Anytime informed path re-planning and optimization for human-robot collaboration

In human-robot collaboration, the robot is required to properly react to the operator's movements to avoid collisions and interference. In this abstract, we present a path re-planning strategy to change the robot path online when a human is on the robot's way. The approach exploits a set of pre-computed paths to speed up the search for a new feasible solution and to improve the trajectory's readability. In addition, the algorithm continuously optimizes the current path in an anytime fashion to deal with strict computing time requirements. Experimental tests demonstrate the effectiveness of the proposed framework with respect to industry best practices.