计算机应用 ›› 2016, Vol. 36 ›› Issue (9): 2545-2549.DOI: 10.11772/j.issn.1001-9081.2016.09.2545

• 人工智能 • 上一篇    下一篇

基于改进支持向量回归算法的移动机器人定位

王春荣, 夏尔冬, 吴龙, 刘建军, 熊昌炯   

  1. 三明学院 机电工程学院, 福建 三明 365004
  • 收稿日期:2016-02-26 修回日期:2016-04-22 出版日期:2016-09-10 发布日期:2016-09-08
  • 通讯作者: 王春荣
  • 作者简介:王春荣(1986-),男,福建漳州人,讲师,博士研究生,主要研究方向:机器人定位;夏尔冬(1986-),女,湖北黄冈人,讲师,硕士,主要研究方向:机器人定位;吴龙(1973-),男,宁夏银川人,教授,博士,主要研究方向:机器人、神经网络;刘建军(1972-),男,黑龙江佳木斯人,副教授,博士,主要研究方向:汽车导航定位;熊昌炯(1963-),男,福建三明人,高级工程师,主要研究方向:机器人定位。
  • 基金资助:
    福建省教育厅科技项目(JK2015046,JA14293);福建省自然科学基金资助项目(2012J01232);三明市科技项目(2014-G-6)。

Localization for mobile robots based on improved support vector regression algorithm

WANG Chunrong, XIA Erdong, WU Long, LIU Jianjun, XIONG Changjiong   

  1. School of Mechanical and Electrical Engineering, Sanming University, Sanming Fujian 365004, China
  • Received:2016-02-26 Revised:2016-04-22 Online:2016-09-10 Published:2016-09-08
  • Supported by:
    This work is partially supported by the Department of Education Science and Technology Program of Fujian Province (JK2015046, JA14293), the Natural Science Foundation of Fujian Province (2012J01232), the Science and Technology Program of Sanming City (2014-G-6).

摘要: 为了提高移动机器人定位精度,提出了一种基于正交编码器和陀螺仪的轮式移动机器人定位系统,建立机器人的定位模型和运动学模型。研究了支持向量回归(SVR)算法,为获得更好的鲁棒性,对目标函数误差平方进行加权,分析不同参数优化算法对支持向量机回归准确率的影响。以自制的移动机器人为实验平台,将改进的算法与最小二乘支持向量回归(LSSVR)算法、加权最小二乘支持向量回归(WLSSVR)算法进行比较,对比了用改进算法时机器人在木地板场地与瓷砖场地的定位误差情况,并对正交编码器+陀螺仪定位系统与双码盘定位系统、单码盘+陀螺仪定位系统进行比较。实验结果表明,改进的算法使机器人的定位精度明显高于对比算法,并且所提出的定位系统定位效果较好。

关键词: 机器人, 定位模型, 运动模型, 加权最小二乘支持向量回归算法, 定位精度

Abstract: In order to improve the positioning accuracy of mobile robots, a kind of positioning system for wheeled mobile robots based on orthogonal encoder and gyroscope was proposed, and the positioning model and kinematics model of robot were established. With the purpose of obtaining better robustness, Support Vector Regression (SVR) algorithm was studied, the error square of objective function was weighted, and the effect of different parameter optimization algorithms on the accuracy of SVR were analyzed. The experimental platform was established by homemade mobile robot, the Least Squares Support Vector Regression (LSSVR) algorithm and the Weighted Least Squares Support Vector Regression (WLSSVR) algorithm were compared with the improved algorithm. The positioning errors of the improved algorithm when the robot worked on ceramic and wood floor were compared, and the orthogonal encoder plus gyroscope positioning system was compared with the double encoder positioning system and the single encoder plus gyroscope positioning system. The experimental results show that the robot positioning accuracy of the improved algorithm is higher than comparison algorithms, and the proposed positioning system has a better location performance.

Key words: robot, localization model, kinematic model, Weighted Least Squares Support Vector Regression (WLSSVR) algorithm, positioning accuracy

中图分类号: