UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm.

Zhang, Y; Feng, W; Shi, G; Jiang, F; Chowdhury, M; Ling, SH

UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm.

Zhang, Y Feng, W Shi, G Jiang, F Chowdhury, M Ling, SH

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel, Switzerland), 2020, 20, (8)
Issue Date:: 2020-04-17

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Feng, W
dc.contributor.author	Shi, G
dc.contributor.author	Jiang, F
dc.contributor.author	Chowdhury, M
dc.contributor.author	Ling, SH
dc.date.accessioned	2020-11-03T01:30:07Z
dc.date.available	2020-04-10
dc.date.available	2020-11-03T01:30:07Z
dc.date.issued	2020-04-17
dc.identifier.citation	Sensors (Basel, Switzerland), 2020, 20, (8)
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/143714
dc.description.abstract	To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel, Switzerland)
dc.relation.isbasedon	10.3390/s20082307
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 0502 Environmental Science and Management, 0602 Ecology
dc.subject.classification	Analytical Chemistry
dc.title	UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm.
dc.type	Journal Article
utslib.citation.volume	20
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-11-03T01:29:50Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	8

Abstract:

To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.

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http://hdl.handle.net/10453/143714