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使用非线性自回归神经网络与外源输入用于脚轨迹生成,用于上坡地形
Hamza Al Kouzbary1, Mouaz Al Kouzbary2, Jingjing Liu3
1Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
概括
这项研究表明,神经网络如何生成机器人假肢脚的轨迹,以便在没有事先训练的情况下在具有挑战性的上坡地形上进行挑战. 这一进步为用户提供了更具适应性和响应性的下肢假肢.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 机器人假肢比被动假肢具有优势,包括降低代谢成本和适应性地形谈判.
- 机器人假肢的传统控制器因其静态性而受到限制,并且需要广泛的用户特定校准.
- 人工智能的进步为动态和自适应的假肢控制提供了潜在的解决方案.
研究的目的:
- 评估非线性自回归循环神经网络与外源输入 (NARX) 在生成假肢脚的轨迹的能力.
- 在未经训练的,具有挑战性的地形 (10°倾斜) 上测试NARX网络的性能.
- 评估AI在创建自适应机器人下肢假肢方面的潜力.
主要方法:
- 使用了带有外源输入 (NARX) 的非线性自回归循环神经网络.
- 在现有数据上训练NARX网络,然后在10°上坡的地形上进行测试.
- 用6名身体健康的受试者的数据来评估表现.
主要成果:
- 在没有重新训练的情况下,NARX网络成功估计了新的上坡地形的足迹轨迹.
- 所有受试者的平均根平均平方误差 (RMSE) 为2.953°.
- 这表明网络能够对未知的环境条件进行概括.
结论:
- 纳克斯神经网络显示出对机器人假肢实时适应的重大前景.
- 这种人工智能驱动的方法可以克服传统控制器的局限性,使假肢功能更加自然.
- 进一步的开发可能会导致假肢无地适应不同的地形和用户需求.
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