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An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles

Published online by Cambridge University Press:  19 May 2020

Yang Long ,
Zheming Zuo ,
Yixin Su ,
Jie Li  and
Huajun Zhang
Yang Long
Affiliation:
(School of Automation, Wuhan University of Technology, Wuhan, China) (Science and Technology College of Hubei Minzu University, Enshi, China)
Zheming Zuo
Affiliation:
(Department of Computer Science, Durham University, Durham, UK)
Yixin Su*
Affiliation:
(School of Automation, Wuhan University of Technology, Wuhan, China)
Jie Li
Affiliation:
(School of Computing & Digital Technologies, Teesside University, Middlesbrough, UK)
Huajun Zhang
Affiliation:
(School of Automation, Wuhan University of Technology, Wuhan, China)
*
(E-mail: suyixin@whut.edu.cn)
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Abstract

The bacterial foraging optimisation (BFO) algorithm is a commonly adopted bio-inspired optimisation algorithm. However, BFO is not a proper choice in coping with continuous global path planning in the context of unmanned surface vehicles (USVs). In this paper, a grid partition-based BFO algorithm, named AS-BFO, is proposed to address this issue in which the enhancement is contributed by the involvement of the A* algorithm. The chemotaxis operation is redesigned in AS-BFO. Through repeated simulations, the relative optimal parameter combination of the proposed algorithm is obtained and the most influential parameters are identified by sensitivity analysis. The performance of AS-BFO is evaluated via five size grid maps and the results show that AS-BFO has advantages in USV global path planning.

Keywords

USVAS-BFOChemotaxis OperationA* AlgorithmSensitivity Analysis
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 6 , November 2020 , pp. 1247 - 1262
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Copyright
Copyright © The Royal Institute of Navigation 2020

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