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作 者:王家恩 肖献强 Wang Jiaen;Xiao Xianqiang(School of Mechanical Engineering,Hefei University of Technology,Hefei 230009,China)
机构地区:[1]合肥工业大学机械工程学院
出 处:《仪器仪表学报》2018年第8期230-238,共9页Chinese Journal of Scientific Instrument
摘 要:为实现机器人运动路径的柔性调整,并避免激光传感器和高精度陀螺仪等带来高成本,提出一种基于QR码视觉定位复合导航方法。结合制造工位离散特点和机器人差速运动模型,利用离散QR码构建地面栅格地图;通过视觉定位QR码特征并提取机器人相对地标偏差,定位过程包括边缘提取、直线检测、矩形边框判断、QR码特征识别及偏差计算;将此偏差作为地标间微机电系统(MEMS)惯性传感器轨迹推算的初始值,消除地标间行驶的累积误差。在ARM-Cortex A9平台上对导航方法进行测试,结果表明,复合导航方法使机器人能准确全局定位并实现多路径柔性切换;在1 m/s速度和1.2 m地标间距下的侧向距离偏差均值为8 mm,方向偏差最大值在1°范围内,能够满足智能制造车间对机器人定位精度和路径柔性配置需求。In order to makethe robot' s moving path flexible,and avoid the high navigation cost of high-precision gyroscopes and lasers,an integrated navigation method based on quick response( QR) code vision localization is proposed. According to the discrete manufacturing workshop station and differential kinematics model of mobile robot,ground grid map is constructed by using the discrete QR code landmark. The relative position between landmark and robot in the grid map is calculated by vision positioning QR code,including edge extraction,line detection,rectangle feature detection,recognition of QR code feature and deviation calculation. As the initiation of inertial navigation,the deviation between landmark and robot obtained from vision positioning can eliminate the cumulative error while robot moving between the landmarks sites navigated by micro-electro mechanical system( MEMS) inertial sensor. The integrated navigation method is tested in the ARM-Cortex A9 platform,the test results show that the proposed method can realize accurate self positioning and multiple driving paths flexible switching. The mean of lateral distance deviation is 8 mm,and the maximal directional deviation is 1 degree when the interval distance of landmarks is 1. 2 m and the robot moves at 1 m/s. The results indicate that the proposed method can meet the demand of intelligent manufacturing on precision positioning and flexible path configuration of the mobile robot.
关 键 词:智能制造 QR码 视觉定位 移动机器人 复合导航
分 类 号:TP242.6[自动化与计算机技术—检测技术与自动化装置]
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