Predicting the post-impact velocity of a robotic arm

Starting from the recorded dynamic response of a 7DOF torque-controlled robot while intentionally impacting a rigid surface, we investigate the possibility of predicting the post-impact robot velocity from the ante-impact velocity and configuration. The velocity prediction is obtained by means of an impact map, derived using the framework of nonsmooth mechanics, that makes use of the known rigid-body robot model and the assumption of a frictionless inelastic impact. The main contribution is proposing a methodology that allows for a meaningful quantitative comparison between the recorded post-impact data, that exhibits a damped oscillatory response after the impact, and the post-impact velocity prediction derived via the rigid-body robot model, that presents no oscillations. The results of this approach are promising and the recorded impact data (18 experiments) is made publicly available, together with the numerical routines employed to generate the quantitative comparison, to further stimulate research in this field

Predicting the Post-Impact Velocity of a Robotic Arm via Rigid Multibody Models: an Experimental Study

Accurate post-impact velocity predictions are essential in developing impact-aware manipulation strategies for robots, where contacts are intentionally established at non-zero speed mimicking human manipulation abilities in dynamic grasping and pushing of objects. Starting from the recorded dynamic response of a 7DOF torque-controlled robot that intentionally impacts a rigid surface, we investigate the possibility and accuracy of predicting the post-impact robot velocity from the pre-impact velocity and impact configuration. The velocity prediction is obtained by means of an impact map, derived using the framework of nonsmooth mechanics, that makes use of the known rigid-body robot model and the assumption of a frictionless inelastic impact.The main contribution is proposing a methodology that allows for a meaningful quantitative comparison between the recorded post-impact data, that exhibits a damped oscillatory response after the impact, and the post-impact velocity prediction derived via the readily available rigid-body robot model, that presents no oscillations and that is the one typically obtained via mainstream robot simulator software. The results of this new approach are promising in terms of prediction accuracy and thus relevant for the growing field of impact-aware robot control. The recorded impact data (18 experiments) is made publicly available, together with the numerical routines employed to generate the quantitative comparison, to further stimulate interest/research in this field

Predicting the Post-Impact Velocity of a Robotic Arm via Rigid Multibody Models: an Experimental Study

International audienceAccurate post-impact velocity predictions are essential in developing impact-aware manipulation strategies for robots, where contacts are intentionally established at non-zero speed mimicking human manipulation abilities in dynamic grasping and pushing of objects. Starting from the recorded dynamic response of a 7DOF torque-controlled robot that intentionally impacts a rigid surface, we investigate the possibility and accuracy of predicting the post-impact robot velocity from the pre-impact velocity and impact configuration. The velocity prediction is obtained by means of an impact map, derived using the framework of nonsmooth mechanics, that makes use of the known rigid-body robot model and the assumption of a frictionless inelastic impact.The main contribution is proposing a methodology that allows for a meaningful quantitative comparison between the recorded post-impact data, that exhibits a damped oscillatory response after the impact, and the post-impact velocity prediction derived via the readily available rigid-body robot model, that presents no oscillations and that is the one typically obtained via mainstream robot simulator software. The results of this new approach are promising in terms of prediction accuracy and thus relevant for the growing field of impact-aware robot control. The recorded impact data (18 experiments) is made publicly available, together with the numerical routines employed to generate the quantitative comparison, to further stimulate interest/research in this field

Dynamic Capture Using a Traplike Soft Gripper With Stiffness Anisotropy

Dynamic capture is a common skill that humans have practiced extensively but is a challenging task for robots in which sensing, planning, and actuation must be tightly coordinated to deal with targets of diverse shapes, sizes, and velocity. In particular, the impact force may cause serious damage to a rigid gripper and even its carrier, e.g., a robotic arm. Existing soft grippers suffer from low speed and force to actively respond to capturing dynamic targets. In this article, we propose a soft gripper capable of efficient capture of dynamic targets, taking inspiration from the biological structures of multitentacled animals or plants. The presented gripper uses a cluster of tentacles to achieve an omnidirectional envelope and high tolerance to dynamic target during the capturing process. In addition, a stiffness anisotropy property is implemented to the tentacle structure to form a “trap” making it easy for the targets to enter yet difficult to escape. We also present an analytical model for the tentacle structure to describe its deformation during the collision with a target. In experiments, we construct a robotic prototype and demonstrate its ability to capture dynamic targets