共に立ち上がれ：協調的異種プレイによるリアルタイム複数ロボットタスク割り当ての最適化
Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays
倉庫での複数ロボットのリアルタイムタスク割り当てを、自己対戦に着想を得た二重エージェント強化学習で最適化し、バッテリー管理や衝突回避も考慮したフレームワークMRTAgentを提案。
著者: Aritra Pal, Anandsingh Chauhan, Mayank Baranwal
分類: cs.RO, cs.AI, cs.LG, cs.MA, cs.SY, eess.SY
原文アブストラクト
Efficient task allocation among multiple robots is crucial for optimizing productivity in modern warehouses, particularly in response to the increasing demands of online order fulfillment. This paper addresses the real-time multi-robot task allocation (MRTA) problem in dynamic warehouse environments, where tasks emerge with specified start and end locations. The objective is to minimize both the total travel distance of robots and delays in task completion, while also considering practical constraints such as battery management and collision avoidance. We introduce MRTAgent, a dual-agent Reinforcement Learning (RL) framework inspired by self-play, designed to optimize task assignments and robot selection to ensure timely task execution. For safe navigation, a modified linear quadratic controller (LQR) approach is employed. To the best of our knowledge, MRTAgent is the first framework to address all critical aspects of practical MRTA problems while supporting continuous robot movements.
