Dynamic Multi-Robot Task Allocation under Uncertainty and Communication Constraints: A Game-Theoretic Approach
For multi-robot systems operating under realistic communication and sensing constraints, this work provides a decentralized, game-theoretic approach that balances performance and computational efficiency.
The paper tackles dynamic multi-robot task allocation under uncertainty, time-window constraints, and limited communication. The proposed Iterative Best Response (IBR) algorithm achieves competitive task-completion performance with lower computation time compared to baselines in city-scale simulations with up to 100 drones.
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.