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首页> 外文期刊>Mechanism and Machine Theory: Dynamics of Machine Systems Gears and Power Trandmissions Robots and Manipulator Systems Computer-Aided Design Methods >Minimization of the positional errors for an accurate determination of the kinematic parameters of a rigid-body system with miniature inertial sensors
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Minimization of the positional errors for an accurate determination of the kinematic parameters of a rigid-body system with miniature inertial sensors

机译:最小化位置误差,以精确确定带有微型惯性传感器的刚体系统的运动学参数

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摘要

This paper presents an approach to minimize and control the error of the kinematic parameters of the space-constraint rigid-body system by using inertial micro-electro-mechanical sensors (MEMS). We analyze the error propagation when the kinematic joint constraints are observed for a sensor-fusion update in the kinematic model because of the uncertain position of the inertial sensors. The minimization of the errors of the kinematic parameters comes from applying multiple inertial units on every rigid body with the controlled input positional error between each inertial unit. The analytical approach proposes the inclusion of the position vectors from the inertial units to the kinematical joints into the state vector that consists of the observed kinematical and sensor parameters. A Kalman-filtering procedure is used to observe the state vector and, additionally, the adaptive estimation of the position vectors from the inertial units to the kinematic joints or constraints is presented in order to achieve the optimum performance of the filter. The analytical approach is experimentally validated on a pendulum mechanism, where the improved performance of the proposed approach is confirmed.
机译:本文提出了一种通过使用惯性微机电传感器(MEMS)来最小化和控制空间约束刚体系统的运动学参数误差的方法。我们分析了由于惯性传感器的位置不确定而在运动学模型中观察到运动学传感器融合更新的运动学关节约束时的误差传播。通过在每个刚体上应用多个惯性单元以及每个惯性单元之间的受控输入位置误差来实现运动参数误差的最小化。分析方法建议将从惯性单位到运动关节的位置矢量包含到状态矢量中,该状态矢量由观察到的运动参数和传感器参数组成。卡尔曼滤波过程用于观察状态矢量,此外,还提出了从惯性单元到运动学关节或约束的位置矢量的自适应估计,以实现滤波器的最佳性能。该分析方法在摆机构上进行了实验验证,证实了所提出方法的改进性能。

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