Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays

TL;DR

This paper presents MRTAgent, a reinforcement learning framework that optimizes real-time multi-robot task allocation in dynamic warehouses, considering travel distance, delays, battery, and collision constraints, supporting continuous robot movements.

Contribution

The paper introduces MRTAgent, the first RL-based framework addressing all key aspects of practical MRTA with continuous robot movement support.

Findings

Effective reduction in total travel distance and task delays.

Successful integration of collision avoidance and battery management.

Demonstrated scalability in dynamic warehouse scenarios.

Abstract

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.