A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance
Andrea Bajcsy, Sylvia L. Herbert, David Fridovich-Keil, Jaime F. Fisac, Sampada Deglurkar, Anca D. Dragan, Claire J. Tomlin
Abstract
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 humans. 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, and showcase scalability in a larger simulation.
BibTeX
@inproceedings{icra2019_ascalableframewo,
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},
booktitle = {ICRA 2019},
year = {2019}
}