在小型人工智能驱动的计算系统和用于精确评估的立体声摄像头上实施的Cobot的姿势估计
Marco-Antonio Cabrera-Rufino1, Juan-Manuel Ramos-Arreguín1, Marco-Antonio Aceves-Fernandez1
1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas, Las Campanas, Queretaro 76010, Mexico.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
这项研究引入了一种使用立体摄像头和卷积神经网络的新型视觉测量系统,以提高机器人手臂的精度. 智能系统可以弥补几何错误,提高工业和医疗机器人的精度.
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:
- 机器人操纵器在工业和医疗应用中需要高精度,特别是在重复性任务中.
- 几何变形通常会影响机器人操纵器的准确性.
- 现有的3D视觉技术有助于确定几何距离,但可以改进.
研究的目的:
- 测量六度自由度机器人手臂的角度位置.
- 通过弥补几何错误来提高机器人操纵器的精度.
- 开发一种智能视觉测量系统,作为编码器的补充工具.
主要方法:
- 使用立体摄像头系统进行视觉数据采集.
- 实现了一个卷积神经网络 (CNN) 算法用于错误补偿.
- 专注于减少由几何不准确性引起的精度退化.
主要成果:
- 在相机测试中达到大约一毫米的准确性.
- 与传统和基本的神经网络方法相比,提出的方法显示出更高的性能.
- 智能视觉系统有效地弥补了几何错误,提高了整体准确性.
结论:
- 开发的视觉测量系统显著提高了机器人手臂的角度位置精度.
- 这种智能系统提供了一种可行的方法来提高机器人应用的精度,而无需取代现有的编码器.
- 立体摄像头和CNN的整合为工业和医疗领域的机器人视觉带来了有希望的进步.
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