Abstract
Robotic manipulators are becoming increasingly popular nowadays with applications in almost every industry and production line. It is difficult but essential to create a common algorithm for the different types of manipulators present in today’s market so that automation can be achieved at a faster rate. This paper aims to present a real-time implementation of a method to control a Tal Brabo! Robotic manipulator to move along a given weld line in order to be utilized in factories for increasing production capacity and decreasing production time. The controller used here is provided by Trio, whose ActiveX component is interfaced to MATLAB. Images were captured to identify weld lines in every possible alignment to find points of interest and the neural network was trained in order to follow a given weld line once the work-piece was placed on the work-table.
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S. Mitsi, K.D. Bouzakis, G. Mansour, D. Sagris, G. Maliaris, Off-line programming of an industrial robot for manufacturing, Int. J. Adv. Manuf. Technol. 26 (2005), 262–267.
B.-O. Kam, Y.-B. Jeon, S.-B. Kim, Motion control of two-wheeled welding mobile robot with seam tracking sensor, in: 2001 IEEE International Symposium on Industrial Electronics Proceedings (ISIE) (Cat. No.01TH8570), Vol. 2, IEEE, Pusan, 12–16 June 2001, pp. 851–856.
T. Säntti, J.K. Poikonen, O. Lahdenoja, M. Laiho, A. Paasio, Online seam tracking for laser welding with a vision chip and FPGA enabled camera system, in: 2015 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Lisbon, 24–27 May 2015, pp. 1985–1988.
R. Ramya, R. Anandanatarajan, R. Priya, G. Arul Selvan, Applications of fuzzy logic and artificial neural network for solving real world problem, in: 2012 IEEE International Conference On Advances in Engineering, Science and Management (ICAESM), IEEE, Nagapattinam, Tamil Nadu, 30–31 March 2012, pp. 443–448.
T.J. Sachin, V.G. Sanjana, V.P. Darshini, A review on neural network and its implementation on breast cancer detection, in: 2016 International Conference on Communication and Signal Processing (ICCSP), IEEE, Melmaruvathur, 6–8 April 2016, pp. 1727–1730.
L. Molina, E.A.N. Carvalho, E.O. Freire, J.R. Montalvão-Filho, F.deA. Chagas, A robotic vision system using a modified hough transform to perform weld line detection on storage tanks, in: 2008 IEEE Latin American Robotic Symposium, Natal, Rio Grande do Norte, 29–30 October 2008, pp. 45–50.
J.N. Pires, J.M.G. Sa da Costa, Running an industrial robot from a typical personal computer, in: 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196), Vol. 1, IEEE, Lisboa, 7–10 September 1998, pp. 267–270.
G. Deng, L.W. Cahill, An adaptive Gaussian filter for noise reduction and edge detection, in: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, Vol. 3, IEEE, San Francisco, CA, 31 October 6 November 1993, pp. 1615–1619.
L.B. Boudaoud, A. Sider, A. Tari, A new thinning algorithm for binary images, in: 2015 3rd International Conference on Control, Engineering and Information Technology (CEIT), IEEE, Tlemcen, 25–27 May 2015, pp. 1–6.
S. Mishra, R. Prusty, P.K. Hota, Analysis of Levenberg–Marquardt and Scaled Conjugate gradient training algorithms for artificial neural network based LS and MMSE estimated channel equalizers, in: 2015 International Conference on Man and Machine Interfacing (MAMI), IEEE, Bhubaneswar, Odisha, India, 17–19 December 2015, pp. 1–7.
A. Payal, C.S. Rai, B.V.R. Reddy, Comparative analysis of Bayesian regularization and Levenberg–Marquardt training algorithm for localization in wireless sensor network, in: 2013 15th International Conference on Advanced Communications Technology (ICACT), IEEE, PyeongChang, 27–30 January 2013, pp. 191–194.
H. Tourajizadeh, S. Manteghi, S.R. Nekoo, Numerical and neural network modeling of motors of a robot, in: 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM), IEEE, Tehran, 7–9 October 2015, pp. 43–48.
M. Stoica, G.A. Calangiu, F. Sisak, I. Sarkany, A method proposed for training an artificial neural network used for industrial robot programming by demonstration, in: 2010 12th International Conference on Optimization of Electrical and Electronic Equipment, IEEE, Basov, 20–22 May 2010.
J.C. Larsen, N.J. Ferrier, A case study in vision based neural network training for control of a planar, large deflection, flexible robot manipulator, in: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), Vol. 3, IEEE, Sendai, 28 September 2 October 2004, pp. 2924–2929.
R.B. Rusu, S. Cousins, 3D is here: Point Cloud Library (PCL), in: 2011 IEEE International Conference on Robotics and Automation, IEEE, Shanghai, 9–13 May 2011, pp. 1–4.
G. Khodamipour, M. Yaghoobi, A new approach to optimal ANFIS controller on manipulator robots, in: 2015 International Congress on Technology, Communication and Knowledge (ICTCK), IEEE, Mashhad, 11–12 November 2015, pp. 9–16.
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Rao, S.H., Kalaichelvi, V. & Karthikeyan, R. Tracing a Weld Line using Artificial Neural Networks. Int J Netw Distrib Comput 6, 216–223 (2018). https://doi.org/10.2991/ijndc.2018.4.6.4
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DOI: https://doi.org/10.2991/ijndc.2018.4.6.4