Accuracy Compensation of a Spraying Robot Based on RBF Neural Network

被引：0

作者：

Zhu, Qiancheng ^{[1
]}

Li, Jiting ^{[1
]}

Yuan, Peijiang ^{[1
]}

Shi, Zhenyun ^{[1
]}

Lin, Minqing ^{[1
]}

Chen, Dongdong ^{[1
]}

Wang, Tianmiao ^{[1
]}

机构：

[1] Beihang Univ, Sch Mech Engn & Automat, Beijing, Peoples R China

来源：

IEEE ICARM 2016 - 2016 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM) | 2016年

关键词：

spraying robot; accuracy compensation; kinematics analysis; positioning accuracy; RBF neural network;

D O I：

暂无

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：

In order to realize automatic aircrafts spraying, it is necessary to improve the positioning accuracy of spraying robot, which has received much attention with the increasing demands for robotic automation. This paper studies accuracy compensation of a spraying robot with six degrees of freedom (DOF). First, Denavit-Hartenberg (D-H) matrix method is employed to establish kinematics analysis and get the ideal position of robot end. Secondly, Radial Basis Function (RBF) neural network algorithm is used to improve the robot absolute positioning accuracy. Finally, simulations based on MATLAB are made to prove that calculation and analysis are correct. The systematic and parametric analysis about the spraying robot in this paper provides a foundation for the design, control and trajectory planning in future.

引用

页码：414 / 419

页数：6

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