强化学习启用控制和设计的刚链机器人鱼:一个全面的审查
Nhat Dinh1, Darion Vosbein1, Yuehua Wang2
1Smart Devices and Intelligent Systems Laboratory, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
自主机器人鱼,特别是刚链鱼机器人 (RLFRs),提供高效的水下勘探. 本综述详细介绍了它们的结构设计和强化学习控制,强调了未来发展的进步和挑战.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 海洋工程 海洋工程
- 人工智能的人工智能
背景情况:
- 自主机器人鱼在海上调查,基础设施检查和环境监测方面越来越重要.
- 灵感来源于鱼的刚链鱼类机器人 (RLFR),提供仿生推进,敏捷的运动和高效的水下操作.
- 它们的模块化设计促进了成本效益和易于组装,使它们适合复杂的水生环境.
研究的目的:
- 审查刚性环节鱼类机器人 (RLFRs) 结构设计的最新进展.
- 检查强化学习 (RL) 对RLFR控制系统 (包括传感器和执行器) 的应用.
- 确定RLFR技术当前的技术差距和未来的研究方向.
主要方法:
- 基于联合配置的现有RLFR设计的分类.
- 总结结构设计中的关键考虑因素:材料,制造和推进.
- 对用于RLFR控制的强化学习算法 (Q-learning,DQN,DDPG) 的分析.
主要成果:
- 通过波浪式运动,RLFR表现出高度的机动性和高效的机动性.
- 强化学习算法在动态水力动态条件下提高RLFR适应性和运动控制.
- 确定了影响RLFR性能的主要结构,材料,制造和推进因素.
结论:
- 刚链鱼类机器人 (RLFR) 是自主水下系统的重大进步.
- 强化学习为控制RLFR在复杂环境中提供了强大的能力.
- 需要进一步的研究来应对非结构化环境和流体体相互作用的挑战,以提高RLFR性能.
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