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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Sensor Fusion for Robotic Workspace State Estimation
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Sensor Fusion for Robotic Workspace State Estimation

机译：用于机器人工作空间状态估计的传感器融合

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摘要

We consider the problem of tool position and orientation state estimation for robot manipulators in workspace by sensor fusion of the internal robot joint measurements with inertial measurement unit data. A prerequisite for this to be successful is accurate calibration of the sensors used. Therefore, we discuss a method for calibration of the sensor with respect to the robot end-effector, which is straightforward to apply on an arbitrary industrial manipulator. We also consider two different workspace state-estimation algorithms requiring a minimum of robot modeling; the first is based on the extended Kalman filter and the second is based on the Rao-Blackwellized particle filter. The calibration procedure and the state-estimation algorithms were evaluated and compared in extensive experiments. Both state-estimation algorithms exhibited an accuracy improvement compared to estimates provided by the forward kinematics of the robot. Moreover, both algorithms were shown to satisfy the constraints of real-time execution at 4-ms sampling period. To further evaluate and compare the robustness of the methods, the algorithms were investigated with respect to the sensitivity of the filter parameters and the noise modeling.
机译：我们通过内部机器人关节测量值与惯性测量单位数据的传感器融合，来考虑工作空间中机器人操纵器的工具位置和取向状态估计问题。成功做到这一点的前提是所用传感器的准确校准。因此，我们讨论了一种相对于机器人末端执行器对传感器进行标定的方法，该方法可以直接应用于任意工业机械手。我们还考虑了需要最少的机器人建模的两种不同的工作空间状态估计算法。第一个基于扩展的卡尔曼滤波器，第二个基于Rao-Blackwellized粒子滤波器。在广泛的实验中，对校准程序和状态估计算法进行了评估和比较。与机器人的正向运动学所提供的估计相比，两种状态估计算法都显示出准确性的提高。而且，两种算法都显示出满足4毫秒采样周期实时执行的约束。为了进一步评估和比较这些方法的鲁棒性，针对滤波器参数的敏感性和噪声建模对算法进行了研究。

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