通过超点-SIFT融合和有限元分析进行无标记力估计:用于可变形物体操纵的无传感器解决方案.
Qingqing Xu1,2, Ruoyang Lai3, Junqing Yin3
1School of Mechanical and Electrical Engineering, Suqian University, Suqian 223800, China.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
本研究介绍了一种无传感器方法,让机器人使用视觉数据和有限元分析来感知外部力量. 这种方法克服了传统传感器的局限性,实现了更安全,更灵活的机器人抓取和交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 机械工程 机械工程
背景情况:
- 安全的机器人抓取依赖于准确的接触力感知.
- 传统的力传感器对先进的人形机器人有局限性,阻碍了仿生应用.
- 现有的传感器往往是复杂的,难以安装,并阻碍机器人的灵活性.
研究的目的:
- 为机器人应用提出一种新的无传感器外力检测方法.
- 为了解决人形机器人中常规力传感器的局限性.
- 为了使机器人能够精确地感知力,以实现机器人的自然相互作用和敏捷运动.
主要方法:
- 集成超点尺度不变特征转换 (SIFT) 用于特征提取.
- 使用SuperPoint-SIFT功能融合的3D位移场的重建.
- 绘制位移场,通过有限元模型 (FEM) 来接触力分布.
- 开发双通道视频比较框架,以提高可靠性.
主要成果:
- 达到平均力估计误差为7.60% (同位热) 和8.15% (异位热).
- 根平均平方误差 (RMSE) 使用灵活的压力传感器被验证为低于8%.
- 通过分析变形一致性,双通道框架证实了模型可靠性.
结论:
- 拟议的无传感器方法为传统的力传感器提供了可行的替代方案.
- 这种方法可以实时感知力,这对于精密组装和医疗机器人技术至关重要.
- 该研究通过增强的无感应力反来推进机器人交互能力.
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