通过强化学习模拟二维翼的生物动动作
Ran Bi1, Changdong Zheng1, Hongyu Zheng2
1School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, People's Republic of China.
Bioinspiration & biomimetics
|April 16, 2025
概括
这项研究使用强化学习来优化飞的翅膀运动,发现复杂的,高效的模式,以大自然为灵感. 这种方法提高了生物灵感的推进系统超越传统方法.
科学领域:
- 生物启发的工程是生物启发的.
- 机器人技术 机器人技术 机器人技术
- 流体动力学 流体动力学
背景情况:
- 自然机动 (鸟类,昆虫,蝙蝠,鱼) 通过自适应的动表现出高效率.
- 现有的生物启发推进研究经常使用简化的模型,限制了捕捉自然运动的复杂性和适应性.
研究的目的:
- 使用强化学习 (RL) 开发一种自适应运动优化框架.
- 为了克服简化模型和预先设计的运动假设在生物启发推进的局限性.
- 发现和完善复杂,高效的鼓动动作,灵感来自生物系统.
主要方法:
- 集成高准确度的数值模拟与物理的飞翔机翼模型.
- 由流场信息指导的运动模式的实时动态调整.
- 使用强化学习的代探索,以发现非和的,准周期的运动模式.
主要成果:
- 学习的动作表现出生物相关的特征,如不对称的振荡和自适应的节奏形成.
- 该框架成功地提高了推进性能和适应动态流量条件的适应性.
- 发现的运动策略通过探索更广泛的复杂运动来超越传统的优化.
结论:
- 强化学习可以发现复杂的,生物灵感的运动动态.
- 拟议的框架为了解自然动机制提供了变革性的潜力.
- 这种方法为设计更高效和多功能生物灵感推进系统铺平了道路.
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