Abstract
This paper introduces a novel extended Kalman filter (EKF) approach for the pose estimation of a cable driven parallel robot (CDPR). The filter fuses accelerometer, rate gyroscope, and winch encoder data through a dynamically-updated covariance on the forward kinematics pose estimation error. The filter is tested on experimental data collected by a six degree-of-freedom CDPR test bed. The results show that the EKF is capable of providing similar pose estimation accuracy compared to forward kinematics alone, but with much lower covariance on the estimation error (i.e., much greater confidence in the pose estimate).
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Puri, N., Caverly, R.J. (2022). Pose Estimation of a Cable-Driven Parallel Robot Using Kalman Filtering and Forward Kinematics Error Covariance Bounds. In: Larochelle, P., McCarthy, J.M. (eds) Proceedings of the 2022 USCToMM Symposium on Mechanical Systems and Robotics. USCToMM MSR 2022. Mechanisms and Machine Science, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-030-99826-4_7
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