Computer Vision-Based Pose Estimation of Tensegrity Robots Using Fiducial Markers

Akmaral Moldagalieva, Denis Fadeyev, Askat Kuzdeuov, Valeriya Khan, Bexultan Alimzhanov, Huseyin Atakan Varol

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

Abstract

Tensegrity paradigm can allow creation of lightweight, robust, efficient and configurable robots. Modeling, design, control and sensing of tensegrity robots are challenging. Effective control of tensegrity robots requires accurate state information in real-time. In this work, we address the problem of six-degree-of-freedom pose estimation of tensegrity robots. Due to the unorthodox structure of the tensegrities and their wide range-of-motion, a computer vision-based technique is employed. To the best of our knowledge, this is the first work which uses computer vision to determine the internal state of a robot. Specifically, a hemispherical camera module was attached to the base of the robot pointing toward the top part of the robot. This camera was used to track the fiducial markers printed on a triangle-shaped plate on the upper part of the robot. Position and orientation of this plate in 3D-space was obtained by the use of ArUco markers, a fiducial marker system originally devised for augmented reality. The efficacy of our fiducial marker-based pose estimation framework was shown with extensive real-world experiments with a six-bar tensegrity prism robot. Specifically, the average position and orientation estimation errors for a 1.5 m tensegrity were 2.3 cm and 7.5 degrees, respectively.

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages478-483
Number of pages6
ISBN (Electronic)9781538636152
DOIs
Publication statusPublished - Apr 25 2019
Event2019 IEEE/SICE International Symposium on System Integration, SII 2019 - Paris, France
Duration: Jan 14 2019Jan 16 2019

Publication series

NameProceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019

Conference

Conference2019 IEEE/SICE International Symposium on System Integration, SII 2019
CountryFrance
CityParis
Period1/14/191/16/19

Fingerprint

computer vision
robots
markers
Computer vision
Robots
Cameras
cameras
Augmented reality
Prisms
triangles
Error analysis
prisms
degrees of freedom
modules

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

Cite this

Moldagalieva, A., Fadeyev, D., Kuzdeuov, A., Khan, V., Alimzhanov, B., & Varol, H. A. (2019). Computer Vision-Based Pose Estimation of Tensegrity Robots Using Fiducial Markers. In Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019 (pp. 478-483). [8700452] (Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SII.2019.8700452

Computer Vision-Based Pose Estimation of Tensegrity Robots Using Fiducial Markers. / Moldagalieva, Akmaral; Fadeyev, Denis; Kuzdeuov, Askat; Khan, Valeriya; Alimzhanov, Bexultan; Varol, Huseyin Atakan.

Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 478-483 8700452 (Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019).

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

Moldagalieva, A, Fadeyev, D, Kuzdeuov, A, Khan, V, Alimzhanov, B & Varol, HA 2019, Computer Vision-Based Pose Estimation of Tensegrity Robots Using Fiducial Markers. in Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019., 8700452, Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019, Institute of Electrical and Electronics Engineers Inc., pp. 478-483, 2019 IEEE/SICE International Symposium on System Integration, SII 2019, Paris, France, 1/14/19. https://doi.org/10.1109/SII.2019.8700452
Moldagalieva A, Fadeyev D, Kuzdeuov A, Khan V, Alimzhanov B, Varol HA. Computer Vision-Based Pose Estimation of Tensegrity Robots Using Fiducial Markers. In Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 478-483. 8700452. (Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019). https://doi.org/10.1109/SII.2019.8700452
Moldagalieva, Akmaral ; Fadeyev, Denis ; Kuzdeuov, Askat ; Khan, Valeriya ; Alimzhanov, Bexultan ; Varol, Huseyin Atakan. / Computer Vision-Based Pose Estimation of Tensegrity Robots Using Fiducial Markers. Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 478-483 (Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019).
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