IJPR manuscript 19 June 2021.pdf (697.69 kB)
Performance analysis of clustering methods for balanced multi-robot task allocations
journal contribution
posted on 2023-06-10, 00:21 authored by Elango Murugappan, Nachiappan SubramanianNachiappan Subramanian, Shams Rahman, Mark Goh, HingKai ChanThis 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.
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Publication status
- Published
File Version
- Accepted version
Journal
International Journal of Production ResearchISSN
0020-7543Publisher
Taylor & FrancisExternal DOI
Department affiliated with
- SPRU - Science Policy Research Unit Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2021-07-14First Open Access (FOA) Date
2022-08-03First Compliant Deposit (FCD) Date
2021-07-14Usage metrics
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