RoboCup3D仿真系统中的机器人自定位方法  

Method of Robot Self-localization in RoboCup3D Simulation System

作　　者：姚千燕[1] 杨宜民[1] 

机构地区：[1]广东工业大学自动化学院,广东广州510090

出　　处：《计算机与现代化》2011年第12期141-143,149,共4页Computer and Modernization

摘　　要：在RoboCup3D仿真比赛中,机器人自定位非常重要,定位不准确会对仿真比赛产生严重的影响。为了模拟真实环境,比赛中加入了视觉噪声,这使机器人定位变得更加困难。本文针对RoboCup3D仿真中的机器人视觉特征,提出一种观测值加权融合的卡尔曼滤波方法来实现机器人自定位,采用此方法能得到更精确的观测值。仿真实验结果表明,此定位方法大大提高了机器人自定位的精度。In RoboCup3D simulation game, the robot self-location is very important, inaccurate location will have serious affect on simulation game, In order to simulate the real environment, it adds visual noise in the game, which makes it more difficult to locate the robot. In this paper, considering the robot visual features of RoboCup3D simulation, it proposes an observations weighted fusion Kalman filter approach to realize robot self-location, using this method to get more accurate observations. The simulation experience result shows that the method greatly improves the accuracy of robot self-location.

关 键 词：ROBOCUP3D 机器人自定位 卡尔曼滤波 加权融合 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]