Kinematic Parameter Identification of Industrial Robot Based on Binocular Vision

Aiming at the low absolute positioning accuracy of industrial robots, a method for identifying kinematic parameters based on binocular vision is proposed. First, a modified Denavit-Hartenberg set of parameters was used to construct the robot's kinematic model. Next, the robot's end was designed to travel in a multi-space sphere. A binocular vision system was used to estimate the actual distance between various endpoints and the sphere's center; moreover, comparison of the measured distance with the theoretical distance generated the relative distance error function. The sine cosine strategy and trust region optimization were used to optimize the particle swarm optimization algorithm and reduce its possibility of falling into local optimization. Then, the kinematic parameter error was addressed iteratively using the particle swarm optimization algorithm. Finally, the kinematic parameters were compensated and validated by comparison. The experimental results demonstrate that the average distance error is reduced from 1. 1601 mm to 0. 2260 mm, improving accuracy by 80.52%. Moreover, the standard deviation is reduced from 0.6582 mm to 0.1412 mm, an accuracy improvement of 78. 55%, demonstrating the efficiency and practicability of the proposed method.