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Distributed Multi-Objective Algorithm for Preventing Multi-Ship Collisions at Sea

Published online by Cambridge University Press:  03 March 2020

Jinxin Li Open the ORCID record for Jinxin Li [Opens in a new window] ,
Hongbo Wang ,
Zhiying Guan  and
Chong Pan
Jinxin Li
Affiliation:
(State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China)
Hongbo Wang*
Affiliation:
(State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China)
Zhiying Guan
Affiliation:
(State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China)
Chong Pan
Affiliation:
(State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China)
*
(E-mail: [email protected])
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Abstract

Avoidance of collisions at sea is crucial to navigational safety. In this paper, we use a distributed algorithm to communicate the entire collision avoidance trajectory information for each ship. In each communication, we suggest a new improvement function considering safety and efficiency to identify the avoidance ship in each cycle. Considering the nonlinear collision avoidance trajectory of ships, a new method for calculating the degree of danger using a velocity obstacle algorithm is proposed. Therefore, in each communication, each ship considers the avoidance behaviours of other ships in planning its avoidance trajectory. Additionally, we combine bi-criterion evolution (BCE) and the ant lion optimiser to plan the entire collision avoidance path. Three scenarios are designed to demonstrate the performance of this method. The results show that the proposed method can find a suitable collision-free solution for all ships.

Keywords

Bi-Criterion EvolutionDistributed Local Search AlgorithmVelocity Obstacle AlgorithmsMultiple Ships Collision Avoidance
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 5 , September 2020 , pp. 971 - 990
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Copyright
Copyright © The Royal Institute of Navigation 2020

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