CALU: Collision Avoidance with Localization Uncertainty

CALU is a multi-robot collision avoidance system based on the velocity obstacle paradigm. In contrast to previous approaches, we alleviate the strong requirement for perfect sensing (i. e. global positioning) using Adaptive Monte-Carlo Localization on a per-agent level.

Authors

• Daniel Claes University of Liverpool
• Daniel Hennes Google DeepMind
• Karl Tuyls University of Liverpool
• Wim Meeussen

Keywords

• multi-robot systems
• optimal reciprocal collision avoidance
• adaptive monte-carlo localization