Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents, such as humans. This paper introduces a novel framework for robot navigation that accounts for high-order system dynamics and maintains safety in the presence of external disturbances, other robots, and non-deterministic intentional agents. Our approach precomputes a tracking error margin for each robot, generates confidence-aware human motion predictions, and coordinates multiple robots with a sequential priority ordering, effectively enabling scalable safe trajectory planning and execution. We demonstrate our approach in hardware with two robots and two humans. We also showcase our work's scalability in a larger simulation.
@article{arxiv.1811.05929,
title = {A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance},
author = {Andrea Bajcsy and Sylvia L. Herbert and David Fridovich-Keil and Jaime F. Fisac and Sampada Deglurkar and Anca D. Dragan and Claire J. Tomlin},
journal= {arXiv preprint arXiv:1811.05929},
year = {2018}
}