深層強化学習による未知複雑環境でのエンドツーエンド分散型マルチロボットナビゲーション
End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning
深層強化学習を用いて、ロボットチームが未知の複雑環境で衝突回避と接続維持しながら目標地点へ到達する分散型ナビゲーション手法を提案し、シミュレーションと実機実験で有効性を示した。
著者: Juntong Lin, Xuyun Yang, Peiwei Zheng, Hui Cheng
分類: cs.RO
原文アブストラクト
In this paper, a novel deep reinforcement learning (DRL)-based method is proposed to navigate the robot team through unknown complex environments, where the geometric centroid of the robot team aims to reach the goal position while avoiding collisions and maintaining connectivity. Decentralized robot-level policies are derived using a mechanism of centralized learning and decentralized executing. The proposed method can derive end-to-end policies, which map raw lidar measurements into velocity control commands of robots without the necessity of constructing obstacle maps. Simulation and indoor real-world unmanned ground vehicles (UGVs) experimental results verify the effectiveness of the proposed method.
