Reactive Whole-Body Obstacle Avoidance for Collision-Free Human-Robot Interaction with Topological Manifold Learning. (arXiv:2203.13821v1 [cs.RO])

Safe collaboration between human and robots in a common unstructured
environment becomes increasingly critical with the emergence of Industry 4.0.
However, to accomplish safe, robust, and autonomous collaboration with humans,
modern robotic systems must possess not only effective proximity perception but
also reactive obstacle avoidance. Unfortunately, for most robotic systems,
their shared working environment with human operators may not always be static,
instead often dynamically varying and being constantly cluttered with
unanticipated obstacles or hazards. In this paper, we present a novel
methodology of reactive whole-body obstacle avoidance methodology that
safeguards the human who enters the robot’s workspace through achieving
conflict-free human-robot interactions even in a dynamically constrained
environment. Unlike existing Jacobian-type or geometric approaches, our
proposed methodology leverages both topological manifold learning and latest
deep learning advances, therefore can not only be readily generalized into
other unseen problem settings, but also achieve high computing efficiency with
concrete theoretical basis. Furthermore, in sharp contrast to the industrial
cobot setting, our methodology allows a robotic arm to proactively avoid
obstacles of arbitrary 3D shapes without direct contacting. To solidify our
study, we implement and validate our methodology with a robotic platform
consisting of dual 6-DoF robotic arms with optimized proximity sensor
placement, both of which are capable of working collaboratively with different
levels of interference. Specifically, one arm will perform reactive whole-body
obstacle avoidance while achieving its pre-determined objective, with the other
arm emulating the presence of a human collaborator with independent and
potentially adversary movements.



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