An automatic high-precision calibration method of legs and feet for quadruped robots using machine vision and artificial neural networks

IF 2.6 3区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computing and Information Science in Engineering Pub Date : 2023-10-25 DOI:10.1115/1.4063891

Yaguan Li, Handing Xu, Yanjie Xu, Qingxue Huang, Xin-Jun Liu, Zhenguo Nie

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引用次数: 0

Abstract

Abstract The kinematics calibration for quadruped robots is essential in 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 an online intelligent 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 and 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 the 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.

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一种基于机器视觉和人工神经网络的四足机器人腿脚高精度自动标定方法

四足机器人的运动学标定是保证机器人运动精度和控制稳定性的关键。对四足机器人的腿部关节角度进行误差补偿，提高了机器人的定位精度。为了简化标定过程，提高标定精度，提出了一种基于机器视觉和人工神经网络的四足机器人在线智能运动学标定方法。该方法包括两个部分:通过目标检测识别固定在腿上的标记点，计算标记点的中心坐标，并基于人工神经网络建立误差模型，求解各关节的角度误差并进行补偿。为了验证模型的准确性，进行了一系列实验。实验结果表明，与传统的人工标定相比，通过在逆运动学神经网络中加入误差修正模型，在满足标定精度的同时，标定效率显著提高。

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来源期刊

Journal of Computing and Information Science in Engineering 工程技术-工程：制造

CiteScore

6.30

自引率

12.90%

发文量

100

审稿时长

6 months

期刊介绍： The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping