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Efficient multi-task allocation and path planning for unmanned surface vehicle in support of ocean operations

Liu, Y; Bucknall, R; (2017) Efficient multi-task allocation and path planning for unmanned surface vehicle in support of ocean operations. Neurocomputing , 275 pp. 1550-1566. 10.1016/j.neucom.2017.09.088. Green open access

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Abstract

Presently, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) to support complex ocean operations. In order to carry out these missions in a more efficient way, an intelligent hybrid multi-task allocation and path planning algorithm is required and has been proposed in this paper. In terms of the multi-task allocation, a novel algorithm based upon a self-organising map (SOM) has been designed and developed. The main contribution is that an adaptive artificial repulsive force field has been constructed and integrated into the SOM to achieve collision avoidance capability. The new algorithm is able to fast and effectively generate a sequence for executing multiple tasks in a cluttered maritime environment involving numerous obstacles. After generating an optimised task execution sequence, a path planning algorithm based upon fast marching square (FMS) is utilised to calculate the trajectories. Because of the introduction of a safety parameter, the FMS is able to adaptively adjust the dimensional influence of an obstacle and accordingly generate the paths to ensure the safety of the USV. The algorithms have been verified and evaluated through a number of computer based simulations and has been proven to work effectively in both simulated and practical maritime environments.

Type: Article
Title: Efficient multi-task allocation and path planning for unmanned surface vehicle in support of ocean operations
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neucom.2017.09.088
Publisher version: https://doi.org/10.1016/j.neucom.2017.09.088
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Unmanned surface vehicle (USV), Task allocation, Path planning, Self-organising map, TRAVELING SALESMAN PROBLEM, SELF-ORGANIZING MAP, MARITIME ENVIRONMENT, POLYGONAL DOMAIN, NEURAL-NETWORK, ALGORITHM
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10041970
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