基于在线海流数据的自然能驱动无人艇能源最优路径规划OA北大核心CSTPCD

In this paper,an energy-saving optimal path planning algorithm is proposed based on online monitoring current data,taking wave rider unmanned surface vehicle driven by natural energy as the object of study.The goal is to reduce energy consumption and increase the endurance of natural energy-driven unmanned surface vehicles(NS-Vs)by effectively using the online monitoring current and wind data during the voyage.Particle swarm optimization(PSO)is improved by the wave rider's minimum radius of gyration,which is obtained from real ship tests.Then,the distance optimal path planning algorithm(DOPSO)is proposed,and an energy consumption model is established,considering the influence of current and wind on the energy consumption of the wave rider.To reach the energy opti-mal objective,the PSO is improved in a static current environment,and energy optimal particle swarm optimization(EOPSO)is explored.Dynamic current data are also obtained based on the online monitoring current and wind infor-mation obtained during the navigation of the wave rider.Upon optimizing the EOPSO,the energy-saving optimal path planning algorithm based on online monitoring current(OCPSO)is proposed.The energy consumption rates of DOP-SO and OCPSO in the same working conditions are compared via simulation tests to verify the feasibility and effective-ness of the algorithm.