符合标准的抓手的接触力测量基于强力传感模型
Yunsong Du1, Pengwei Zhang1, Tiemin Li2
1College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
The Review of scientific instruments
|December 11, 2024
概括
这项研究引入了一种新的符合规范的抓手,配备了一种力感应模型,用于准确测量非结构化物体上的接触力. 这种新的方法使用深度神经网络来提高力感应精度,达到不到3%的误差.
科学领域:
- 机器人与机械工程 机器人与机械工程
- 人工智能和机器学习
背景情况:
- 准确的接触力测量对现有的抓器来说是一个重大挑战,特别是对于非结构化的物体.
- 当前的方法往往缺乏精度,阻碍了微妙的操作任务和对象完整性.
研究的目的:
- 为符合标准的抓柄开发一种新型的力传感模型,以准确测量接触力.
- 为了提高机器人抓手在与可变形或不规则物体相互作用时的力感应精度.
主要方法:
- 使用拓优化设计了一个符合标准的抓手.
- 使用OpenCV进行了非接触变形测量.
- 通过单轴压缩测试研究了一个超弹性构成模型.
- 使用线性有限元理论和深度神经网络 (DNN) 算法建立了一个力感应模型.
- 用DNN来弥补实际和理论测量之间的非线性变形误差.
主要成果:
- 这种新型的力传感模型在符合标准的抓手的中间部分实现了高精度 (平均误差<3%).
- 抓手端的力感应精度下降,但该模型的有效性在中央区域得到验证.
- DNN算法成功地弥补了非线性错误,提高了理论变形的准确性.
结论:
- 开发的强力传感模型与符合规定的抓柄和DNN集成,有效地测量非结构化的物体上的接触力.
- 这种方法为提高机器人操纵精度和安全提供了一个有希望的解决方案.
- 进一步的研究可能将重点放在提高抓手端的精度上.
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