Dynamic Multi-Robot Task Allocation under Uncertainty and Communication Constraints: A Game-Theoretic Approach

TL;DR

This paper presents a game-theoretic, decentralized approach for dynamic multi-robot task allocation under uncertainty, limited communication, and time constraints, demonstrating competitive performance in city-scale simulations.

Contribution

It introduces the Iterative Best Response (IBR) policy for decentralized task allocation under incomplete information and communication constraints, with empirical evaluation against baselines.

Findings

IBR achieves competitive task completion rates.

IBR has lower computation time compared to baselines.

Performance is robust under full and sparse communication.

Abstract

We study dynamic multi-robot task allocation under uncertain task completion, time-window constraints, and incomplete information. Tasks arrive online over a finite horizon and must be completed within specified deadlines, while agents operate from distributed hubs with limited sensing and communication. We model incomplete information through hub-based sensing regions that determine task visibility and a communication graph that governs inter-hub information exchange. Using this framework, we propose Iterative Best Response (IBR), a decentralized policy in which each agent selects the task that maximizes its marginal contribution to the locally observed welfare. We compare IBR against three baselines: Earliest Due Date first (EDD), Hungarian algorithm, and Stochastic Conflict-Based Allocation (SCoBA), on a city-scale package-delivery domain with up to 100 drones and varying task arrival scenarios. Under full and sparse communication, IBR achieves competitive task-completion performance with lower computation time.