Performance analysis of clustering methods for balanced multi-robot task allocations

Elango Murugappan, Nachiappan Subramanian, Shams Rahman, Mark Goh, Hing Kai Chan

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

This paper models the Multi-Robot Task Allocation (MRTA) problem with a balance constraint to improve the utilisation (completion time) of the robots. Our balancing constraint attempts to minimise the travel distance difference among the robots as well as allocates an equal set of tasks to these robots. The clustering-based approach is employed to solve the Balanced Multi-Robot Task Allocation (BMRTA) problem for two principal reasons. That is, this approach clusters given tasks into groups using various clustering techniques for each robot and sequences the route for each robot using the travelling salesman problem (TSP) conhull algorithm. This work analyses the suitability and performance of the clustering techniques with respect to the balancing criteria using a benchmark dataset. Our findings suggest that K-means clustering is the most suitable for the solving BMRTA problem with complex topologies and it is scalable to deal with any number of tasks and robots compared with Gaussian Mixtures Models (GMM) and hierarchical clustering methods.

Original languageEnglish
Pages (from-to)4576-4591
Number of pages16
JournalInternational Journal of Production Research
Volume60
Issue number14
DOIs
Publication statusPublished Online - 2 Aug 2021

Keywords

  • Balanced multi-robot task allocation problem
  • clustering
  • conhull algorithm
  • heuristics approach
  • multiple travelling salesperson problem

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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