为更好地完成水下探测任务, 提升潜行效率, 本文提出了自主水下航行器 (autonomous underwater vehicle, AUV)在可变洋流中的全局路径规划方法. 首先, 识别潜行区域地形与静态障碍物, 建立以三维空 间路径最短与路径平滑度最大为约束的多目标函数, 利用提出的改进QPSO算法求解, 生成初始路径; 其 次, 考虑水下环境中不确定障碍物的存在和时变洋流的干扰, 将动态障碍物信息更新在控制图上, 用高斯 噪声对洋流速度进行估计, 确保AUV实现动态躲避与适应洋流变化以输出稳定的速度; 最后, 建立观测 与惩罚函数来实时调整初始路径, 得到更为科学合理的潜行路径. 仿真结果表明, 本文提出的全局规划方 法求解的路径能使AUV潜行更加平稳与安全, 使其具有更好的自主能力; 所提改进算法与常规算法对比, 改进的QPSO算法求解多目标离散问题所得解的精度与质量更好.
Abstract
To improve the work efficiency of underwater detection by AUV, a global path planning method is proposed for AUV in variable currents. Firstly, the terrain and static obstacles are identified, and the multi-objective function is established, which is constrained by the shortest and maximum smoothness of the path in the 3D space. The improved QPSO algorithm is used to solve the problem and generate the initial path. Secondly, considering the existence of uncertain obstacles in underwater environment and disturbance of time-varying currents, the dynamic obstacle information is updated on the known map, and the velocity vector of ocean current is estimated by using Gaussian noise, so that ensure that the AUV adapts to the change of the ocean current to output the appropriate speed. Finally, the observation and penalty function are established to adjust the initial path in real-time and get a more scientific and reasonable path. The simulation results show that the path planning method can make the AUV stealthy more stable and autonomous. Comparing with the conventional algorithm, the proposed algorithm is better than other algorithm in the accuracy and quality of the solution.
关键词
复杂水下环境 /
路径规划 /
改进QPSO算法 /
混合编程
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Key words
complex underwater environment /
path planning /
improved QPSO algorithm /
hybrid programming
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脚注
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基金
国家自然科学基金(71971035, 71572022);大连海事大学"双一流"建设专项资金(BSCXXM017)
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