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作 者:王鑫凯 苏中[1] WANG Xinkai;SU Zhong(Beijing Key Laboratory of High Dynamic Navigation Technology,Beijing Information Science and Technology University,Beijing 100192,China)
机构地区:[1]北京信息科技大学高动态导航技术北京市重点实验室,北京100192
出 处:《传感器世界》2024年第7期31-37,共7页Sensor World
摘 要:针对管道内检测机器人在管道内自主定位的问题,提出了一种基于MIMU/里程计融合的管道内检测机器人定位算法。将捷联惯性导航系统(Stradown Inertial Navigation System,SINS)的姿态误差、速度误差和位置误差以及陀螺仪零偏和加速度计零偏作为误差状态向量,构造误差状态模型;将SINS解算的距离增量与里程计模块计算的距离增量之差和SINS解算的载体坐标系下x轴与z轴的速度信息作为误差观测量,并通过对里程计因打滑、空转等导致的错误信息进行分析与处理,构造误差观测模型。通过扩展卡尔曼滤波(Ectended Kalman Filter,EKF)对SINS解算的姿态、速度和位置进行误差补偿,绘制管道内检测机器人的轨迹图。管道内检测机器人通过111 m长的燃气管道,定位精度达到了0.99%,满足管道内检测机器人自主定位的要求。Aiming at the problem of autonomous localization of in-pipe inspection robots in pipelines,a localization algorithm for in-pipe inspection robots based on MIMU/odometer fusion is proposed.The attitude error,velocity error and position error of Strap-down Inertial Navigation System(SINS),as well as the zero bias of the gyroscope and zero bias of the accelerometer,are taken as the error state vectors to construct an error state model;The difference between the distance increment solved by SINS and the distance increment calculated by the odometer module,and the speed information of x-axis and z-axis in the carrier coordinate system solved by SINS are used as the error observation quantities,and the error observation model is constructed by analyzing and processing the error information of the odometer due to skidding and idling.The attitude,velocity and position solved by SINS are compensated for errors by Extended Kalman Filtering(EKF),and the trajectory of the inspection robot in the pipe is plotted.The in-pipe inspection robot passes through a 111-meter-long gas pipeline with a positioning accuracy of 0.99%,which meets the requirements for autonomous positioning of the in-pipe inspection robot.
关 键 词:管道内检测机器人 里程计 MIMU 融合定位 扩展卡尔曼滤波
分 类 号:TP242[自动化与计算机技术—检测技术与自动化装置]
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