GripDepthSense3DNet:在软机器人抓取中使用深度启用硬度感应框架.
Ting Rang Ling1, Bryan Jun Sheng Lee2, Chee Pin Tan1
1Department of Electrical and Robotics Engineering, School of Engineering, Monash University Malaysia, Subang Jaya, Malaysia.
Soft robotics
|April 15, 2025
概括
这项研究介绍了GripDepthSense3DNet,一种机器学习模型,使用3D深度传感来准确地检测抓取过程中对象的硬度. 这种新型网络有效地感知可变形物体的硬度,优于现有的方法.
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
- 机器学习 机器学习
背景情况:
- 软抓柄对于处理可变形物体至关重要,但准确的硬度感应仍然是一个重大挑战.
- 硬度感应对于果实成熟度评估,食品质量控制和产品分类等应用至关重要.
研究的目的:
- 开发一种创新的方法,使用3D深度传感和机器学习在抓取过程中准确地检测硬度.
- 介绍GripDepthSense3DNet,一个能够捕捉空间时间变形特征的新型网络,用于硬度估计.
主要方法:
- 一个名为GripDepthSense3DNet的新型网络被开发出来,它将3D深度感应与机器学习相结合.
- 该网络是通过捕捉对象变形的深度图像数据集进行训练的.
- 从一系列深度图像中提取了时空变形特征,以估计硬度.
主要成果:
- GripDepthSense3DNet在训练的形状和硬度方面实现了0.46%的平均绝对百分比误差.
- 该模型显示出显著的效率,与ResNet-50.0相比,参数减少了94.8%,训练时间缩短了92.9%.
- 通过系统研究确定了最佳的深度范围和间隔.
结论:
- GripDepthSense3DNet为可变形物体的硬度感应提供了一个高度准确和高效的解决方案.
- 网络的动态调整能力允许无地适应新的形状,硬度水平和任意对象,展示其多功能性.
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