Forward Kinematics and Online Self-calibration of Cable-Driven Parallel Robots with Covariance-Based Data Quality Assessment

Ryan J. Caverly, Keegan Bunker, Samir Patel, Vinh L. Nguyen

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

Abstract

This paper presents an algorithm for the forward kinematics and online self-calibration of cable-driven parallel robots. Covariance-based metrics known as the position dilution of precision (PDOP) and orientation dilution of precision (ODOP) are introduced as a means to quantify the quality of data collected with regards to self-calibration. These metrics enable systematic pruning of the data used for self-calibration and an assessment of when sufficiently rich data has been collected to perform self-calibration. The proposed algorithm is demonstrated through inverse-kinematics- and dynamics-based numerical simulations.

Original languageEnglish (US)
Title of host publicationCable-Driven Parallel Robots - Proceedings of the 6th International Conference on Cable-Driven Parallel Robots
EditorsStéphane Caro, Andreas Pott, Tobias Bruckmann
PublisherSpringer Science and Business Media B.V.
Pages369-380
Number of pages12
ISBN (Print)9783031323218
DOIs
StatePublished - 2023
Event6th International Conference on Cable-Driven Parallel Robots, CableCon 2023 - Nantes, France
Duration: Jun 25 2023Jun 28 2023

Publication series

NameMechanisms and Machine Science
Volume132
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

Conference6th International Conference on Cable-Driven Parallel Robots, CableCon 2023
Country/TerritoryFrance
CityNantes
Period6/25/236/28/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Cable-driven parallel robots
  • Calibration
  • Forward kinematics
  • Least-squares optimization
  • Pose estimation

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