一个软的外骨架,用于部延伸和曲辅助,基于强化学习控制
Lei Sun1, Aofei Deng2, Hao Wang2
1School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, 300384, China. sunlei@email.tjut.edu.cn.
Scientific reports
|February 13, 2025
概括
一种新的强化学习方法增强了软外骨的控制力,用于部辅助,提高了适应各种环境和用户的适应性. 这种先进的控制可以将穿着者在步行时的代谢成本降低10%以上.
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:
- 传统的软外骨力量控制与环境和个人步态变化作斗争.
- 适应性控制对于在现实场景中优化外骨架辅助至关重要.
研究的目的:
- 开发和验证一种基于强化学习 (RL) 的控制方法,用于辅助部曲-延伸的软外骨架.
- 提高软外骨架的适应性和有效性,以帮助人类步行.
主要方法:
- 系统识别被用来建模外骨的运动控制系统.
- 在模拟环境中使用TD3 RL算法训练了一个自适应控制器.
- 控制器调整脉冲宽度调制 (PWM) 值以匹配所需的辅助力.
主要成果:
- 基于RL的控制器证明了对所需的辅助力配置文件的有效跟踪.
- 代谢实验显示,净代谢成本显著降低.
- 与关机模式相比,佩戴者在均表面下降了12.9%±3.3%,在斜坡上下降了10.7%±3.7%.
结论:
- 强化学习为软外骨控制提供了强大而适应性的解决方案.
- 拟议的RLPID方法有效地减少了步行期间的代谢需求,为用户展示了实际的好处.
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