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Calibration of Kinematic Parameters for Two Wheel Differential Mobile Robots by Using Experimental Heading Errors

机译:使用实验标题误差校准两个轮子差动移动机器人的运动参数

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摘要

Odometry using incremental wheel encoder sensors provides the relative position of mobile robots. This relative position is fundamental information for pose estimation by various sensors for EKF Localization, Monte Carlo Localization etc. Odometry is also used as unique information for localization of environmental conditions when absolute measurement systems are not available. However, odometry suffers from the accumulation of kinematic modeling errors of the wheel as the robot's travel distance increases. Therefore, systematic odometry errors need to be calibrated. Principal systematic error sources are unequal wheel diameters and uncertainty of the effective wheelbase. The UMBmark method is a practical and useful calibration scheme for systematic odometry errors of two-wheel differential mobile robots. However, the approximation errors of the calibration equations and the coupled effect between the two systematic error sources affect the performance of the kinematic parameter estimation. In this paper, we proposed a new calibration scheme whose calibration equations have less approximation errors. This new scheme uses the orientation errors of the robot's final pose in the test track. This scheme also considers the coupled effect between wheel diameter error and wheelbase error. Numerical simulations and experimental results verified that the proposed scheme accurately estimated the kinematic error parameters and improved the accuracy of odometry calibration significantly.
机译:使用增量轮编码器传感器的内径测量提供移动机器人的相对位置。这种相对位置是通过各种传感器进行EKF定位的各种传感器的姿态估计的基本信息,蒙特卡罗定位等。在绝对测量系统不可用时,OCTomary也被用作环境条件的本地化的独特信息。然而,随着机器人的行进距离增加,内径测量率遭受车轮的运动模型误差。因此,需要校准系统的内径误差。主系统误差源是不等的轮径和有效轴距的不确定性。 UMBMark方法是用于双轮差分移动机器人的系统内径误差的实用和有用的校准方案。然而,校准方程的近似误差和两个系统误差源之间的耦合效果影响了运动参数估计的性能。在本文中,我们提出了一种新的校准方案,其校准方程具有较少的近似误差。该新方案使用机器人最终姿势的定向误差在测试轨道中。该方案还认为车轮直径误差和轴距误差之间的耦合效果。数值模拟和实验结果证实,所提出的方案准确地估计了运动误差参数,并显着提高了内径校准的准确性。

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