Kalman-Filter-Based Pose Estimation of Cable-Driven Parallel Robots Using Cable-Length Measurements with Colored Noise

Abstract

This paper introduces a cable-length-based extended Kalman filter (L-EKF) framework to estimate the end-effector pose of a cable-driven parallel robot (CDPR). The L-EKF fuses end-effector accelerometer and rate gyroscope measurements with cable-length measurements. The main contribution compared to prior CDPR pose estimation EKF methods is that the L-EKF framework does not require an iterative forward kinematics algorithm to be solved each time step, reducing the computation time of the EKF. Moreover, the L-EKF is amenable to the inclusion of colored measurement noise, which provides a more realistic quantification of the kinematic uncertainty present in the cable-length measurements. Experimental results demonstrate that the L-EKF is computationally more efficient than previous forward-kinematics-based EKF methods, as well as the moderate improvement in pose estimation provided by the colored noise model.