Coordinated defensive escorts can aid a navigating payload by positioning themselves in order to maintain the safety of the payload from obstacles. In this paper, we present a novel, end-to-end solution for coordinating an escort team for protecting high-value payloads. Our solution employs deep reinforcement learning (RL) in order to train a team of escorts to maintain payload safety while navigating alongside the payload. This is done in a distributed fashion, relying only on limited range positional information of other escorts, the payload, and the obstacles. When compared to a state-of-art algorithm for obstacle avoidance, our solution with a single escort increases navigation success up to 31%. Additionally, escort teams increase success rate by up to 75% percent over escorts in static formations. We also show that this learned solution is general to several adaptations in the scenario including: a changing number of escorts in the team, changing obstacle density, and changes in payload conformation. Video: https://youtu.be/SoYesKti4VA.
@article{arxiv.1910.04537,
title = {Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning},
author = {Arpit Garg and Yazied A. Hasan and Adam Yañez and Lydia Tapia},
journal= {arXiv preprint arXiv:1910.04537},
year = {2019}
}
Comments
IEEE Robotics and Automation Letters with International Conference on Robotics and Automation (ICRA) option, 2020, under review