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Analysis of Cluster Formation Techniques for Multi-robot Task Allocation Using Sequential Single-Cluster Auctions
Bradford Heap and Maurice Pagnucco
ARC Centre of Excellence in Autonomous Systems School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australiabradfordh@cse.unsw.edu.au
morri@cse.unsw.edu.au
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.
LNAI 7691, p. 839 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012