An empirical evaluation of auction-based task allocation in multi-robot teams

Abstract

Task allocation is an important topic in multiagent and multi-robot teams. In recent years, there has been much research on the use of auction-based methods to provide a distributed approach to task allocation. Team members bid on tasks based on local information, and the allocation is based on these bids. The focus of prior work has been on optimal allocations and has established that auction-based methods perform well in comparison with optimal methods, with the advantage of scaling better. Here we take a different approach, comparing auction-based methods not on the optimality of the allocation, but on the efficient execution of the allocated tasks. This approach factors in aspects such as the utilisation of the team members and the degree to which they interfere with each others' progress, giving a fuller picture of the practical use of auction-based methods.