Cable-Driven Parallel Robot Pose Estimation Using Extended Kalman Filtering with Inertial Payload Measurements

Vinh Le Nguyen, Ryan James Caverly

Research output: Contribution to journalArticlepeer-review

Abstract

This letter introduces two novel extended Kalman filtering (EKF) approaches to fuse payload accelerometer and rate gyroscope data with forward kinematics to estimate the payload pose of a cable-driven parallel robot (CDPR). An Euler-angle-based EKF and a rotation-vector-based multiplicative extended Kalman filter (MEKF) are proposed for this purpose. An unconstrained attitude parameterization identity is used to derive an analytic form of the Jacobian involved in the iterative forward kinematics calculations, which facilitates the use of different attitude parameterizations. Monte-Carlo simulations are performed with two levels of realistic sensor noise and bias, as well as calibration errors. The numerical results demonstrate more accurate pose estimates using the EKF and MEKF compared to forward kinematics computations alone.

Original languageEnglish (US)
Article number9372787
Pages (from-to)3615-3622
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume6
Issue number2
DOIs
StatePublished - Apr 2021

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • cable-driven parallel robots
  • extended kalman filter
  • parallel robots
  • sensor fusion
  • Tendon/wire mechanism

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