Work

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

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2604.11954

Authors: Mendoza, Maria G., Su, Pan-Yang, Ferguson, Bryce L., Sastry, S. Shankar

Institutions: University of California, Berkeley, Dartmouth College

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Aeronautics and Space Administration

Award: Collaborative Research: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable Networks

OpenAlex ID: W7154477192

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