Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task

Chen, Y; Yu, J; Mei, Y; Zhang, S; Ai, X; Jia, Z

Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task

Chen, Y

Yu, J Mei, Y Zhang, S Ai, X Jia, Z

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Chinese Journal of Aeronautics, 2016, 29, (1), pp. 184-201
Issue Date:: 2016-02-01

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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	Zhang, S
dc.contributor.author	Ai, X
dc.contributor.author	Jia, Z
dc.date.accessioned	2022-10-12T00:12:33Z
dc.date.available	2022-10-12T00:12:33Z
dc.date.issued	2016-02-01
dc.identifier.citation	Chinese Journal of Aeronautics, 2016, 29, (1), pp. 184-201
dc.identifier.issn	1000-9361
dc.identifier.uri	http://hdl.handle.net/10453/162487
dc.description.abstract	A hierarchic optimization strategy based on the offline path planning process and online trajectory planning process is presented to solve the trajectory optimization problem of multiple quad-rotor unmanned aerial vehicles in the collaborative assembling task. Firstly, the path planning process is solved by a novel parallel intelligent optimization algorithm, the central force optimization-genetic algorithm (CFO-GA), which combines the central force optimization (CFO) algorithm with the genetic algorithm (GA). Because of the immaturity of the CFO, the convergence analysis of the CFO is completed by the stability theory of the linear time-variant discrete-time systems. The results show that the parallel CFO-GA algorithm converges faster than the parallel CFO and the central force optimization-sequential quadratic programming (CFO-SQP) algorithm. Then, the trajectory planning problem is established based on the path planning results. In order to limit the range of the attitude angle and guarantee the flight stability, the optimized object is changed from the ordinary six-degree-of-freedom rigid-body dynamic model to the dynamic model with an inner-loop attitude controller. The results show that the trajectory planning process can be solved by the mature SQP algorithm easily. Finally, the discussion and analysis of the real-time performance of the hierarchic optimization strategy are presented around the group number of the waypoints and the equal interval time.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Chinese Journal of Aeronautics
dc.relation.isbasedon	10.1016/j.cja.2015.12.008
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.subject.classification	Aerospace & Aeronautics
dc.title	Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task
dc.type	Journal Article
utslib.citation.volume	29
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2022-10-12T00:12:30Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	29
utslib.citation.issue	1

Abstract:

A hierarchic optimization strategy based on the offline path planning process and online trajectory planning process is presented to solve the trajectory optimization problem of multiple quad-rotor unmanned aerial vehicles in the collaborative assembling task. Firstly, the path planning process is solved by a novel parallel intelligent optimization algorithm, the central force optimization-genetic algorithm (CFO-GA), which combines the central force optimization (CFO) algorithm with the genetic algorithm (GA). Because of the immaturity of the CFO, the convergence analysis of the CFO is completed by the stability theory of the linear time-variant discrete-time systems. The results show that the parallel CFO-GA algorithm converges faster than the parallel CFO and the central force optimization-sequential quadratic programming (CFO-SQP) algorithm. Then, the trajectory planning problem is established based on the path planning results. In order to limit the range of the attitude angle and guarantee the flight stability, the optimized object is changed from the ordinary six-degree-of-freedom rigid-body dynamic model to the dynamic model with an inner-loop attitude controller. The results show that the trajectory planning process can be solved by the mature SQP algorithm easily. Finally, the discussion and analysis of the real-time performance of the hierarchic optimization strategy are presented around the group number of the waypoints and the equal interval time.

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http://hdl.handle.net/10453/162487