环境驱动无人船，如波浪滑翔机、saildrone等，由于自身低航速性、欠驱动性和弱机动性，导致其在大洋航行时受洋流干扰显著，甚至在部分强洋流海域出现失控的情况。本文基于先验已知的动态流场，通过B样条控制点随机生成无人船路径，并计算每一条路径在动态流场中考虑路径距离、航行速度以及路径光滑度的综合代价函数，使用遗传算法迭代搜索最优控制点组合。此外，考虑到大洋长航程路径规划中时间跨度长的特点，将出发时间作为路径优化参数，对比分析不同出发时间对行程用时的影响。仿真表明，所提出的路径规划策略能够有效提升大范围动态流场中欠驱动无人船的平均航速，缩短目标抵达时间。同时，在大范围长时序航行中，选择适当的出发时间可以进一步缩短行程用时。
Environmentally driver unmanned vehicles, such as wave gliders, saildrone, etc., due to their own low speed, underdrive and weak maneuverability, are significantly disturbed by ocean currents when navigating in the ocean, and even lose control in some sea areas with strong ocean currents. In this paper, based on the priori known dynamic flow field, the path of the unmanned ship is randomly generated by the B-spline control points. Then calculating the comprehensive cost function of each path, which includes path distance, navigation speed and path smoothness. And using the genetic algorithm iteratively searches for the optimal combination of control points. In addition, comparing and analyzing the impact of departure time on travel time, because of the characteristics of long voyage route planning in ocean. Simulation results show that the proposed path planning strategy can effectively increase the average speed of the unmanned vehicle in a large-scale dynamic flow field and shorten the target arrival time. At the same time, in the large-scale and long-sequence voyage, choosing an appropriate departure time can further shorten the travel time.
2024,46(14): 81-88 收稿日期：2023-09-15
DOI：10.3404/j.issn.1672-7649.2024.14.014
分类号：U675
基金项目：国家自然科学基金联合基金重点项目（U20A20328）
作者简介：廖年游(1998-),男,硕士研究生,研究方向为波浪滑翔机路径规划
参考文献：
[1] KULARATNE D, BHATTACHARYA S, HSIEH M A. Going with the flow: a graph based approach to optimal path planning in general flows[J]. Autonomous Robots, 2018, 42: 1369-1387.
[2] ALVAREZ A, CAITI A, ONKEN R. Evolutionary path planning for autonomous underwater vehicles in a variable ocean[J]. IEEE Journal of Oceanic Engineering, 2004, 29(2): 418-429.
[3] GARAU B, ALVAREZ A, OLIVER G. Path planning of autonomous underwater vehicles in current fields with complex spatial variability: an A* approach[C]//Proceedings of the 2005 IEEE international conference on robotics and automation. IEEE, 2005: 194–198.
[4] 姚绪梁, 王峰, 王景芳, 等. 一种时变洋流场下AUV最优能耗路径规划方法[J]. 控制与决策, 2020, 35(10): 2424-2432.
[5] 王斐然. 大规模复杂海洋环境下AUV自主决策系统研究[D]. 成都: 电子科技大学, 2019.
[6] KRUGER D, STOLKIN R, BLUM A, et al. Optimal AUV path planning for extended missions in complex, fast-flowing estuarine environments[C]//Proceedings 2007 IEEE International Conference on Robotics and Automation. Ieee, 2007, 14(10): 4265–4270.
[7] WITT J, DUNBABIN M. Go with the flow: Optimal AUV path planning in coastal environments[C]//Australian conference on robotics and automation, 2009.
[8] ZHANG S, SANG H, SUN X, et al. A multi-objective path planning method for the wave glider in the complex marine environment[J]. Ocean Engineering, 2022, 264: 112481.
[9] WANG P, WANG D, ZHANG X, et al. Path following control of the wave glider in waves and currents[J]. Ocean Engineering, 2019, 193: 106578.
[10] NGUYEN N T, GANGAVARAPU P T, KOMPE N F, et al. Navigation with polytopes: a toolbox for optimal path planning with polytope maps and b-spline curves[J]. Sensors, 2023, 23(7): 3532.
[11] 张鹏, 刘文芬. 基于B-样条曲线的AGV路径规划与控制[J]. 工业控制计算机, 2022, 35(10): 98-99.
[12] ZHAO J, ZHANG J, SHI Y, et al. Based on adaptive improved genetic algorithm of optimal path planning[C]//2022 7th International Conference on Multimedia and Image Processing. 2022: 225–230.
[13] 晏刚, 王力, 周俊, 等. 基于改进型遗传算法的AUV路径规划[J]. 重庆理工大学学报: 自然科学, 2010(5): 81-85.
[14] 徐玉如, 姚耀中. 考虑海流影响的水下机器人全局路径规划研究[J]. 中国造船, 2008, 49(4): 109-114.
