対数マップと深層強化学習による分散型マルチロボット障害物回避
Distributed Multi-Robot Obstacle Avoidance via Logarithmic Map-based Deep Reinforcement Learning
レーザー情報を対数マップに変換し、通信不要の分散型深層強化学習で複数ロボットの障害物回避を実現した研究。
著者: Jiafeng Ma, Guangda chen, Yingfeng Chen, Yujing Hu, Changjie Fan, Jianming Zhang
分類: cs.RO
原文アブストラクト
Developing a safe, stable, and efficient obstacle avoidance policy in crowded and narrow scenarios for multiple robots is challenging. Most existing studies either use centralized control or need communication with other robots. In this paper, we propose a novel logarithmic map-based deep reinforcement learning method for obstacle avoidance in complex and communication-free multi-robot scenarios. In particular, our method converts laser information into a logarithmic map. As a step toward improving training speed and generalization performance, our policies will be trained in two specially designed multi-robot scenarios. Compared to other methods, the logarithmic map can represent obstacles more accurately and improve the success rate of obstacle avoidance. We finally evaluate our approach under a variety of simulation and real-world scenarios. The results show that our method provides a more stable and effective navigation solution for robots in complex multi-robot scenarios and pedestrian scenarios. Videos are available at https://youtu.be/r0EsUXe6MZE.
