An Automatic High-Precision Calibration Method of Legs and Feet for Quadruped Robots Using Machine Vision and Artificial Neural Networks

Abstract

Abstract Kinematics calibration for quadrupled robots is essential to ensuring motion accuracy and control stability. The angle of the leg joints of the quadruped robot is error-compensated to improve its position accuracy. This paper proposes a real-time high-precision kinematics calibration method for quadruped robots using machine vision and artificial neural networks to simplify the calibration process and improve calibration accuracy. The method includes two parts: identifying the markers fixed on the legs through target detection, calculating the center coordinates of the markers, and building an error model based on an artificial neural network to solve the angle error of each joint and compensate for it. A series of experiments have been carried out to verify the model’s accuracy. The experimental results show that, compared to traditional manual calibration, by adding an error correction model to the inverse kinematics neural network, the calibration efficiency can be significantly improved while the calibration accuracy is met.