Analysis of cluster formation techniques for multi-robot task allocation using sequential single-cluster auctions

Authors:
Bradford Heap;Maurice Pagnucco
Affiliations:
ARC Centre of Excellence in Autonomous Systems, School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;ARC Centre of Excellence in Autonomous Systems, School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia
Venue:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

Recent research has shown the benefits of using K-means clustering in task allocation to robots. However, there is little evaluation of other clustering techniques. In this paper we compare K-means clustering to single-linkage clustering and consider the effects of straight line and true path distance metrics in cluster formation. Our empirical results show single-linkage clustering with a true path distance metric provides the best solutions to the multi-robot task allocation problem when used in sequential single-cluster auctions.