适应在线学习和强大的3D形状服务连续和软机器人在非结构化的环境中
1Department of Mechanical and Automation Engineering, T Stone Robotics Institute, The Chinese University of Hong Kong, Shatin, Hong Kong.
Soft robotics
|February 7, 2024
概括
本研究引入了一种新的适应性控制方法,用于使用纤维布拉格格 (FBG) 传感器的软机器人. 数据驱动的方法使得精确的3D形状控制,即使在未知的机器人动态和干扰.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 生物医学工程 生物医学工程
背景情况:
- 开发用于外科手术的自主连续和软机器人需要先进的3D形状控制.
- 软机器人中的非线性特性和可变刚度存在重大建模挑战.
研究的目的:
- 为连续机器人和软机器人推出一种新的,数据驱动的,可适应的形状控制器.
- 利用纤维布拉格格网 (FBG) 传感器的自感知传感器进行实时形状估计和控制.
主要方法:
- 一个基于学习的自适应形状控制器,利用FBG传感器反进行3D配置.
- 一个复合的适应算法在线估计未知的机器人模型并适应干扰.
- 利亚普诺夫理论证明了闭环系统的非对称收.
主要成果:
- 在机器人辅助结肠镜和多截面软操纵器上证明了控制器的可行性和适应性.
- 在非结构化环境和幻影实验中验证了卓越的性能.
- 成功在线估计未知的机器人动态,并适应未建模的系统.
结论:
- 拟议的数据驱动方法为软机器人精确的3D形状控制提供了多功能解决方案.
- 适应式控制器在复杂的现实应用中提高了自主性和性能,例如机器人手术.
- 集成的FBG传感器提供了强大的自身感应,在具有挑战性的场景中提供有效的控制.
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