Multi-UAV Task Assignment with Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Chen, Y; Yang, D; Yu, J

Multi-UAV Task Assignment with Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Chen, Y

Yang, D Yu, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Aerospace and Electronic Systems, 2018, 54 (6), pp. 2853 - 2872
Issue Date:: 2018-12-01

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Field	Value	Language
dc.contributor.author	Chen, Y https://orcid.org/0000-0003-2756-7050	en_US
dc.contributor.author	Yang, D	en_US
dc.contributor.author	Yu, J	en_US
dc.date.available	2020-05-25T19:02:53Z
dc.date.issued	2018-12-01	en_US
dc.identifier.citation	IEEE Transactions on Aerospace and Electronic Systems, 2018, 54 (6), pp. 2853 - 2872	en_US
dc.identifier.issn	0018-9251	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125920
dc.description.abstract	© 1965-2011 IEEE. This paper presents a systematical framework to solve the multiple unmanned aerial vehicles (multi-UAV) cooperative task assignment problem. Based on a combinatorial optimization model, it is solved by a digraph-based method and a novel meta-heuristic optimization method, named modified two-part wolf pack search (MTWPS) algorithm. When the number of UAVs/targets is large, in order to reduce the simulation time, we also present a new solution framework based on an easy-computing objective function. Additionally, the parameter and time-sensitive uncertainties are considered in the extended task assignment problem. For the problem with parameter uncertainty, it is formulated by a robust optimization method and solved by a novel combined algorithm, including the classical interior point method and our MTWPS algorithm. For the problem with time-sensitive uncertainty, it is solved by a practical online hierarchical planning algorithm. Finally, numerical simulations and physical experiments demonstrate that the proposed methods can provide a flyable solution for the UAVs and achieve outstanding performance in comparison with other algorithms.	en_US
dc.relation.ispartof	IEEE Transactions on Aerospace and Electronic Systems	en_US
dc.relation.isbasedon	10.1109/TAES.2018.2831138	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Aerospace & Aeronautics	en_US
dc.title	Multi-UAV Task Assignment with Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	54	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0901 Aerospace Engineering	en_US
utslib.for	0909 Geomatic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	54	en_US

Abstract:

© 1965-2011 IEEE. This paper presents a systematical framework to solve the multiple unmanned aerial vehicles (multi-UAV) cooperative task assignment problem. Based on a combinatorial optimization model, it is solved by a digraph-based method and a novel meta-heuristic optimization method, named modified two-part wolf pack search (MTWPS) algorithm. When the number of UAVs/targets is large, in order to reduce the simulation time, we also present a new solution framework based on an easy-computing objective function. Additionally, the parameter and time-sensitive uncertainties are considered in the extended task assignment problem. For the problem with parameter uncertainty, it is formulated by a robust optimization method and solved by a novel combined algorithm, including the classical interior point method and our MTWPS algorithm. For the problem with time-sensitive uncertainty, it is solved by a practical online hierarchical planning algorithm. Finally, numerical simulations and physical experiments demonstrate that the proposed methods can provide a flyable solution for the UAVs and achieve outstanding performance in comparison with other algorithms.

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http://hdl.handle.net/10453/125920