Masaharu Takano, and Kenji Inoue Control of Robots, Miomir Vukobratovic and D. Stokic Control of Robotic Systems in Contact Tasks, Miomir Vukobratovic andD. Surdilovic Intelligent Soft-Computing Techniques in Robotics, D. Katic and B. Karan Teleoperation, Telerobotics, A. Bejczy Mobile Robotic Systems, Nenad M. Kircanski Humanoid Robots, Miomir Vukobratovic , Branislav Borovac, D. Stokic, and D. Surdilovic Present State and Future Trends in Robot Applications, M. Hagele and Dieter Schraft

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, the postural synergy configuration subspace given by the fundamental eigengrasps of the UB Hand IV is derived from experiments, and a simplified synergy-based strategy for planning grasps is proposed. The objectives of this work are, on one side, the simplification of grasp synthesis in a configuration space of reduced dimensions and, on the other side, the attainment of a human-like behavior for anthropomorphic hands. A reference set of 36 hand postures, chosen with the goal of covering the entire grasp variety referring to a recently proposed taxonomy, has been considered for the evaluation of the hand synergies. With the aim of defining general properties of the three predominant synergies, the reference set of hand postures has been applied to other two anthropomorphic robot hands, and the obtained synergies have been compared with the ones computed considering the UB Hand IV kinematics. Moreover, the synthesis of new grasps, not contained in the reference set of hand postures, has also been achieved by means of the synergy subspace. The experiments carried out demonstrate that the adopted synergy-based planning method works efficiently for all the considered grasps even if not contained in the reference set used for the evaluation of the postural synergies. © 2013 Elsevier B.V. All rights reserved.

A new method to catch a thrown ball with a robot endowed with an eye-in-hand monocular visual system integrated into a gripper is proposed. As soon as the thrown ball is recognized by the visual system, the camera carried by the robot end-effector is forced to follow a baseline in the space so as to acquire an initial dataset of visual measurements from several points of view, providing a first estimate of the catching point through a linear estimation algorithm. Hereafter, additional measurements are acquired to constantly refine the previous estimate by exploiting a nonlinear estimation algorithm. During the robot trajectory, the translational components of the camera are controlled in such a way as to follow the planned path to intercept the ball, while the rotational components are forced to keep the ball into the field of view. Experimental results performed on a common industrial robotic system prove the effectiveness of the presented solution.

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.

This paper presents a nonlinear least squares approach to deal with dual-hand robotic ball juggling. The task considers the repetitive batting (throwing and catching in a single collision) of a ball between two paddles/hands in a nonprehensile way. In detail, assuming to measure the trajectory of the ball, by solving a sequence of nonlinear minimization problems through a least squares method, the configuration of the paddles at the next impact is computed online to juggle the ball between the hands. Afterwards, an optimal trajectory for the paddles is planned in SE(3). The proposed approach is evaluated on a semi-humanoid robot with 21 degrees of freedom. Numerical tests show the smoothness of the planned trajectories and the precision of the proposed juggling algorithm.

This paper presents a method for tracking a 3D textureless object which undergoes elastic deformations, using the point cloud data provided by an RGB-D sensor and in real-time. This solution is expected to be useful for enhanced manipulation of humanoid robotic systems, especially in the case of pizza dough to be ideally manipulated by a pizza chef robot. Our tracking 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 system has been evaluated on synthetic and real data, and by integrating it into manipulation experiments on the RoDyMan1 http://www.rodyman.eu/. The research leading to these results has been supported by the RoDyMan project, which has received funding from the European Research Council(FP7 IDEAS) under Advanced Grant agreement number 320992. The authors are solely responsible for its content. It does not represent the opinion of the European Community and the Community is not responsible for any use that might be made of the information contained therein. humanoid robotic platform.

This paper presents a novel method to optimize the motion of a paddle within a nonprehensile batting task. The proposed approach shows that it is possible to online predict the impact time and the configuration of the paddle, in terms of its linear velocity and orientation, to re-direct a ball towards a desired location, imposing also a desired spin during the free flight. While exploiting the hybrid dynamics of the task during the minimization process, the obtained position and orientation paths are planned by minimizing the acceleration function of the paddle in SE(3). The batting paths are then tracked by a semi-humanoid robot through a closed-loop kinematic inversion. Numerical tests are implemented to compare different metrics to define the optimal impact time.

Robots! Robots on Mars and in oceans, in hospitals and homes, in factories and schools; robots fighting fires, making goods and products, saving time and lives. Robots today are making a considerable impact on many aspects of modern life, from industrial manufacturing to healthcare, transportation, and exploration of the deep space and sea. Tomorrow, robots will be as pervasive and personal as today’s personal computers. This chapter retraces the evolution of this fascinating field from the ancient to the modern times through a number of milestones: from the first automated mechanical artifact (1400 BC) through the establishment of the robot concept in the 1920s, the realization of the first industrial robots in the 1960s, the definition of robotics science and the birth of an active research community in the 1980s, and the expansion towards the challenges of the human world of the twenty-first century. Robotics in its long journey has inspired this handbook which is organized in three layers: the foundations of robotics science; the consolidated methodologies and technologies of robot design, sensing and perception, manipulation and interfaces, mobile and distributed robotics; the advanced applications of field and service robotics, as well as of human-centered and life-like robotics.

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.

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.

Purpose – To present an open architecture for real‐time sensory feedback control of a dual‐arm industrial robotic cell. The setup is composed of two industrial robot manipulators equipped with force/torque sensors and pneumatic grippers, a vision system and a belt conveyor.Design/methodology/approach – 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.Findings – The new control architecture 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.Originality/value – 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 the role of redundancy in Cartesian impedance control of a robotic arm for the execution of tasks in co-manipulation with humans is considered. In particular, the problem of stability is experimentally investigated. When a human operator guides the robot through direct physical interaction, it is desirable to have a compliant behaviour at the end effector according to a decoupled impedance dynamics. In order to achieve a desired impedance behaviour, the robot’s dynamics has to be suitably reshaped by the controller. Moreover, the stability of the coupled human-robot system should be guaranteed for any value of the impedance parameters within a prescribed region. If the robot is kinematically or functionally redundant, also the redundant degrees of freedom can be used to modify the robot dynamics. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we compare two different strategies to solve redundancy and we show that, when redundancy is exploited to ensure a decoupled apparent inertia at the end effector, the stability region in the parameter space becomes larger. Thus, better performance can be achieved by using, e.g., variable impedance control laws tuned to human intentions.

International audience; 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.

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 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.

In this paper the problem of achieving a cooperative behavior in a dual-arm robot system is addressed. A control strategy is conceived in which one robot is position controlled and devoted to task execution, whereas a suitable compliance is conferred to the end effector of the other robot to cope with unavoidable misalignment between the two arms and task planning inaccuracies. A modular control structure is adopted that allows the selection of the proper operating mode for each robot, depending on the task requirements. The proposed approach is experimentally tested in two different tasks involving the two robots in the laboratory setup. First, a parts-mating task of peg-in-hole type is executed; the robot carrying the peg is position controlled, whereas the robot holding the hollow part is controlled to behave as a mechanical impedance. Then, a pressure-forming task is executed, in which a disk-shaped tool is required to align with a flat surface while exerting a desired pressure; in this case, the robot carrying the disk is position controlled, whereas the robot holding the surface is force controlled. (C) 2001 John Wiley & Sons, Inc.