Abstract
In this work a monocular machine vision based pose estimation system is developed for industrial robots and the accuracy of the estimated pose is improved via sparse regression. The proposed sparse regression based method is used improve the accuracy obtained from the Levenberg-Marquardt (LM) based pose estimation algorithm during 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, and to prevent ambiguities in pose estimation, the markers are placed in such a way to guarantee the detection of at least two distinct non parallel markers from a single camera within ±90° in all directions of the camera’s view. The effectiveness of the proposed method is validated by an experimental study performed using a KUKA KR240 R2900 ultra robot while following 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.
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Notes
- 1.
Tested on a workstation with Intel Xeon E5-1650 CPU @ 3.5 GHz and 16 GB RAM.
References
Klimchik, A., Ambiehl, A., Garnier, S., Furet, B., Pashkevich, A.: Efficiency evaluation of robots in machining applications using industrial performance measure. Robot. Comput.-Integr. Manuf. 48, 12–29 (2017)
Devlieg, R.: Expanding the use of robotics in airframe assembly via accurate robot technology. SAE Int. J. Aerosp. 3(1846), 198–203 (2010)
Keshmiri, M., Xie, W.F.: Image-based visual servoing using an optimized trajectory planning technique. IEEE/ASME Trans. Mechatron. 22(1), 359–370 (2016)
Hashimoto, K.: A review on vision-based control of robot manipulators. Adv. Robot. Int.: J. Robot. Soc. Japan 17(10), 969–991 (2003)
Shu, T., Gharaaty, S., Xie, W., Joubair, A., Bonev, I.A.: Dynamic path tracking of industrial robots with high accuracy using photogrammetry sensor. IEEE/ASME Trans. Mechatron. 23(3), 1159–1170 (2018)
Comet project. https://comet-project.eu/results.asp. Accessed 07 Aug 2020
Nissler, C., Stefan, B., Marton, Z.C., Beckmann, L., Thomasy, U.: Evaluation and improvement of global pose estimation with multiple apriltags for industrial manipulators. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2016)
Liu, B., Zhang, F., Qu, X.: A method for improving the pose accuracy of a robot manipulator based on multi-sensor combined measurement and data fusion. Sensors 15(4), 7933–7952 (2015)
Janabi-Sharifi, F., Marey, M.: A Kalman-filter-based method for pose estimation in visual servoing. IEEE Trans. Robot. 26(5), 939–947 (2010)
D’Errico, G.E.: A la kalman filtering for metrology tool with application to coordinate measuring machines. IEEE Trans. Ind. Electron. 59(11), 4377–4382 (2011)
Alcan, G.: Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Ph.D. thesis, Sabanci University (2019)
Mumcuoglu, M.E., et al.: Driving behavior classification using long short term memory networks. In: 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE), pp. 1–6. IEEE (2019)
Alcan, G., et al.: Estimating soot emission in diesel engines using gated recurrent unit networks. IFAC-PapersOnLine 52(5), 544–549 (2019)
Aran, V., Unel, M.: Gaussian process regression feedforward controller for diesel engine airpath. Int. J. Automot. Technol. 19(4), 635–642 (2018)
Darcis, M., Swinkels, W., Guzel, A.E., Claesen, L.: PoseLab: a levenberg-marquardt based prototyping environment for camera pose estimation. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6. IEEE (2018)
Brunton, S.L., Proctor, J.L., Kutz, J.N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Nat. Acad. Sci. 113(15), 3932–3937 (2016)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An introduction to statistical learning, vol. 112. Springer, New York (2013)
Romero-Ramirez, F.J., Muñoz-Salinas, R., Medina-Carnicer, R.: Speeded up detection of squared fiducial markers. Image Vis. Comput. 76, 38–47 (2018)
LabVIEW. https://www.ni.com/en-tr/shop/labview.html. Accessed 07 Aug 2020
Python. https://www.python.org/. Accessed 07 Aug 2020
Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice. OTexts, Melbourne (2018)
Matlab. https://www.mathworks.com/products/matlab.html. Accessed 07 Aug 2020
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This work was funded by TUBITAK with grant number 217M078.
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Bilal, D.K., Unel, M., Tunc, L.T. (2020). Improved Vision Based Pose Estimation for Industrial Robots via Sparse Regression. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_11
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