桌面型机械臂的机器视觉应用框架研究

与传统工业机械臂相比，桌面型机械臂具有环境多变、人机协作等特点，为其增加视觉功能显得尤为重要。而目前实现机器视觉的应用框架有很多，如何根据机械臂工作环境及性质，搭建合适的视觉应用软硬件平台，以提高机器视觉识别的准确率和效率是本文研究的重点。本文通过采用TensorFlow深度学习框架，利用嵌入式系统的软硬件设计，结合OpenCV等图像处理软件，搭建适合桌面型机械臂的机器视觉二次开发框架，为进一步开发基于视觉的机械臂应用提供了基础。仿真测试及人机协作的案例应用表明该框架具有较好的适应性和高效性。

Machine Vision Application Framework of Desktop Robot Arm

Compared with the traditional industry robot arm, the desktop robot arm has the characteristics of changeable environment and man-machine cooperation, which makes it more important to increase its visual function. At present, there are many application frameworks to realize machine vision. How to build an appropriate software and hardware platform for visual application according to the working environment and nature of the robot arm, so as to improve the accuracy and efficiency of machine vision recognition is the focus of this paper. Based on TensorFlow’s deep learning framework, the embedded system’s software and hardware design, and the application of OpenCV and other image processing software, this paper constructs a machine vision secondary development framework suitable for desktop robot arm, which provides a foundation for the further development of vision-based robot arm applications. Through simulation test and case application of man-machine cooperation, it is shown that the framework has better adaptability and better efficiency.