Hou Rong, Yin Jianping, Liu Yanchen, Lu Huijuan
School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China.
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2024 Feb 2;24(3):984. doi: 10.3390/s24030984.
In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand-eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.
在工业生产过程中，工件的人工装配存在效率低、强度大的问题，并且人体的一些装配过程存在一定程度的危险。同时，传统机器学习算法难以适应当前工业现场环境的复杂性；环境变化会极大地影响机器人工作的准确性。因此，本文提出一种基于机器视觉与YOLOv5深度学习模型相结合的方法，获取盘状多孔定位信息，经ROS通信控制机器人手臂工作进行坐标映射，以提高环境抗干扰能力和工作效率，同时降低对人体的危险。该系统利用摄像头采集复杂环境下目标的实时图像，然后对其进行训练和处理以实现识别，从而获得坐标定位信息。通过手眼标定将该信息转换为机器人坐标系下的坐标，再通过上下位机通信控制机器人完成多孔定位与跟踪。结果表明，目标物体的训练和测试精度较高，机器人手臂的控制精度也相对较高。该方法对工业复杂环境具有较强的抗干扰能力，具有一定的可行性和有效性。它为实现工业生产中对接盘状工件的自动化安装奠定了基础，也为螺丝定位需求的生产安装过程提供了更有利的选择。
