通过基于指导矢量场的路径集成控制,增强了自动水下车辆的障碍回避能力
Jintao Zhao1, Tao Liu2, Junhao Huang1
1School of Ocean Engineering and Technology, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China.
ISA transactions
|July 15, 2025
概括
本研究引入了自动水下车辆 (AUV) 的新型导航方法,通过整合指导向量场 (GVF) 和模型预测路径集成 (MPPI) 控制,显著提高在复杂的水下环境中避开障碍物和路径跟踪.
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
- 控制系统 控制系统
背景情况:
- 自动水下车辆 (AUV) 需要先进的导航来完成复杂的任务.
- 挑战包括精确的跟踪,实时导航和在动态的水下环境中避开障碍物.
研究的目的:
- 开发和验证AUV的新导航技术.
- 提高AUV在具有挑战性的水下环境中的操作精度和效率.
主要方法:
- 整合指导向量场 (GVF) 概念,用于全球路径规划和障碍回避.
- 模型预测路径集成 (MPPI) 控制的应用,用于精确的轨迹跟踪和动态约束管理.
- 利用AUV相对定位和环境数据用于GVF生成和MPPI优化.
主要成果:
- 实现了64%的路径跟踪错误的减少.
- 成功导航复杂的场景与非凸的障碍物,同时保持安全距离.
- 在干扰条件下表现出强大的性能,跟踪误差最小为0.017微米.
结论:
- 结合的GVF和MPPI方法有效地将全球规划与AUV导航的本地优化相结合.
- 这种方法提高了AUV在复杂的水下环境中的效率,安全性和可靠性.
- 这项研究提升了AUV的功能,用于诸如海洋勘测和水下搜救等应用.
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