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An online method for serial robot self-calibration with CMAC and UKF

机译:基于CMAC和UKF的在线串行机器人自校准方法

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摘要

The aim of this paper is to propose an online self-calibration method which is used to estimate the kinematic parameters errors of the serial robot manipulators. In this method, a position marker and an inertial measurement unit (IMU) are solidly fixed at the robot end-effector (EE). The position is determined with a position sensor by tracking the marker and the orientation is measured by the IMU in real time. The Factored Quaternion Algorithm (FQA) is used to represent the orientation in a quaternion. In order to eliminate the influence of the noises and the measurement errors from the sensors, the Cerebellar Model Articulation Controller (CMAC) algorithm is adopted to estimate poses of the robot EE. With the estimated poses, the errors between the actual and the nominal kinematic parameters of the robot manipulator could be identified by the Unscented Kalman Filter (UKF). This method only takes several uncomplicated steps but performs with high autonomy and accuracy. Several experiments are carried out with a GOOGOL GRB3016 robot to verify this method and the results indicate that it is indeed of high convenience, precision and efficiency.
机译:本文的目的是提出一种在线自校准方法,该方法用于估计串行机器人操纵器的运动学参数误差。通过这种方法,位置标记和惯性测量单元(IMU)牢固地固定在机器人末端执行器(EE)上。使用位置传感器通过跟踪标记确定位置,并通过IMU实时测量方向。分解四元数算法(FQA)用于表示四元数中的方向。为了消除来自传感器的噪声和测量误差的影响,采用小脑模型关节控制器(CMAC)算法来估计机器人EE的姿态。利用估计的姿势,可以通过无味卡尔曼滤波器(UKF)识别机器人操纵器的实际运动参数和名义运动参数之间的误差。该方法仅需执行几个简单的步骤,但具有很高的自治性和准确性。使用GOOGOL GRB3016机器人进行了多次实验,验证了该方法的有效性,结果表明该方法确实具有很高的便利性,准确性和效率。

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