Pose Estimation of a Cable-Driven Parallel Robot Using Kalman Filtering and Forward Kinematics Error Covariance Bounds

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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).

Original languageEnglish (US)
Title of host publicationProceedings of the 2022 USCToMM Symposium on Mechanical Systems and Robotics
EditorsPierre Larochelle, J. Michael McCarthy
PublisherSpringer Science and Business Media B.V.
Pages65-75
Number of pages11
ISBN (Print)9783030998257
DOIs
StatePublished - 2022
Event2nd USCToMM Symposium on Mechanical Systems and Robotics, USCToMM MSR 2022 - Rapid City, United States
Duration: May 19 2022May 21 2022

Publication series

NameMechanisms and Machine Science
Volume118 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

Conference2nd USCToMM Symposium on Mechanical Systems and Robotics, USCToMM MSR 2022
Country/TerritoryUnited States
CityRapid City
Period5/19/225/21/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Cable driven parallel robots
  • Extended Kalman filter
  • Forward kinematics
  • Pose estimation

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