基于多传感器的AGV定位误差校正方法研究
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摘要
针对自动导引运输车(Automated Guided Vehicle,AGV)系统中定位误差问题,基于一种快速响应(Quick Response,QR)码地标系统和新的编码器安装方法,提出了一种视觉定标方法和S形曲线修正算法.视觉定标方法通过QR码内嵌入标签号和位置信息,利用相机识别到QR码后,提取QR码特征点在网络中的位置,通过计算当前QR码与AGV之间的位置偏差和姿态对AGV校正.S形曲线修正算法通过左右编码器记录的路程差对AGV进行位置偏差校正.结果表明,本文将以上两种方法组合使用,补偿了编码器大角度范围变化下积累的误差.
Aimed at the problem of positioning error in Automated Guided Vehicle(AGV)system,based on a fast response(Quick Response,QR)code landmark system and a new encoder installation method,a visual calibration method and a S curve correction algorithm were proposed.The visual calibration method used the QR code which embed the tag number and location information,and used the camera to identify the QR code,then extracted the location of the QR code feature points in the network,and made a correction to AGV by calculating the position deviation and attitude between the current QR code and the AGV.The S curve correction algorithm corrected the position deviation of AGV through the distance difference between the left and right encoder.The results show that the combination of the two methods can compensate the error accumulated under the large angle range of encoder.
Aimed at the problem of positioning error in Automated Guided Vehicle(AGV)system,based on a fast response(Quick Response,QR)code landmark system and a new encoder installation method,a visual calibration method and a S curve correction algorithm were proposed.The visual calibration method used the QR code which embed the tag number and location information,and used the camera to identify the QR code,then extracted the location of the QR code feature points in the network,and made a correction to AGV by calculating the position deviation and attitude between the current QR code and the AGV.The S curve correction algorithm corrected the position deviation of AGV through the distance difference between the left and right encoder.The results show that the combination of the two methods can compensate the error accumulated under the large angle range of encoder.
引文
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[2]Cho B S,Moon W S,Seo W J,et al.A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding[J].Journal of Mechanical Science&Technology,2011,25(11):2907-2917.
[3]张瑛.用于室内定位惯性导航技术研究[D].南京:南京邮电大学,2015.
[4]李雪茹.基于航迹推算的全向移动机器人定位及磁导航的研究[D].南京:南京理工大学,2017.
[5]Kobayashi H.A new proposal for self-localization of mobile robot by self-contained 2D barcode landmark[C]//Sice Conference.IEEE,2012:2080-2083.
[6]Okuyama K,Kawasaki T,Kroumov V.Localization and position correction for mobile robot using artificial visual landmarks[C]∥International Conference on AdvancedMechatronicSystems. IEEE, 2011:414-418.
[7]Zhang H,Zhang C,Yang W,et al.Localization and navigation using QR code for mobile robot in indoor environment[C]//IEEE International Conference on Robotics and Biomimetics.IEEE,2015:2501-2506.
[8]黄露.基于人工路标的室内机器人导航方法研究与实现[D].合肥:中国科学技术大学,2017.
[9]Lee S J,Lim J,Tewolde G,et al.Autonomous tour guide robot by using ultrasonic range sensors and QR code recognition in indoor environment[C]//IEEE International Conference on Electro/information Technology.IEEE,2014:410-415.
[10]张涛,马磊,梅玲玉.基于单目视觉的仓储物流机器人定位方法[J].计算机应用,2017,37(9):2491-2495.Zhang Tao,Ma Lei,Mei Lingyu.Location method of warehouse logistics robot based on monocular vision[J].Journal of Computer Applications,2017,37(9):2491-2495.(in Chinese)
[11]唐金君.基于传感器信息融合的自动导引车自由路径转向研究[D].南京:南京航空航天大学,2012.
[12]Okuyama K,Kawasaki T,Kroumov V.Localization and position correction for mobile robot using artificial visual landmarks[C]∥International Conference on Advanced Mechatronic Systems.IEEE,2011:414-418.
[13]王珊珊.轮式移动机器人控制系统设计[D].南京:南京理工大学,2013.
[14]周柱.基于STM32的智能小车研究[D].重庆:西南交通大学,2011.
[15]赵博.基于视觉导航的自动导引车设计与实现[D].济南:山东大学,2017.