Comparative experiments on optimization criteria and algorithms for auction based multi-robot task allocation

Abstract

Auction based techniques are a highly successful tool used for multi-robot task allocation. However, theoretical performance and a proper taxonomy of optimization objectives have remained scarce until recent studies. Implementations from different authors have not been compared in common grounds and in light of these recent findings. In this paper we address this lack of comparative experimentation, providing simulation results on a large real life based scenario and in random worlds. Two intuitive optimization objectives, minimum total resource usage and minimum total time, are evaluated in object searching missions. A method for flexible tailoring of the bidding rules is presented and new insight is gained on the effect of using hybrid criteria for the optimization objective.