Method of Parallel Robot Arm Positioning Based on Visual Guidance

Traditional manually taught robotic arms lack a certain level of adaptability in control and are unable to autonomously adjust when random situations occur, resulting in poor production line flexibility. To address this issue, a parallel robotic arm grasping control method based on YOLO vision is proposed. A hardware system consisting of parallel robotic arms, industrial cameras, Ubuntu servers, etc., is constructed, and each working module is encapsulated into independent ROS2 communication nodes. After training the model of the target object, pixel coordinates of the target object are converted into world coordinates of the robotic arm through data processing and message communication between nodes. Motion trajectories are generated based on the relationship between target points and placement point coordinates, and real-time inverse kinematics calculations of the robotic arm are performed to determine the angles of each motor at corresponding moments, thereby driving the robotic arm to perform grasping operations. The performance of the robotic arm control method is improved in terms of target detection accuracy and real-time performance. Experimental results indicate that the robotic arm grasping control method based on YOLO vision achieves high recognition accuracy for detecting targets, high efficiency in node-based development, strong portability, and has certain application value.