Collision avoidance under bounded localization uncertainty

TLDR: Their close and error-bounded convex approximation of the localization density distribution results in collision-free paths under uncertainty, while in many algorithms the robots are approximated by circumscribed radii, the authors use the convex hull to minimize the overestimation in the footprint.

Abstract: We present a multi-mobile robot collision avoidance system based on the velocity obstacle paradigm. Current positions and velocities of surrounding robots are translated to an efficient geometric representation to determine safe motions. Each robot uses on-board localization and local communication to build the velocity obstacle representation of its surroundings. Our close and error-bounded convex approximation of the localization density distribution results in collision-free paths under uncertainty. While in many algorithms the robots are approximated by circumscribed radii, we use the convex hull to minimize the overestimation in the footprint. Results show that our approach allows for safe navigation even in densely packed environments.