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

2026-04-13

AI Summary

This work addresses the online task allocation problem in dynamic multi-robot systems under task uncertainty, time-window constraints, and limited communication. The authors propose a distributed framework based on perceptual regions and communication graphs, introducing for the first time the game-theoretic iterative best response (IBR) mechanism to this setting. Each robot optimizes its marginal contribution using only local observations, enabling efficient task allocation in a decentralized manner. Experimental results in a simulated urban delivery scenario with up to one hundred drones demonstrate that IBR achieves task completion rates comparable to or better than those of EDD, the Hungarian algorithm, and SCoBA, under both full and sparse communication topologies, while incurring significantly lower computational overhead. The approach thus balances efficiency and scalability in large-scale multi-robot coordination.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Distributed Problem Solving
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• multi-robot task allocation
• uncertainty
• communication constraints
• incomplete information
• dynamic task arrival

Innovation

• Game-Theoretic Task Allocation
• Iterative Best Response
• Decentralized Multi-Robot Systems
• Uncertain Task Completion
• Communication Constraints