Auction-Based Task Allocation for Multi-robot Teams in Dynamic Environments.

There has been much research on the use of auction-based methods to provide a distributed approach to task allocation for robot teams. Team members bid on tasks based on their locations, and the allocation is based on these bids. The focus of prior work has been on the optimality of the allocation, establishing that auction-based methods perform well in comparison with optimal allocation methods, with the advantage of scaling better. This paper compares several auction-based methods not on the optimality of the allocation, but on the efficiency of the execution of the allocated tasks giving a fuller picture of the practical use of auction-based methods. Our results show that the advantages of the best auction-based methods are much reduced when the robots are physically dispersed throughout the task space and when the tasks themselves are allocated over time.