深層強化学習とポテンシャル場による2Dレーザスキャンを用いた社会的配慮型マルチエージェント追従
Socially-Aware Multi-Agent Following with 2D Laser Scans via Deep Reinforcement Learning and Potential Field
複数ロボットが2Dレーザスキャンのみを用い、強化学習で局所的な衝突回避を、ポテンシャル場で編隊制御と目標割当を行い、歩行者環境で社会的に配慮しながら対象を追従する手法を提案した。
著者: Yuxiang Cui, Xiaolong Huang, Yue Wang, Rong Xiong
分類: cs.RO
原文アブストラクト
Target following in dynamic pedestrian environments is an important task for mobile robots. However, it is challenging to keep tracking the target while avoiding collisions in crowded environments, especially with only one robot. In this paper, we propose a multi-agent method for an arbitrary number of robots to follow the target in a socially-aware manner using only 2D laser scans. The multi-agent following problem is tackled by utilizing the complementary strengths of both reinforcement learning and potential field, in which the reinforcement learning part handles local interactions while navigating to the goals assigned by the potential field. Specifically, with the help of laser scans in obstacle map representation, the learning-based policy can help the robots avoid collisions with both static obstacles and dynamic obstacles like pedestrians in advance, namely socially aware. While the formation control and goal assignment for each robot is obtained from a target-centered potential field constructed using aggregated state information from all the following robots. Experiments are conducted in multiple settings, including random obstacle distributions and different numbers of robots. Results show that our method works successfully in unseen dynamic environments. The robots can follow the target in a socially compliant manner with only 2D laser scans.
