Bolster Spring Visual Servo Positioning Method Based on Depth Online Detection

Abstract

The intelligent assembly system for railway wagon bolster springs needs to realize the positioning and grabbing of bolster springs, and also has high requirements for grabbing efficiency. To solve the problem of low efficiency of traditional visual servo positioning methods, an image visual servo (IBVS) control method based on depth online detection is proposed to improve the efficiency of maintenance operations. Based on MobileNetv3 network architecture and ECA attention mechanism, a lightweight object detection ME-YOLO model is proposed to improve the real-time positioning efficiency of bolster springs. The training results show that compared with the original YOLOv5s model, the detection accuracy of ME-YOLO is slightly reduced, but the model size is reduced by 81% and the detection speed is increased by 1.7 times. Taking advantage of the real-time detection advantages of the depth camera, a visual servo control method based on depth online detection is proposed to speed up the convergence of the IBVS system. A bolster spring grasping robot experimental platform was used to conduct a visual servo bolster spring positioning comparison test. The results show that the proposed ME-YOLO detection model can meet the grabbing needs of the bolster spring assembly robot system based on IBVS, while reducing the system convergence times by about 35%. The proposed IBVS method based on deep online detection can also further improve system operation efficiency by 7%.