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

Permalink

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

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published VersionAdobe PDF (1.74 MB)

View on publisher's site

View statistics

Full metadata record

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/embargoedAccess
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.

Please use this identifier to cite or link to this item:

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