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基于卷积神经网络的姿势映射估计作为传统手眼校准的替代方案

Kuai Zhou¹, Xiang Huang¹, Shuanggao Li¹

¹College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

The Review of scientific instruments

|October 20, 2023

概括

这项研究引入了一种新的手眼校准方法,用于工业机器人使用姿势估计卷积神经网络 (PECNN). 这种方法提高了视觉引导并行机器人在自动装配任务中的准确性.

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
人工智能的人工智能

背景情况:

工业机器人自动化严重依赖于视觉系统.
精确的手眼校准对于机器人端效应器和摄像机关系的确定至关重要.
传统的校准方法面临着与平行机器人运动范围和精度的局限性.

研究的目的:

为平行机器人开发一种强大的手眼校准方法.
为解决有限运动的机器人传统校准技术中的准确性问题.
为了实现精确的视觉引导的自动装配任务.

主要方法:

提出了一种姿势,非线性映射估计方法用于手眼校准.
开发了一个1D姿势估计卷积神经网络 (PECNN).
通过PECNN,可以从目标物体的姿势变化到机器人端的姿势变化进行端到端的映射.

主要成果:

基于PECNN的手眼校准方法显示了高精度.
实验验证证了该方法的有效性.
这种方法适用于自动装配中的视觉引导并行机器人.

结论:

提出的手眼校准方法是准确的,适用于并行机器人.
该方法显示了提高视觉引导机器人的自动装配的前景.
该技术可以适应大多数并行和并联机器人.

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