Kinematics Parameters Identification for IRB 1400 Using Improved Quantum Behaved Particle Swarm Optimization

被引：3
|
作者：
Wang, Fengliang [1 ]
Wang, Yali [1 ]
Li, Jie [1 ]
Fang, Wei [2 ]
机构：
[1] Tianjin Univ Technol, Zhonghuan Informat Coll, Tianjin, Peoples R China
[2] COMAC, Beijing Aeronaut Sci & Technol Res Inst, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS | 2016年 / 386卷
关键词：
Kinematics parameter errors; Quantum behaved particle swarm optimization; Robot positioning accuracy; ACCURACY;
D O I：
10.1007/978-3-662-49831-6_91
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents an improved quantum behaved particle swarm optimization (IQPSO) algorithm to identify the robot kinematics parameter errors to improve the absolute accuracy of serial robots. The IQPSO algorithm is based on the quantum behaved particle swarm optimization (QPSO) algorithm. To improve the convergence speed, in the IQPSO algorithm, each dimension of the global best position is kept to be the best at each iterative process by comparing each dimension with the pre-value in the last iterative process. Comparing the IQPSO algorithm with least squared algorithm, the absolute accuracy of the robot can be improved 200 %. And compared with the standard particle swarm optimization (SPSO) algorithm and QPSO algorithm, the convergence speed is improved about 200 %. So the proposed algorithm can effectively identify the robot kinematics parameter errors.
引用
收藏
页码：881 / 890
页数：10
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