分散型マルチ自律表面車両ナビゲーションのための分布強化学習
Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning
混雑水域での複数自律表面車両の衝突回避を、分布強化学習を用いた分散型ポリシーで実現し、安全性と効率を両立させた研究。
著者: Xi Lin, Yewei Huang, Fanfei Chen, Brendan Englot
分類: cs.RO
原文アブストラクト
Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that follow the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) have been proposed in recent years. However, it may be difficult and unsafe to follow COLREGs in congested waters, where multiple ASVs are navigating in the presence of static obstacles and strong currents, due to the complex interactions. To address this problem, we propose a decentralized multi-ASV collision avoidance policy based on Distributional Reinforcement Learning, which considers the interactions among ASVs as well as with static obstacles and current flows. We evaluate the performance of the proposed Distributional RL based policy against a traditional RL-based policy and two classical methods, Artificial Potential Fields (APF) and Reciprocal Velocity Obstacles (RVO), in simulation experiments, which show that the proposed policy achieves superior performance in navigation safety, while requiring minimal travel time and energy. A variant of our framework that automatically adapts its risk sensitivity is also demonstrated to improve ASV safety in highly congested environments.
