一个基于深度强化学习的人形控制策略,用于提高下肢康复机器人的舒适度
Yingrui Jin1, Jiahao Zhang2, Wei Li3
1School of Zhongyuan-Petersburg Aviation, Zhongyuan University of Technology, Zhengzhou, 450007, China. 5412@zut.edu.cn.
Scientific reports
|February 5, 2026
概括
这项研究引入了下肢康复机器人的新型人形控制策略,提高了训练效率和患者舒适度. 智能系统学习个人的步态模式,以获得个性化的康复体验.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物力学 生物力学
- 人工智能的人工智能
背景情况:
- 下肢康复机器人需要先进的控制,以便有效和舒适地训练患者.
- 现有的控制器往往缺乏适应个人人类步行特征的适应性.
研究的目的:
- 开发和评估用于下肢康复机器人的类人智能控制器.
- 通过个性化步行轨迹规划,提高机器人康复培训的有效性,人性化和舒适性.
主要方法:
- 使用拉格朗奇公式建立了下肢康复机器人的动力学和动态模型.
- 收集和处理健康受试者的步态数据,以计划参考关节角度轨迹.
- 提出了一种混合控制策略,整合了深度强化学习 (DRL) 和比例差异 (PD) 控制器.
主要成果:
- 开发的控制器成功地根据学习到的人类步行特征计划了个性化的步行轨迹.
- 模拟表明机器人控制器的智能和人类相似性得到了改善.
- 随着拟议的人形控制策略,观察到训练舒适度的显著提高.
结论:
- 整合DRL和PD控制为智能和自适应的下肢康复提供了一个有希望的方法.
- 这种人形控制策略个性化了步行训练,从而改善了康复结果和患者舒适度.
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