COLLISION AVOIDANCE PATH PLANNING FOR SHIPS BY PARTICLE SWARM OPTIMIZATION

Authors

Yu-Tao Kang, College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China.Follow
Wei-Jiong Chen, College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China.
Da-Qi Zhu, Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai, China
Jin-Hui Wang, College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
Qi-Miao Xie, College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China

Abstract

Ship collision avoidance is a key consideration in maritime systems. Collision avoidance maneuvers depend on navigators’ experience and skill levels. Because both maritime traffic densities and average ship speeds are increasing, the times available for decision-making are decreasing, which elevates the risk of human errors in the collision avoidance process. To reduce the effect of human factors and efficiently prevent collisions between ships navigating in open water with effective visibility, a particle swarm optimization (PSO) algorithm can be used to plan ship paths. An improved ship domain dynamic model can assess collision risks in close-range encounters. Several marine traffic scenarios based on standard encounter types were simulated; the proposed PSO algorithm was tested in those scenarios. This paper discusses the compatibility and consistency of the algorithm outputs as well as the execution efficiency of the algorithm.

Recommended Citation

Kang, Yu-Tao; Chen, Wei-Jiong; Zhu, Da-Qi; Wang, Jin-Hui; and Xie, Qi-Miao (2018) "COLLISION AVOIDANCE PATH PLANNING FOR SHIPS BY PARTICLE SWARM OPTIMIZATION," Journal of Marine Science and Technology: Vol. 26: Iss. 6, Article 3.
DOI: 10.6119/JMST.201812_26(6).0003
Available at: https://jmstt.ntou.edu.tw/journal/vol26/iss6/3