التفاصيل البيبلوغرافية
| العنوان: |
基于改进鸡群优化算法运动学与改进鸡群优化-Elman 神经网络非运动学的机器人误差标定. (Chinese) |
| Alternate Title: |
Robot Error Calibration Based on Improved CSO Algorithm Kinematics and Improved CSO-Elman Neural Network Non-kinematics. (English) |
| المؤلفون: |
江小辉, 孙翼飞, 郭维诚, 侯春杰, 任斐 |
| المصدر: |
Information & Control; Jun2024, Vol. 53 Issue 3, p315-328, 14p |
| Abstract (English): |
Aiming at the positioning error calibration problem of industrial robots, we combine the kinematics and non-kinematics aspects to calibrate the positioning error of robots. Aiming at the kinematics of a robot, a kinematics error model is developed and an improved chicken swarm optimication (CSO) algorithm is proposed to identify the geometric parameter error of the robot. The effect of the proposed algorithm is verified by comparing the Levenberg-Marquardt iterative algorithm and the particle swarm optimization algorithm. The IRB1200 robot is taken as the experimental object, and error data are collected using an APIT3 laser tracker. A robot error calibration experiment platform is built to conduct experiments. The experimental measurement shows that the average positioning error of the robot end is decreased from 2.76 mm to 1.45 mm, which is increased by 47.5%. Furthermore, for the non-kinematic aspect of the robot, the improved CSO algorithm proposed in the kinematic error calibration is used to optimize the initial threshold and weight of the Elman neural network, and the Elman neural network optimized with initial parameters is used to establish the mapping relationship between the robot end position error and the robot joint angle to predict the robot position error in a trained robot cube space. The prediction effect of the ordinary Elman neural network was compared. The experimental measurement shows that the average positioning of the robot end is improved by 34.9% compared with that before calibration, which verifies the fitting prediction effect of the neural network proposed in this study. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
针对工业机器人末端定位误差标定问题, 本文从机器人运动学与非 运动学两个方面对机器人进行定位误差标定。针对机器人运动学方面, 建立了运动学误差模型并提出一种改进CSO (chicken swarm optimization) 算法对机器人进行几何参数误差辨识, 通过对比LM (Levenberg-Mar- quardt)迭代算法与 PSO (particle swarm optimization) 算法验证本文所提出 算法的效果, 以I RB1200 机器人为实验对象, 通过APIT3 激光跟踪仪采 集误差数据, 搭建了机器人误差标定实验平台进行实验。经实验测量, 机器人末端平均定位误差由 2.76 mm下降至1.45mm, 提升了47.5%; 同时 针对机器人非运动学方面, 使用在运动学误差标定中提出的改进 CSO 算 法优化了 Elman 神经网络的初始阀值与权重, 并使用初始参数优化的 Elman 神经网络建立机器人末端位置误差与机器人关节角之间的映射关 系, 以预测训练完成的机器人立方体空间内的机器人位置误差, 并对比 了普通 Elman 神经网络的预测效果。经实验测量, 机器人末端平均定位 精度较标定前提升34.9%, 验证了本文所提出神经网络的拟合预测效果。 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |