Abstract
Aiming at the path planning of Unmanned Surface Vehicle (USV) sailing at sea, a USV multi-objective path planning algorithm based on S-57 electronic chart data and adaptive and mutual learning particle swarm optimization (AMPSO) algorithm is proposed. By using ISO8211 lib to analyze S-57 electronic chart, the marine environment model of USV navigation through grid method is established. Due to the inappropriate value of inertia weight and the decrease of particle population diversity, the traditional algorithm show problems such as slow convergence speed and weak search ability. Then this paper introduces adaptive inertia weight factor, learning factor and mutual learning mechanism to enhance the global optimization ability and search efficiency of the algorithm. Finally, to meet the needs of different tasks of USV, a multi-objective path planning model considering path length, smoothness, energy consumption and marine environment interference is established. Compared with other path planning algorithms, the algorithm has stronger convergence speed and search ability, and takes into account multi-objective optimization.
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