Modified central force optimization (MCFO) algorithm for 3D UAV path planning

Chen, Y; Yu, J; Mei, Y; Wang, Y; Su, X

Modified central force optimization (MCFO) algorithm for 3D UAV path planning

Chen, Y

Yu, J Mei, Y Wang, Y Su, X

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2016, 171, pp. 878-888
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Chen, Y https://orcid.org/0000-0003-2756-7050
dc.contributor.author	Yu, J
dc.contributor.author	Mei, Y
dc.contributor.author	Wang, Y
dc.contributor.author	Su, X
dc.date.accessioned	2022-10-12T00:14:17Z
dc.date.available	2022-10-12T00:14:17Z
dc.date.issued	2016-01-01
dc.identifier.citation	Neurocomputing, 2016, 171, pp. 878-888
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/162488
dc.description.abstract	Path planning for the three-dimensional (3D) unmanned aerial vehicles (UAV) is a very important element of the whole UAV autonomous control system. In this paper, a modified central force optimization (MCFO) method is introduced to solve this complicated path-optimization problem for the rotary wing vertical take-off and landing (VTOL) aircraft. In the path planning process, the idea from the particle swarm optimization (PSO) algorithm and the mutation operator of the genetic algorithm (GA) are applied to improve the original CFO method. Furthermore, the convergence analysis of the whole MCFO method is established by the linear difference equation method. Then, in order to verify the effectiveness and practicality of this new path planning method, the path following process is put forward based on the six-degree-of-freedom quadrotor helicopter control system. At last, the comparison simulations among the six algorithms show that the trajectories produced by the whole MCFO method are more superior than the original CFO algorithm, the GA, the Firefly algorithm (FA), the PSO algorithm, the random search (RS) way and the other MCFO algorithm under the same conditions. What is more, the path following process results show that the path planning results are practical for the real dynamic model of the quadrotor helicopter.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2015.07.044
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Modified central force optimization (MCFO) algorithm for 3D UAV path planning
dc.type	Journal Article
utslib.citation.volume	171
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-12T00:14:15Z
pubs.publication-status	Published
pubs.volume	171

Abstract:

Path planning for the three-dimensional (3D) unmanned aerial vehicles (UAV) is a very important element of the whole UAV autonomous control system. In this paper, a modified central force optimization (MCFO) method is introduced to solve this complicated path-optimization problem for the rotary wing vertical take-off and landing (VTOL) aircraft. In the path planning process, the idea from the particle swarm optimization (PSO) algorithm and the mutation operator of the genetic algorithm (GA) are applied to improve the original CFO method. Furthermore, the convergence analysis of the whole MCFO method is established by the linear difference equation method. Then, in order to verify the effectiveness and practicality of this new path planning method, the path following process is put forward based on the six-degree-of-freedom quadrotor helicopter control system. At last, the comparison simulations among the six algorithms show that the trajectories produced by the whole MCFO method are more superior than the original CFO algorithm, the GA, the Firefly algorithm (FA), the PSO algorithm, the random search (RS) way and the other MCFO algorithm under the same conditions. What is more, the path following process results show that the path planning results are practical for the real dynamic model of the quadrotor helicopter.

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http://hdl.handle.net/10453/162488