探索使用深度强化学习学习的风暴和海
Jiaqi Xue1,2,3, Fei Han1,2, Brett Klaassen van Oorschot4
1Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, People's Republic of China.
Bioinspiration & biomimetics
|October 5, 2023
概括
这项研究模拟了风暴的运动模式,揭示了它们独特的动作方式.
科学领域:
- 机器人学和生物模拟学
- 流体动力学 流体动力学
- 鸟类的机车运动
背景情况:
- 混合空水车辆对于环境监测和救援行动非常有价值.
- 风暴在水面上表现出独特的"拍拍"或"海"运动.
- 了解这种行为可以为仿生机器人的设计提供信息.
研究的目的:
- 开发一款风暴鱼运动的计算模型.
- 为了研究风暴的生物力学"拍拍"行为.
- 为了指导空气-水接口任务的 petrel-inspired 机器人的设计.
主要方法:
- 三种风暴的形态测量.
- 发展空气/水力动力学模型和计算风暴鸟模型.
- 深度强化学习在混合流体环境中的应用.
主要成果:
- 风暴模型在各种风速中表现出高度的机动性和稳定性.
- 该模型成功地利用"图形"来支持重量,运动和稳定.
- 风暴的运动模式适应增加风速和不同的优化目标.
结论:
- 风暴鱼通过适应性"纹"行为在空气-水界面实现高效的运动和动态稳定.
- 这项研究提供了对鸟类生物力学和在开放环境中的行为的见解.
- 这些发现将有助于开发生物模拟机器人,用于水面操作.
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