用强化学习优化仓库多机器人实时任务分配，兼顾效率与安全
Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays
- 双智能体强化学习框架，模拟机器人协作自演进
- 降低总行程距离与任务延迟，支持连续移动与电池管理
- 适合物流自动化、智能仓储场景的实时调度系统
在动态仓库环境中，高效分配多机器人任务对提升现代仓储生产力至关重要，尤其面对在线订单激增的挑战。本文针对实时多机器人任务分配（MRTA）问题，处理具有指定起止位置的任务动态生成。目标是最小化机器人总行程距离和任务完成延迟，同时考虑电池管理与碰撞避免等实际约束。提出MRTAgent，一种受自对弈启发的双智能体强化学习框架，用于优化任务指派与机器人选择，确保任务及时执行。为保障安全导航，采用改进的线性二次调节器（LQR）方法。据我们所知，MRTAgent是首个同时解决实际MRTA关键问题并支持连续机器人运动的框架。
原文摘要 · Abstract (English)
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.
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