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相关实验视频

Updated: May 26, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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一种整体的间接接触识别方法,用于软机器人自身感知.

Shuoqi Wang1, Keng-Yu Lin2, Xiangru Xu1

  • 1Department of Mechanical Engineering, University of Wisconsin, Madison, Madison, Wisconsin, USA.

Soft robotics
|February 24, 2025
PubMed
概括

研究人员开发了一种新方法,使软机器人能够感知自己的姿势,即使被外部力量变形. 这通过了解曲和接触相互作用来改善符合规范的机器人的控制.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 材料科学 材料科学 材料科学
  • 控制系统 控制系统

背景情况:

  • 软机器人提供了优势,但由于其固有的合规性和反向驱动性,存在控制挑战.
  • 精确感知机器人姿势 (位置和方向) 对于有效控制至关重要,特别是在扰乱的条件下.
  • 现有的方法经常与软机器人特有的复杂变形和外部接触作斗争.

研究的目的:

  • 开发一种全面的方法来感知软机器人的姿势在自由曲和外部接触干扰下.
  • 通过准确估计软机器人系统的变形状态,实现对软机器人系统的精确控制.
  • 为软执行器提供一种可通用的接触识别方法,并估计软执行器中的位置.

主要方法:

  • 使用多个软应变传感器,与接触点不结合,以测量变形.
  • 开发了一个基于数据的模型来描述自由曲变形模式 (前,侧,扭).
  • 通过比较从未受到干扰状态的传感器偏差来估计位置来实现扰乱感知方法.

主要成果:

  • 成功描述了传感器对各种变形类型的响应在2度自由度的软手指上.
  • 证明了在单指和双指软机器人抓手上感知扰动姿势的能力.
  • 验证了整体接触识别和姿势感知方法的有效性.

结论:

  • 拟议的整体方法使软机器人能够进行强大的姿势感知,即使存在外部干扰和接触.
  • 这种方法是可通用的,对于推进符合规范的机器人系统的控制能力至关重要.
  • 改进的姿势感知直接解决了软机器人的实际应用的一个关键局限性.
关键词:
具有嵌入式传感器的执行器.间接接触传感器是一种间接接触传感器.多方向的PneuNet执行器构成估计估计的估计.

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