在多种约束下,生物启发的同时学习和运动力混合控制用于机器人操纵器
Yuchuang Tong1, Haotian Liu1, Zhengtao Zhang1,2
1CAS Engineering Laboratory for Intelligent Industrial Vision, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Biomimetics (Basel, Switzerland)
|December 24, 2025
概括
本研究介绍了一种基于生物启发的学习的机器人运动力混合控制 (LMFC) 框架. 它通过统一学习和动力控制,在受限制的环境中实现精确,合规的控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 生物启发系统 生物启发系统
背景情况:
- 生物系统表现出适应性运动协调.
- 机器人操纵器需要精确和合规的控制才能进行灵巧的交互.
- 物理限制的环境对机器人控制构成了挑战.
研究的目的:
- 开发一种生物启发的控制策略,用于精确且符合机器人操纵器的运动力协调.
- 在受限制的环境中增强机器人的强度和精度.
- 为了实现体现的智能和灵巧的互动.
主要方法:
- 提出了一个基于学习的运动力混合控制 (LMFC) 框架.
- 作为一个时间变化的二次编程 (TVQP) 问题,制定了运动力协调.
- 集成了一个基于RNN的控制器,用于自适应式学习和在线参数估计.
主要成果:
- 在不完整的动力信息下,LMFC框架有效调节运动和相互作用力.
- 实际限制 (联合限制,方向,障碍物) 被纳入加速水平.
- RNN控制器缓解了关节漂移,并在线估计了不确定的动力学参数.
结论:
- 拟议的LMFC框架证明了自适应和合规的机器人控制的有效性和实用性.
- 这种方法可以提高机器人的能力,在限制丰富的环境中.
- 生物启发的策略为先进的机器人应用提供了巨大的潜力.
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