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Safe and efficient collaboration among multiple robots in unstructuredenvironments is increasingly critical in the era of Industry 4.0. However,achieving robust and autonomous collaboration among humans and other robotsrequires modern robotic systems to have effective proximity perception andreactive obstacle avoidance. In this paper, we propose a novel methodology forreactive whole-body obstacle avoidance that ensures conflict-free robot-robotinteractions even in dynamic environment. Unlike existing approaches based onJacobian-type, sampling based or geometric techniques, our methodologyleverages the latest deep learning advances and topological manifold learning,enabling it to be readily generalized to other problem settings with highcomputing efficiency and fast graph traversal techniques. Our approach allows arobotic arm to proactively avoid obstacles of arbitrary 3D shapes withoutdirect contact, a significant improvement over traditional industrial cobotsettings. To validate our approach, we implement it on a robotic platformconsisting of dual 6-DoF robotic arms with optimized proximity sensorplacement, capable of working collaboratively with varying levels ofinterference. Specifically, one arm performs reactive whole-body obstacleavoidance while achieving its pre-determined objective, while the other armemulates the presence of a human collaborator with independent and potentiallyadversarial movements. Our methodology provides a robust and effective solutionfor safe human-robot collaboration in non-stationary environments.

SERA: Safe and Efficient Reactive Obstacle Avoidance for Collaborative Robotic Planning in Unstructured Environments