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

Gespeichert in:

Autor*in:	Murugappan, Elango [verfasserIn] Subramanian, Nachiappan [verfasserIn] Rahman, Shams [verfasserIn] Goh, Mark - 1960- [verfasserIn] Chan, Hing Kai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

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

Übergeordnetes Werk:	Enthalten in: International journal of production research - London [u.a.] : Taylor & Francis, 1996, 60(2022), 14, Seite 4576-4591
Übergeordnetes Werk:	volume:60 ; year:2022 ; number:14 ; pages:4576-4591

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1080/00207543.2021.1955994

Katalog-ID:	183282442X

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allfields	10.1080/00207543.2021.1955994 doi (DE-627)183282442X (DE-599)KXP183282442X DE-627 ger DE-627 rda eng Murugappan, Elango verfasserin aut Performance analysis of clustering methods for balanced multi-robot task allocations Elango Murugappan, Nachiappan Subramanian, Shams Rahman, Mark Goh and Hing Kai Chan 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Balanced multi-robot task allocation problem (dpeaa)DE-206 clustering (dpeaa)DE-206 conhull algorithm (dpeaa)DE-206 heuristics approach (dpeaa)DE-206 multiple travelling salesperson problem (dpeaa)DE-206 Subramanian, Nachiappan verfasserin (DE-588)1193741602 (DE-627)1672818656 aut Rahman, Shams verfasserin (DE-588)171142233 (DE-627)061312754 (DE-576)131959824 aut Goh, Mark 1960- verfasserin (DE-588)171087356 (DE-627)061252417 (DE-576)131909258 aut Chan, Hing Kai verfasserin (DE-588)142643424 (DE-627)637812689 (DE-576)332572196 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 60(2022), 14, Seite 4576-4591 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:60 year:2022 number:14 pages:4576-4591 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2021.1955994 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 14 4576-4591 26 01 0206 4255698198 x1z 31-01-23 26 00 DE-206 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.
spelling	10.1080/00207543.2021.1955994 doi (DE-627)183282442X (DE-599)KXP183282442X DE-627 ger DE-627 rda eng Murugappan, Elango verfasserin aut Performance analysis of clustering methods for balanced multi-robot task allocations Elango Murugappan, Nachiappan Subramanian, Shams Rahman, Mark Goh and Hing Kai Chan 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Balanced multi-robot task allocation problem (dpeaa)DE-206 clustering (dpeaa)DE-206 conhull algorithm (dpeaa)DE-206 heuristics approach (dpeaa)DE-206 multiple travelling salesperson problem (dpeaa)DE-206 Subramanian, Nachiappan verfasserin (DE-588)1193741602 (DE-627)1672818656 aut Rahman, Shams verfasserin (DE-588)171142233 (DE-627)061312754 (DE-576)131959824 aut Goh, Mark 1960- verfasserin (DE-588)171087356 (DE-627)061252417 (DE-576)131909258 aut Chan, Hing Kai verfasserin (DE-588)142643424 (DE-627)637812689 (DE-576)332572196 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 60(2022), 14, Seite 4576-4591 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:60 year:2022 number:14 pages:4576-4591 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2021.1955994 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 14 4576-4591 26 01 0206 4255698198 x1z 31-01-23 26 00 DE-206 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.
allfields_unstemmed	10.1080/00207543.2021.1955994 doi (DE-627)183282442X (DE-599)KXP183282442X DE-627 ger DE-627 rda eng Murugappan, Elango verfasserin aut Performance analysis of clustering methods for balanced multi-robot task allocations Elango Murugappan, Nachiappan Subramanian, Shams Rahman, Mark Goh and Hing Kai Chan 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Balanced multi-robot task allocation problem (dpeaa)DE-206 clustering (dpeaa)DE-206 conhull algorithm (dpeaa)DE-206 heuristics approach (dpeaa)DE-206 multiple travelling salesperson problem (dpeaa)DE-206 Subramanian, Nachiappan verfasserin (DE-588)1193741602 (DE-627)1672818656 aut Rahman, Shams verfasserin (DE-588)171142233 (DE-627)061312754 (DE-576)131959824 aut Goh, Mark 1960- verfasserin (DE-588)171087356 (DE-627)061252417 (DE-576)131909258 aut Chan, Hing Kai verfasserin (DE-588)142643424 (DE-627)637812689 (DE-576)332572196 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 60(2022), 14, Seite 4576-4591 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:60 year:2022 number:14 pages:4576-4591 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2021.1955994 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 14 4576-4591 26 01 0206 4255698198 x1z 31-01-23 26 00 DE-206 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.
allfieldsGer	10.1080/00207543.2021.1955994 doi (DE-627)183282442X (DE-599)KXP183282442X DE-627 ger DE-627 rda eng Murugappan, Elango verfasserin aut Performance analysis of clustering methods for balanced multi-robot task allocations Elango Murugappan, Nachiappan Subramanian, Shams Rahman, Mark Goh and Hing Kai Chan 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Balanced multi-robot task allocation problem (dpeaa)DE-206 clustering (dpeaa)DE-206 conhull algorithm (dpeaa)DE-206 heuristics approach (dpeaa)DE-206 multiple travelling salesperson problem (dpeaa)DE-206 Subramanian, Nachiappan verfasserin (DE-588)1193741602 (DE-627)1672818656 aut Rahman, Shams verfasserin (DE-588)171142233 (DE-627)061312754 (DE-576)131959824 aut Goh, Mark 1960- verfasserin (DE-588)171087356 (DE-627)061252417 (DE-576)131909258 aut Chan, Hing Kai verfasserin (DE-588)142643424 (DE-627)637812689 (DE-576)332572196 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 60(2022), 14, Seite 4576-4591 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:60 year:2022 number:14 pages:4576-4591 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2021.1955994 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 14 4576-4591 26 01 0206 4255698198 x1z 31-01-23 26 00 DE-206 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.
allfieldsSound	10.1080/00207543.2021.1955994 doi (DE-627)183282442X (DE-599)KXP183282442X DE-627 ger DE-627 rda eng Murugappan, Elango verfasserin aut Performance analysis of clustering methods for balanced multi-robot task allocations Elango Murugappan, Nachiappan Subramanian, Shams Rahman, Mark Goh and Hing Kai Chan 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Balanced multi-robot task allocation problem (dpeaa)DE-206 clustering (dpeaa)DE-206 conhull algorithm (dpeaa)DE-206 heuristics approach (dpeaa)DE-206 multiple travelling salesperson problem (dpeaa)DE-206 Subramanian, Nachiappan verfasserin (DE-588)1193741602 (DE-627)1672818656 aut Rahman, Shams verfasserin (DE-588)171142233 (DE-627)061312754 (DE-576)131959824 aut Goh, Mark 1960- verfasserin (DE-588)171087356 (DE-627)061252417 (DE-576)131909258 aut Chan, Hing Kai verfasserin (DE-588)142643424 (DE-627)637812689 (DE-576)332572196 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 60(2022), 14, Seite 4576-4591 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:60 year:2022 number:14 pages:4576-4591 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2021.1955994 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/00207543.2021.1955994 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 60 2022 14 4576-4591 26 01 0206 4255698198 x1z 31-01-23 26 00 DE-206 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.
language	English
source	Enthalten in International journal of production research 60(2022), 14, Seite 4576-4591 volume:60 year:2022 number:14 pages:4576-4591
sourceStr	Enthalten in International journal of production research 60(2022), 14, Seite 4576-4591 volume:60 year:2022 number:14 pages:4576-4591
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topic_facet	Balanced multi-robot task allocation problem clustering conhull algorithm heuristics approach multiple travelling salesperson problem
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container_title	International journal of production research
authorswithroles_txt_mv	Murugappan, Elango @@aut@@ Subramanian, Nachiappan @@aut@@ Rahman, Shams @@aut@@ Goh, Mark @@aut@@ Chan, Hing Kai @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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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.</subfield></datafield></record></collection>
author	Murugappan, Elango
spellingShingle	Murugappan, Elango misc Balanced multi-robot task allocation problem misc clustering misc conhull algorithm misc heuristics approach misc multiple travelling salesperson problem Performance analysis of clustering methods for balanced multi-robot task allocations
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topic_title	26 00 DE-206 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 Performance analysis of clustering methods for balanced multi-robot task allocations Elango Murugappan, Nachiappan Subramanian, Shams Rahman, Mark Goh and Hing Kai Chan Balanced multi-robot task allocation problem (dpeaa)DE-206 clustering (dpeaa)DE-206 conhull algorithm (dpeaa)DE-206 heuristics approach (dpeaa)DE-206 multiple travelling salesperson problem (dpeaa)DE-206
topic	misc Balanced multi-robot task allocation problem misc clustering misc conhull algorithm misc heuristics approach misc multiple travelling salesperson problem
topic_unstemmed	misc Balanced multi-robot task allocation problem misc clustering misc conhull algorithm misc heuristics approach misc multiple travelling salesperson problem
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title_full	Performance analysis of clustering methods for balanced multi-robot task allocations Elango Murugappan, Nachiappan Subramanian, Shams Rahman, Mark Goh and Hing Kai Chan
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title_short	Performance analysis of clustering methods for balanced multi-robot task allocations
url	https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1955994 https://doi.org/10.1080/00207543.2021.1955994 https://www.tandfonline.com/doi/epub/10.1080/00207543.2021.1955994
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Performance analysis of clustering methods for balanced multi-robot task allocations

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