强化学习用于在各种流体环境中对鱼类类物质进行可靠的导航
Jin Zhang1, Xiaolong Chen1, Bochao Cao1
1Department of Aeronautics and Astronautics & College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, People's Republic of China.
Bioinspiration & biomimetics
|September 30, 2025
概括
本研究介绍了生物灵感水下机器人的强化学习框架,以实现节能导航. 人工智能学习了强大的游泳策略,可以适应各种流体条件,并显示出高的成功率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 流体动力学 流体动力学
背景情况:
- 水下机器人在未知的流体环境中面临自主导航和能源效率方面的挑战.
- 生物灵感设计为改进机器人机动和适应性提供了潜力.
研究的目的:
- 为类似鱼类的水下机器人开发强化学习 (RL) 控制框架.
- 允许在复杂的流体动态中自主获取节能导航策略.
主要方法:
- 使用高保真计算流体动力学 (CFD) 环境进行训练.
- 实施了奖励功能,优先考虑能源效率,以指导RL代理.
- 训练了一名代理人,在没有先前的流体力学知识的情况下发现各种运动模式.
主要成果:
- RL代理自发地发展了各种步态,包括爆发和爆发和海岸运动.
- 学习的游泳政策在不同的导航任务中得到了有效的概括.
- 证明了对流动干扰的强度,如均电流和旋唤醒.
- 在所有测试的场景中实现了100%的导航成功率.
结论:
- 将基于物理的模拟与RL控制相结合,提高了水上机器人的设计.
- 这种方法产生了适应性,效率和弹性的生物灵感的水下机器人.
- 该框架显示了推动自主水下车辆能力的前景.
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