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首页> 外文会议>2010 American Control Conference >Estimation of load side position in indirect drive robots by sensor fusion and kalman filtering
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Estimation of load side position in indirect drive robots by sensor fusion and kalman filtering

机译：通过传感器融合和卡尔曼滤波估算间接驱动机器人的负载侧位置

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In indirect drive robot joint, discrepancies exist between the motor side and the load side due to joint flexibilities. Thus, sensor signals may not precisely represent the actual information of interest. In this paper, estimation algorithms for load side information of the indirect robot joint are investigated. Low-cost MEMS sensors, such as gyroscopes and accelerometers, are installed on the load side. Measurement dynamics are incorporated into the model to deal with the sensor noise and bias. Kalman filtering methods are designed based on the extended dynamic/kinematic model using the fusion of multiple sensor signals. Specific issue related to the noise covariance adaptation is studied. The effectiveness of the proposed schemes is experimentally demonstrated and also confirmed in the friction compensation.
机译：在间接驱动机器人关节中，由于关节的柔韧性，电机侧和负载侧之间存在差异。因此，传感器信号可能无法精确地表示感兴趣的实际信息。本文研究了间接机器人关节载荷侧信息的估计算法。低成本的MEMS传感器（例如陀螺仪和加速度计）安装在负载侧。测量动力学被合并到模型中以处理传感器噪声和偏差。卡尔曼滤波方法是基于扩展的动态/运动模型，使用多个传感器信号的融合而设计的。研究了与噪声协方差适应有关的特定问题。实验证明了所提方案的有效性，并在摩擦补偿中得到了证实。

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