Multi-objective path planning for unmanned vessels in deep-sea fisheries under time-varying current disturbance
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摘要: 深远海无人船在开发渔业资源时,面临着续航能力不足和路径规划算法收敛慢、精度低等问题,为尽可能减少渔业无人船在实际任务执行过程中环境影响和最大限度地优化航行路线,在保证其安全航行的前提下,设计了以路径长度、转舵和海流能耗等多个参数最小为目标的路径规划算法。通过对无人船在航行时海域环境和任务目标的分析,建立了时变海流干扰下的无人船多目标计算模型,采用改进的自适应灰狼优化算法进行求解,算法通过引入多项策略进行统筹优化。该算法应用于复杂水域下渔业无人船多目标优化领域的仿真实验,证实了算法的可行性和改进策略的有效性,多目标相较于3个单目标仿真结果对总目标值的优化率分别提高了9.2%、1.7%、11.9%；不同海流状态下的仿真路径表明了相较于传统的以距离最优算法能够节省更多的成本,有效地提高了无人船全局航迹的规划性能。Abstract: The deep-sea unmanned vessel faces the problems of insufficient range and slow convergence and low accuracy of path planning algorithm when exploiting fishery resources.For the purpose of minimizing the environmental impact and optimizing navigation routes of unmanned fishing vessels during the actual mission execution of the unmanned vessel in fisheries, a path planning algorithm with the objective of minimizing multiple parameters such as path length, smooth and current energy was designed under the premise of ensuring its safe navigation.The multi-objective computational model of unmanned vessel under time-varying current disturbance was established by analyzing the ocean environment and mission objectives of the unmanned ship during navigation, and an improved adaptive gray wolf optimization algorithm was employed to settle the problem, which is integrated optimization by introducing multiple strategies. Eventually, the feasibility of the algorithm in the field of multi-objective optimization of unmanned surface vehicles, as well as the usefulness of the improved strategy can be confirmed by simulation experiments. The optimization rate of total objective value was improved by 9.2%, 1.7% and 11.9% for multi-objective compared to three single-objective simulation results. The simulation paths under different current conditions show that the distance-optimal algorithm can save more cost than the traditional distance-optimal algorithm and effectively improve the performance of global trajectory planning.
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