首页|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

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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
为了有效地利用在线监测到的海流和风数据，减少能源消耗以提高自然能驱动无人艇的续航力，以"驭浪者"号自然能驱动无人艇为研究对象，提出一种基于在线海流数据的能源最优路径规划方法。通过"驭浪者"号实船试验得到的最小回转半径对粒子群算法进行改进，设计距离最优的路径规划算法。考虑到海流和风对"驭浪者"号能源消耗的影响，建立海流和风影响下能源消耗模型;在静态海流环境中，以能源最优为目标对粒子群算法进行改进，探索能源最优的路径规划算法。根据"驭浪者"号在线监测到的海流和风信息，得到随时间-空间变化的动态海流数据，对能源最优的路径规划算法进行优化，提出基于在线监测海流数据的能源最优路径规划算法。通过仿真试验，对比距离最优的路径规划算法和基于在线监测海流数据的能源最优路径规划算法在相同工况下能源消耗情况，验证所提出算法的可行性和有效性。
Energy-saving optimal path planning algorithm for natural energy-driven unmanned surface vehicle based on o-line monitoring current data
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.

driven by natural energyonline current dataenvironment modelingoptimal energypath planningparti-cle swarm optimization

李可、廖煜雷、刘骁锋、贾琪、李相杰、翟子正

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哈尔滨工程大学 智能海洋航行器技术全国重点实验室,黑龙江 哈尔滨 150001

哈尔滨工程大学 三亚南海创新发展基地,海南 三亚 572000

自然能驱动无人艇 在线海流数据 环境建模 能源最优 路径规划 粒子群算法

国家自然科学基金项目海南省自然科学基金项目智能海洋航行器技术全国重点实验室研究基金项目

52071097522MS1622024-HYHXQ-WDZC01

2024

10.11990/jheu.202209016

哈尔滨工程大学学报
哈尔滨工程大学

哈尔滨工程大学学报

CSTPCD北大核心
影响因子：0.655
ISSN：1006-7043
年,卷(期)：2024.45(3)
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