在串联机械臂运动跟踪中,将惯性测量单元(IMU)的运动信息映射到机械臂运动链上,针对IMU测量时的安装误差和随机噪声会影响到测量信息准确性的问题,提出了一种基于IMU数据融合的运动状态估计方法。该方法首先对传感器与旋转关节坐标系进行校准,再通过融合IMU中陀螺仪和加速计信息,建立了基于扩展卡尔曼滤波(EKF)算法的运动姿态最优估计模型。与机械臂实际输出的角度信息相比,所提方法的平均角度和位姿均方根误差(RMSE)为0.19°和0.45 mm
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