Global path planning and multi-objective path control for unmanned surface vehicle based on modified particle swarm optimization (PSO) algorithm. (15th November 2020)

• Record Type: Journal Article
• Authors: Guo, Xinghai; Ji, Mingjun; Zhao, Ziwei; Wen, Dusu; Zhang, Weidan
• Is Part Of: Ocean engineering. Volume 216(2020)
• Publication Date: 2020-11-15
• Subjects: Global path planning -- Multi-objective path control -- Unmanned surface vehicle -- Chaotic and sharing-learning particle swarm optimization -- Currents; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.107693
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This paper investigates the novel path navigation method for an unmanned surface vehicle (USV), which is divided into two-stage: global path planning and path control. In the first stage, combined with the travelling salesman problem (TSP), a global path is obtained by maximizing the profit per unit time in multiple task locations. In the second stage, a nonlinear multi-objective optimization model is formulated for the path control between two task locations. In addition, fixed and time-varying currents are also considered for USV motion, which aims to avoid collision and take full advantage of the direction of currents. To solve the problem quickly and accurately, a chaotic and sharing-learning particle swarm optimization (CSPSO) algorithm is developed to solve the extended TSP and the nonlinear multi-objective model. Simulation experiments validate that the proposed hierarchical navigation method, CSPSO algorithm, and collision avoidance rules are effective and justifiable. Highlights: The global path planning is designed by extended TSP model. Multi-objective function model are established for USV path control. Proposed improve PSO algorithm (CSPSO) and dynamic crowding distance. USV path control also considers the influence of currents.