Augmenting Control Policies with Motion Planning for Robust and Safe Multi-robot Navigation

T. Pan, C. K. Verginis, A. M. Wells, L. E. Kavraki, and D. V. Dimarogonas, “Augmenting Control Policies with Motion Planning for Robust and Safe Multi-robot Navigation,” 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020.

Abstract

This work proposes a novel method of incorporating calls to a motion planner inside a potential field control policy for safe multi-robot navigation with uncertain dynamics. The proposed framework can handle more general scenes than the control policy and has low computational costs. Our work is robust to uncertain dynamics and quickly finds high-quality paths in scenarios generated from real-world floor plans. In the proposed approach, we attempt to follow the control policy as much as possible, and use calls to the motion planner to escape local minima. Trajectories returned from the motion planner are followed using a path-following controller guaranteeing robustness. We demonstrate the utility of our approach with experiments based on floor plans gathered from real buildings.

PDF preprint: http://kavrakilab.org/publications/pan2020augmenting-control-policies.pdf