基于拉格朗跟踪的自主水下车辆的长期漂移轨迹预测方法
Shuwen Zheng1, Mingjun Zhang1, Jing Zhang2
1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.
Mathematical biosciences and engineering : MBE
|December 21, 2023
概括
本研究提出了一种新方法,用于预测自动水下车辆 (AUV) 在失去功率后的漂移轨迹. 该方法准确地预测了AUV的情况.
科学领域:
- 海洋机器人 海洋机器人
- 海洋学 海洋学 海洋学
- 水下航行水下航行
背景情况:
- 自主水下车辆 (AUV) 在海洋环境中面临着挑战.
- 电力损失会导致AUV失去浮力和漂移,特别是在热线中.
- 目前用于预测长期AUV漂移的方法不足.
研究的目的:
- 开发一个AUV漂移轨迹在长期停电后的预测方法.
- 预测失效的自动驾驶汽车的潜在复苏位置.
- 为搜索和救援行动提供技术支持.
主要方法:
- 提出了使用拉格朗日跟踪方法的三维轨迹预测方法.
- 集成的AUV纵向速度,上升时间和海洋电流数据.
- 开发了一种用于估计热线电流的方法,以预测横向漂移.
主要成果:
- 拟议的方法准确地预测了AUV漂移轨迹.
- 与真实事故数据相比,模拟显示了小的方向和位置错误.
- 验证了轨迹预测方法的有效性.
结论:
- 开发的方法提供了一种可靠的方法,可以预测AUV在失去电源后漂移.
- 这有助于定位失效的AUV,提高救援效率.
- 解决了当前AUV事故响应能力的关键缺口.
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