改进动力外骨架的任务无意识的能量塑造控制,使用任务/步态分类
Jianping Lin1, Robert D Gregg2, Peter B Shull1
1State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
IEEE robotics and automation letters
|September 30, 2024
概括
这项研究引入了一种新的动力外骨控制方法,该方法将能量塑造与机器学习结合起来. 它优化了对不同任务和用户的帮助,改善了自然主义的运动和稳定性.
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
- 机器学习 机器学习
背景情况:
- 动力外骨架的任务无关控制方法提供了多功能性,但通常会对特定任务或用户的性能产生影响.
- 现有的能量塑造控制器提供了稳定性,但可能无法最佳地适应个人用户的需求或各种日常活动.
研究的目的:
- 开发和评估一项针对动力外骨的新型控制策略,该策略将能量塑造与机器学习分类器集成在一起.
- 在没有明确的任务检测的情况下,在各种任务和用户之间提供最佳的,个性化的帮助,增强自然主义的运动.
主要方法:
- 开发了一种机器学习分类器,以检测多个任务和步态模式之间的过渡.
- 这个分类器可以从预先优化的能量塑造控制器的加权和中选择一个优化的,任务无关的控制器.
- 在各种任务中进行了in-silico评估,包括倾斜行走,楼梯谈判和坐立过渡.
主要成果:
- 综合控制方法在5倍交叉验证中表现优于基准方法.
- 该方法在各种任务和用户之间实现了93.17 ± 7.39%的等号相似性和77.92 ± 19.76%的差异.
- 结果表明,在调整外骨辅助与人类关节动作时,具有显著的适应能力.
结论:
- 拟议的混合控制策略通过适应个人用户和任务,有效地提高动力外骨的性能.
- 这种方法为可穿戴机器人系统中的多功能和自然的人类辅助提供了一个有希望的解决方案.
- 进一步的研究可以探索这种自适应控制系统的现实世界实施和改进.
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