基于运动动力学来预测物体属性
Lena Kopnarski1, Laura Lippert2, Julian Rudisch1
1Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of Münster, Wilhelm-Schickard-Str. 8, 48149, Münster, Germany.
Brain informatics
|November 5, 2023
概括
机器人现在可以通过分析手臂运动来估计物体的重量,减少对先前学习的依赖,以抓住各种物体. 这种方法准确地预测了运动早期的重量,改善了机器人操纵.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 生物力学 生物力学
背景情况:
- 抓住和运输物体需要根据物体属性 (如重量) 调整抓地力和负载力.
- 目前用于体重估计的方法,例如机器人图像识别,往往严重依赖于先前的学习.
- 对于机器人来说,需要一种不那么依赖于经验的方法来处理各种各样的物体.
研究的目的:
- 评估使用上肢动力学或物体速度配置文件预测物体的重量级的可行性.
- 调查时间序列长度和交叉验证程序对预测准确性的影响.
- 开发一种不太依赖于先前学习的机器人体重估计方法.
主要方法:
- 记录了12名参与者的运动动力学,他们用不同,未知重量的物体执行了替代任务.
- 使用光学运动跟踪系统捕捉身体上部的角度.
- 适用于时间序列平滑/压缩和支持向量机器的离散等号变换,用于监督的重量级预测.
主要成果:
- 在受交叉验证程序和时间序列长度影响的情况下,对象重量类的预测准确度很好.
- 可靠的体重预测在运动的早期是可能的 (在300 ms内).
- 拟议的方法在没有广泛的先前学习的情况下,在估计物体重量方面表现出有效性.
结论:
- 预测物体重量级从上肢动力学在更换任务期间是可行的.
- 该方法为机器人的传统,经验依赖的重量估计方法提供了一个有希望的替代方案.
- 这种技术增强了机器人抓取和操纵各种物体的能力.
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