传感流量梯度是学习自主水下导航所必需的
Yusheng Jiao1, Haotian Hang1, Josh Merel2
1Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA.
Nature communications
|March 29, 2025
概括
机器人水下导航是通过使用自我中心的观察和试错学习来改进的. 感知局部流量梯度,而不仅仅是速度,是人工游泳者在没有外部参考的情况下导航的关键.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物模拟技术是生物模拟的
- 流体动力学 流体动力学
背景情况:
- 由于有限的全球定位信号和复杂的流动动力学,机器人车辆在水下导航中扎.
- 水生动物表现出优越的水下导航能力,这表明生物灵感的方法是有益的.
- 强化学习为开发适应性水下导航策略提供了一个有希望的途径.
研究的目的:
- 为了研究只使用船上传感器的人工游泳者自我中心的水下导航的可行性.
- 为了确定成功的自我中心导航所需的感觉信息 (流速与梯度).
- 探索在各种流环境中学习的导航政策的稳定性和可转移性.
主要方法:
- 一个人工游泳者被训练使用强化学习,以在不稳定的流动中导航到目的地.
- 游泳者完全依靠来自车载流量传感器的自我中心观测,没有地球中心的参考框架.
- 这项研究比较了仅使用局部流速的导航性能与结合局部流梯度的导航性能.
主要成果:
- 仅仅感知局部流速是不足以有效的自我中心导航.
- 在不稳定的流量中,成功的自我中心导航,将局部流量梯度纳入其中至关重要.
- 自我中心导航策略在不熟悉的流量条件下表现出旋转对称性和增强的稳定性.
结论:
- 在复杂的水生环境中以自我为中心的导航是可以通过适当的感官输入 (流梯度) 实现的.
- 这些发现支持了水生生物利用流量传感器来检测导航梯度的假设.
- 这项研究为开发更有能力,生物灵感的水下机器人提供了基础,并促进了机器人导航的转移学习.
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