Improved vision based pose estimation for industrial robots via sparse regression

Bilal, Diyar Khalis and Ünel, Mustafa and Tunç, Lütfi Taner (2020) Improved vision based pose estimation for industrial robots via sparse regression. In: 16th International Conference on Intelligent Computing, ICIC 2020, Bari, Italy

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In this work amonocular machine vision based pose estimation system is developed for industrial robots and the accuracy of the estimated pose is im-proved via sparse regression. The proposed sparse regressionbased methodis usedimprove the accuracy obtained from the Levenberg-Marquardt (LM) based pose estimation algorithmduring the trajectory tracking of an industrial robot’s end effector. The proposed method utilizes a set of basis functions to sparsely identify the nonlinear relationship between the estimated pose and the true pose provided by a laser tracker.Moreover,a camera target was designed and fitted with fiducial markers,andto prevent ambiguities in pose estimation, the markers are placed in such a way to guarantee the detection of at least two distinct nonparallel markers from a single camera within ± 90° in all directions of the cam-era’s view. The effectiveness of the proposed method is validated by an experi-mental study performed using a KUKA KR240 R2900 ultra robot while follow-ing sixteen distinct trajectories based on ISO 9238. The obtained results show that the proposed method provides parsimonious models which improve the pose estimation accuracy and precision of the vision based system during trajectory tracking of industrial robots' end effector.
Item Type: Papers in Conference Proceedings
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions: Integrated Manufacturing Technologies Research and Application Center
Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Faculty of Engineering and Natural Sciences
Depositing User: Mustafa Ünel
Date Deposited: 24 Sep 2020 13:39
Last Modified: 04 Aug 2023 14:09

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