Fast Mobile Component Location Method for Cable-driven Parallel Robots Based on YOLO Model

doi:10.3901/JME.2019.03.064

Abstract

Abstract: Aiming at the problem of real-time mobile component localization of cable-driven parallel robot, a fast mobile component location method based on YOLO object detection model is proposed. Firstly, the deep convolution neural network structure is designed, a data set is constructed in PASCAL VOC format, and the model is trained and tested on that data set. Then the image data collected by the industrial camera are inputted into the model to detect the target, record the category and position of the target, analyze the color characteristics of the target and binarization. The precise position of the mobile component of the cable-driven parallel robot is further calculated. The experimental results show that the method can classify and locate the target accurately. The positioning error is less than 1°, and the frame rate of image processing can reach 33 frames, which can meet the real-time requirements. At the same time, the algorithm has good accuracy and effectiveness.

Key words: cable-driven parallel robot, deep learning, fast location method, object detection

CLC Number:

TP242

ZI Bin, YIN Zeqiang, LI Yongchang, ZHAO Tao. Fast Mobile Component Location Method for Cable-driven Parallel Robots Based on YOLO Model[J]. Journal of Mechanical Engineering, 2019, 55(3): 64-72.

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