Repeated Auctions for Robust Task Execution by a Robot Team

Abstract

We present empirical results of an auction-based algorithm for dynamic allocation of tasks to robots. The results have been obtained both in simulation and using real robots. A distinctive feature of our algorithm is its robustness to uncertainties and to robot malfunctions that happen during task execution, when unexpected obstacles, loss of communication, and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are resubmitted for bids every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks, enabling the robots to recover from failures and reducing the overall time for task completion.