人工物理引擎用于实时反向动态的手臂和手的运动
Mykhailo Manukian1, Serhii Bahdasariants2, Sergiy Yakovenko2,3,4,5,6
1Faculty of Applied Science, Ukrainian Catholic University, Lviv, Ukraine.
PloS one
|December 13, 2023
概括
机器学习加速了人体动力学模拟. 人工神经网络 (ANN) 准确预测四肢动态,使实时辅助技术控制成为可能.
科学领域:
- 生物力学 生物力学
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 精确的人体动力学模拟对于辅助技术至关重要.
- 模型的复杂性往往限制了计算速度,需要近似.
- 特定对象的参数化提高了模拟的准确性.
研究的目的:
- 开发一种机器学习方法,以快速准确地对人类四肢动力学进行物理模拟.
- 为了使逆动态模型能够进行特定对象的参数化.
- 为了降低计算复杂性,同时保持高精度.
主要方法:
- 利用了具有多种架构的人工神经网络 (ANN).
- 使用物理模型生成了现实的手臂和手部运动 (23度自由度) 的数据集.
- 训练有素的ANN可以执行反向动态转换,并解决动力学雅可比式.
主要成果:
- ANN 实现了低扭矩误差 (小于 0.1 Nm).
- 成功解决了高维动力学雅可比式和手臂和手部运动的反向动力学.
- 使用时间延迟值的时间轨迹证明了强大而准确的预测.
结论:
- 在生物力学中,ANN为加速复杂物理模拟提供了有效的解决方案.
- 这种方法有助于实时控制辅助技术,如骨架和肌肉/神经刺激器.
- 机器学习提高了特定学科的人类动态建模的准确性和效率.
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