基于学习的软机器人环境交互控制与力量/位置跟踪能力
Zhiqiang Tang1, Wenci Xin1, Peiyi Wang2
1Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore.
Soft robotics
|February 22, 2024
概括
这项研究引入了软机器人的新型数据驱动控制方法,使其能够与未知的环境进行精确的交互. 该方法结合了预测和学习控制器,用于强大的力量和位置跟踪.
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
- 机器学习 机器学习
背景情况:
- 软机器人提供安全的人与环境的互动,但由于复杂的动态和未知的环境,它们面临控制挑战.
- 开发软机器人的分析模型是困难的,阻碍了有效的控制策略.
研究的目的:
- 为软机器人环境交互提出基于学习的最佳控制方法.
- 应对非线性动态,未知的环境和不确定性所带来的挑战.
主要方法:
- 输送控制器 (概率模型预测控制) 和反控制器 (非参数学习方法) 的优化组合.
- 这是一种纯粹基于数据的方法,不需要对机器人动态或环境结构的先验知识.
- 在线更新功能,适应未知的环境.
主要成果:
- 拟议的方法通过理论分析证明了稳定性和收性.
- 一个柔软的机器人操纵器成功地跟踪了目标位置和力量在与人形的相互作用.
- 在比较测试中表现优于其他数据驱动的控制方法.
结论:
- 这项工作为软机器人环境交互提供了一种可行的基于学习的控制方法.
- 该方法为软机器人系统提供了强大的力量和位置跟踪能力.
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