A Method of Robot DH Parameter Calibration Based on the Genetic Algorithm

被引:0
作者
Zhang, Tao [1 ]
Zhang, Boqiang [1 ]
Sun, Zejun [1 ]
机构
[1] Shanghai Elect Grp Cent Acad, Shanghai Engn Res Ctr CNC Equipment, Shanghai, Peoples R China
来源
2024 8TH INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION SCIENCES, ICRAS 2024 | 2024年
关键词
robot kinematics; genetic algorithm; Monte Carlo Method; error compensation; calibration;
D O I
10.1109/ICRAS62427.2024.10654482
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The kinematic DH parameters of the robot directly affect the movement accuracy of the robot. Therefore, it is vital to calibrate the DH parameters. The commonly used method for robot parameter calibration is to approximate the data using the least squares method based on the actual collected data. However, this method inevitably requires calculating the inverse kinematics solution of the manipulator, which is cumbersome and complicated. To avoid the complex calculations caused by the inverse kinematics of the robot, this paper constructs the DH parameter error model of the robot through forward kinematics, uses a genetic algorithm to iterate the DH parameter error as an individual, and obtains the optimized DH parameters. The genetic algorithm operators were designed to ensure species diversity and avoid falling into local optimality. Finally, the correctness of this method was verified through experiments, and the method can improve the accuracy of robot movement.
引用
收藏
页码:1 / 5
页数:5
相关论文
共 16 条
[1]   A Multi-agent genetic algorithm for multi-objective optimization [J].
Akopov, Andranik S. ;
Hevencev, Maxim A. .
2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013), 2013, :1391-1395
[2]  
Choi J, 2017, 2017 2ND INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION ENGINEERING (ICRAE), P37, DOI 10.1109/ICRAE.2017.8291349
[3]  
Deng X, 2020, CHIN CONT DECIS CONF, P2198, DOI 10.1109/CCDC49329.2020.9164756
[4]  
Honghua Zhao, 2020, 2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), P221, DOI 10.1109/AIEA51086.2020.00054
[5]   VQA-based Robotic State Recognition Optimized with Genetic Algorithm [J].
Kawaharazuka, Kento ;
Obinata, Yoshiki ;
Kanazawa, Naoaki ;
Okada, Kei ;
Inaba, Masayuki .
2023 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2023), 2023, :8306-8311
[6]  
Shi Beichao, 2022, 2022 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO), P133, DOI 10.1109/3M-NANO56083.2022.9941703
[7]  
Tan Y University C S Changsha, Computer & Digital Engineering
[8]   An effective robot trajectory planning method using a genetic algorithm [J].
Tian, LF ;
Collins, C .
MECHATRONICS, 2004, 14 (05) :455-470
[9]  
Wang G, 2016, AEROSP CONF PROC, DOI 10.1109/ICSSSM.2016.7538573
[10]  
Wang HX, 2013, CHIN CONT DECIS CONF, P5118