文献类型： 外文期刊

作者： Chen, Shitu ¹ ; Feng, Ling ¹ ; Bao, Xuteng ¹ ; Jiang, Zhe ¹ ; Xing, Bowen ¹ ; Xu, Jingxiang ¹ ;

作者机构： 1.Shanghai Ocean Univ, Coll Engn Sci & Technol, Shanghai 201306, Peoples R China

2.Chinese Acad Fishery Sci, Fishery Machinery & Instrument Res Inst, Shanghai 200092, Peoples R China

关键词： unmanned surface vehicles; dynamic obstacle; water currents; eight-directional current resistance; path smoothness

期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.9； 五年影响因子：2.9 ）

ISSN：

年卷期： 2024 年 12 卷 3 期

页码：

收录情况： SCI

摘要： Path planning is crucial for unmanned surface vehicles (USVs) to navigate and avoid obstacles efficiently. This study evaluates and contrasts various USV path-planning algorithms, focusing on their effectiveness in dynamic obstacle avoidance, resistance to water currents, and path smoothness. Meanwhile, this research introduces a novel collective intelligence algorithm tailored for two-dimensional environments, integrating dynamic obstacle avoidance and smooth path optimization. The approach tackles the global-path-planning challenge, specifically accounting for moving obstacles and current influences. The algorithm adeptly combines strategies for dynamic obstacle circumvention with an eight-directional current resistance approach, ensuring locally optimal paths that minimize the impact of currents on navigation. Additionally, advanced artificial bee colony algorithms were used during the research process to enhance the method and improve the smoothness of the generated path. Simulation results have verified the superiority of the algorithm in improving the quality of USV path planning. Compared with traditional bee colony algorithms, the improved algorithm increased the length of the optimization path by 8%, shortened the optimization time by 50%, and achieved almost 100% avoidance of dynamic obstacles.