Fault-tolerant control method of robotic arm based on machine vision

Abstract

We proposed a fault-tolerant control method based on machine vision to enhance the reliability ofthe robotic arm in the radiation environment. Firstly, we introduced the joint angle visual detection method as an angle detection backup solution, which was used to timely detect the joint angle of the robotic arm in case of encoder fault. Next, we introduced an encoder fault diagnosis mechanism, and the difference between the machine visual feedback value and corresponding encoder feedback values was used as the basis of encoder fault. When the encoder failed, it could be switched to the joint angle visual detection in real time. With the Canny edge detection and the Probabilistic Hough transform, the method could extract the identification tag of robotic arm joint image from the front-end camera, and hence the joint angle value was acquired. Finally, in the fault-tolerance experiment, the proposed approach found the encoder failure and switched detection method, and the scheduled task continued to the end. The error about the angle detection based on the machine vision would not exceed ± 0.008.