优化时间的全球路径规划和避免碰撞的局部路径规划,用于在交通分隔方案实施的沿海水域中的USV
1School of Marine Electrical Engineering, Dalian Maritime University, No.1, Linghai Road, Dalian, 116026, Liaoning, China.
ISA transactions
|July 5, 2025
概括
本研究介绍了一种基于高斯过程的非线性编程 (GPNLP) 方法,用于无人驾驶表面车辆 (USV) 路径规划. 该方法确保时间最佳的全球和避免碰撞的本地路径,同时遵守交通分隔方案和USV动态.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 海洋工程 海洋工程
- 人工智能的人工智能
背景情况:
- 由于多种限制,在带有交通分隔方案 (TSS) 的沿海水域,无人驾驶地表车辆 (USV) 的路线规划是复杂的.
- 现有的方法难以平衡时间优化,避免碰撞和遵守TSS要求.
研究的目的:
- 开发一种创新的层次方法,用于为USVs进行时间最佳的全球路径规划和避免碰撞 (COLAV) 的本地路径规划.
- 有效管理包括USV动态,TSS规则,可航水界限和碰撞风险在内的约束因素.
主要方法:
- 开发了一种基于高斯过程的等级非线性编程 (GPNLP) 方法.
- 用高斯过程回归来建模不规则的静态障碍物,优化可航水利用.
- 整合了TSS合规性评估功能,以惩罚TSS违规行为.
主要成果:
- 拟议的GPNLP方法成功地为USVs规划了最佳时间的全球路径和COLAV本地路径.
- 计划中的路径在模拟中显示符合USV动态和TSS要求.
- 有效地模拟了不规则的障碍物,提高了可航行水的利用率.
结论:
- 层次GPNLP方法为复杂的TSS实施水域的USV路径规划提供了强大的解决方案.
- 这种方法通过确保动态遵守和遵守交通法规来提高安全性和效率.
- 对障碍物建模的高斯过程回归的创新性使用改善了路径规划结果.
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