1,720,967 research outputs found
Combined joint-cartesian mapping for simultaneous shape and precision teleoperation of anthropomorphic robotic hands
Full text linkThere are many applications involving robotic hands in which teleoperation-based approaches are preferred to autonomous solutions. The main reason is that cognitive skills of human operators are desirable in some task scenarios, in order to overcome limitations of robotic hands abilities in dealing with unstructured environments and/or unpredetermined requirements. In particular, in this work we focus on the use of anthropomorphic grasping devices and, specifically, on their teleoperation based on movements of the human operator's hand (the master hand.) Indeed, the mapping of human hand configurations to an anthropomorphic robotic hand (the slave device) is still an open problem, because of the presence of dissimilar kinematics between master and slave that produce shape and/or Cartesian errors - as addressed within our study. In this work, we propose a novel algorithm that combines joint and Cartesian mappings in order to enhance the preservation of both finger shapes and fingertip positions during the teleoperation of the robotic hand. In particular, a transition between the joint and Cartesian mappings is realized on the basis of the distance between the fingertip of the master hands' thumb and the opposite fingers, in which the mapping of the thumb fingertip is specifically addressed. The result of the testing of the algorithm with a ROS-based simulator of a commercially available robotic hand is reported, showing the effectiveness of the proposed mapping
Combining Unsupervised Muscle Co-Contraction Estimation with Bio-Feedback Allows Augmented Kinesthetic Teaching
Full text linkNowadays, an increasingly diversification of products and production lines would largely benefit from intuitive and multimodal robot teaching strategies. The present article proposes an augmented kinesthetic teaching system, which is based on surface electromyographic (sEMG) measurements from the operator forearm. Specifically, sEMG signals are used for minimal-training unsupervised estimation of forearm's muscles co-contraction level. In this way, also exploiting a vibrotactile bio-feedback, we evaluate the ability of operators in stiffening their hand-during kinesthetic teaching-in order to modulate the estimated level of muscle co-contraction to (i) match target levels and (ii) command the opening/closing of a gripper, i.e. in exploiting their sEMG signals for effective augmented robot kinesthetic teaching tasks. Experiments were carried out involving ten subjects in two different kind of experimental sessions, in order to test both co-contraction modulation abilities, and actual usage of the co-contraction for programming robot functionalities during kinesthetic teaching. The obtained results provide positive outcomes on the intuitiveness and effectiveness of the proposed system and approach, paving the way to a new generation of advanced teaching by demonstration interfaces
Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information
Full text linkTwo sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side and the mapping method of human hand movements (primary hand) on the robotic hand (target hand). In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. Several approaches have been presented to deal with this problem, which is still unresolved in general. In this work, we exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We therefore report on two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint–Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The general mapping approach is presented, and the two realizations are tested. In order to report the results of an evaluation of the proposed mappings for multiple robotic hand kinematic structures (both industrial grippers and anthropomorphic hands, with a variable number of fingers), a simulative evaluation was performed
Mapping Finger Motions on Anthropomorphic Robotic Hands: Two Realizations of a Hybrid Joint-Cartesian Approach Based on Spatial In-Hand Information
No full textIn literature, two sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side, and the mapping method of human hand movements on the robotic hand. In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires to preserve the Cartesian position of the fingertips and the finger shapes given by the joint values. Several approaches have been presented to deal with this problem, which is still unresolved in general. In this work, we propose an approach for combining joint and Cartesian mapping in a single method. More specifically, we exploit the spatial information available in-hand, in particular, related to the thumb-fingers relative position. In this way, it is possible to perform both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We therefore report for two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint-Cartesian transition is defined on the areas of the workspace in which thumb and finger fingertips can get in contact
A Vision-based Shared Autonomy Framework for Deformable Linear Objects Manipulation
Full text linkThe manipulation of Deformable Linear Objects (DLOs) is a critical process in which the introduction of automation and autonomous systems is still marginal. In this paper, a novel teleoperation framework is proposed in which an intuitive manipulation of DLOs is achieved by means of visual aid. The proposed system could be deployed for manipulating DLOs in hazardous scenarios or for simplifying robot teaching tasks by allowing a faster demonstration time. Experiments are performed involving several subjects and their feedback is collected by means of a survey. The results show that the proposed teleoperation framework simplifies DLOs manipulation and reduces the task completion time by 20% on average
Experimental Evaluation Of Intuitive Programming Of Robot Interaction Behaviour During Kinesthetic Teaching Using sEMG And Cutaneous Feedback
Full text linkModern applications related to service or production can nowadays benefit from the introduction of collaborative robots already available on the market and endowed with several advanced features such as precise torque control or safe physical interaction with operators. More importantly, collaborative robots allow operators to teach end-effector trajectories by means of physical interaction – known as kinesthetic teaching – which is one of the most intuitive programming-by-demonstration techniques. However, important functionalities provided by modern collaborative robots, like the possibility of performing smooth interactions, cannot be programmed intuitively with the available framework of kinesthetic teaching. In the present study, we propose and experimentally evaluate a robot programming framework for the simultaneous teaching of both trajectories by means of kinesthetic teaching, and robot interaction behavior by means of impedance shaping along the trajectory exploiting a wearable interface. Specifically, the wearable interface is designed to not affect the free motion of the operator, necessary to perform kinesthetic teaching, and it is based on the usage of surface electromyography (sEMG) and vibrotactile stimulation. In this way, we propose an intuitive robot programming framework for an offline robot trajectory and interaction behavior programming, according to which the operator will be able to plan interactions with the environment and humans. In this article, we report a preliminary experimental evaluation of the proposed system, in which an operator will teach a 7-degrees-of-freedom manipulator the execution of a task on a robotic wiring test-bed. In the experiment, the programming of requested compliance levels during the kinesthetic teaching of a trajectory is performed, and the reported results show that the provided wearable interface is successfully exploited by the operator. Finally, the experiment also demonstrates that the offline intuitive programming of trajectories and impedance levels can be exploited online for human-robot co-work
Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation
Full text linkTrajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion
Exploiting In-Hand Knowledge in Hybrid Joint-Cartesian Mapping for Anthropomorphic Robotic Hands
Full text linkReplication of human hand motions on anthropomorphic robotic hands is typically treated in literature as the combination of two sub-problems: the measurement of human hand motions, and the mapping of such motions on the robotic hand. In this letter we focus on the second one. Different approaches have been proposed to deal with this problem, but none of them preserves both master finger shapes and fingertip positions on the robotic hand, i.e. ensuring predictability and natural motion for the teleoperator. In this article, we propose a novel hybrid approach that combines both joint and Cartesian mappings in a single solution. In particular, we exploit the a priori, in-hand information related to the areas of the workspace in which thumb and finger fingertips can get in contact. This allows to define, for each finger, a zone of transition from joint to Cartesian mapping. As a consequence, both hand shape during volar grasps and correctness of the fingertip positions for precision grasps are preserved, despite the master-slave kinematic dissimilarities. The proposed hybrid mapping is presented and experimentally evaluated both in simulation and with a real slave anthropomorphic robotic hand
Integration of a Multi-Camera Vision System and Admittance Control for Robotic Industrial Depalletizing
No full textThis work addresses the task of robot depalletizing by means of a mobile manipulator, taking into account the problem of localizing the boxes to be removed from the pallet and a manipulation strategy that allows to pull the boxes without lifting them with the robot arm. The depalletizing task is of particular interest in the industrial scenario in order to increase efficiency, flexibility and economic affordability of automatic warehouses.The proposed solution makes use of a multi-sensor vision system and a force-controlled collaborative robot in order to detect the boxes on the pallet and to control the robot interaction with the boxes to be removed. The vision system comprises a fixed 3D Time-of-flight camera and an eye-in-hand 2D camera. Preliminary experimental results performed on a laboratory setup with a fixed-based robotic manipulator are reported to show the effectiveness of the perception and control system
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