Open Access Open Access  Restricted Access Subscription or Fee Access

Modeling, Simulation and Implementation of Visual Servoing for a 6-DOF Industrial Robot


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iremos.v16i6.23662

Abstract


Visual servoing involves controlling robotic systems in a closed loop using vision sensors. A camera, strategically positioned, captures information about the pose of the robot's end effector and the target pose. This control law utilizes the gathered information to calculate the image feature error between the current and final poses. The error then guides the control law in generating the robot velocity screw. Visual feedback, as a technology, excels in processing vast amounts of data from non-contact sensors. This rich dataset proves invaluable for decision-making in control systems. The applications of visual servoing span across industrial, medical, and automotive sectors. The focus of this paper is to simulate and implement visual feedback using a 6-degree-of-freedom industrial robot, particularly for pick-and-place applications. The article presents kinematic models of the robot, both forward and inverse, along with simulations in MATLAB. Experimental evaluation with an industrial robot validates the results.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Robot Visual Servoing; Machine Vision; Forward Kinematic Model; Inverse Kinematic Model; File Transfer Protocol; IBVS

Full Text:

PDF


References


Forsyth, D. A., & Ponce, J Computer Vision: A Modern Approach (2nd ed). (Pearson Education UK, 2015).

Malamas, E. N., Petrakis, E. G. M., Zervakis, M., Petit, L., & Legat, J.-D. (2003). A survey on industrial vision systems, applications, and tools. Image and Vision Computing, 21(2), 171-188.
https://doi.org/10.1016/S0262-8856(02)00152-X

Golnabi, H., & Asadpour, A. (2007). Design and application of industrial machine vision systems. Robotics and Computer-Integrated Manufacturing, 23(6), 630-637
https://doi.org/10.1016/j.rcim.2007.02.005

Mazouzi, A., Yssaad, S., Karas, I., A Hybrid Method for Road Marking Detection, (2022) International Review of Automatic Control (IREACO), 15 (1), pp. 38-43.
https://doi.org/10.15866/ireaco.v15i1.21708

Sánchez Ocaña, W., Delgado, E., Jácome, E., Moreano, L., Designing and Implementing a Didactic Module of Artificial Vision for the Selection of Objects According to Colors and Morphological Characteristics, (2020) International Review of Automatic Control (IREACO), 13 (5), pp. 244-254.
https://doi.org/10.15866/ireaco.v13i5.19089

Fernandes, A. O., Moreira, L. F. E., & Mata, J. M. (2011). Machine vision applications and development aspects. 2011 9th IEEE International Conference on Control and Automation (ICCA), 1274-1278.
https://doi.org/10.1109/ICCA.2011.6138014

Bouhalassa, L., Benchikh, L., Ahmed-Foitih, Z., Bouzgou, K., Path Planning of the Manipulator Arm FANUC Based on Soft Computing Techniques, (2020) International Review of Automatic Control (IREACO), 13 (4), pp. 171-181.
https://doi.org/10.15866/ireaco.v13i4.18506

Lin, S.-F., Chen, Y.-Y., & Liu, S.-C. (2006). A Vision-Based Parking Lot Management System. 2006 IEEE International Conference on Systems, Man and Cybernetics, 2897-2902.
https://doi.org/10.1109/ICSMC.2006.385314

Bouganis, A., & Shanahan, M. (2007). A Vision-Based Intelligent System for Packing 2-D Irregular Shapes. IEEE Transactions on Automation Science and Engineering, 4(3), 382-394.
https://doi.org/10.1109/TASE.2006.887158

Cheng, Y., & Jafari, M. A. (2008). Vision-Based Online Process Control in Manufacturing Applications. IEEE Transactions on Automation Science and Engineering, 5(1), 140-153.
https://doi.org/10.1109/TASE.2007.912058

Loga Nga Fouda, J., Kom, C., Moffo Lonla, B., Mbihi, J., Implementation of a Decoupled Digital Feedback Control Architecture on an Arduino - Free RTOS Real-Time Embedded System for Input Delay Robotic Servomechanisms, (2021) International Review of Automatic Control (IREACO), 14 (4), pp. 201-213.
https://doi.org/10.15866/ireaco.v14i4.21223

Nerakae, P., Uangpairoj, P., & Chamniprasart, K. (2016). Using Machine Vision for Flexible Automatic Assembly System. Procedia Computer Science, 96, 428-435.
https://doi.org/10.1016/j.procs.2016.08.090

Yao, Y., & Hu, Y. (2017). Recognition and location of solar panels based on machine vision. 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), 7-12.
https://doi.org/10.1109/ACIRS.2017.7986055

Kopparapu, S. K. (2006). Lighting design for machine vision application. Image and Vision Computing, 24(7), 720-726.
https://doi.org/10.1016/j.imavis.2005.12.016

Hutchinson, S., Hager, G. D., & Corke, P. I. (1996). A tutorial on visual servo control. IEEE Transactions on Robotics and Automation, 12(5), 651-670.
https://doi.org/10.1109/70.538972

Chaumette, F., & Hutchinson, S. (2006). Visual servo control. I. Basic approaches. IEEE Robotics & Automation Magazine, 13(4), 82-90.
https://doi.org/10.1109/MRA.2006.250573

Chaumette, F. (1998). Potential problems of stability and convergence in image-based and position-based visual servoing. In D. J. Kriegman, G. D. Hager, & A. S. Morse (Eds.), The confluence of vision and control (Vol. 237, pp. 66-78). Springer London.
https://doi.org/10.1007/BFb0109663

T, N., Peters, D., & Sommer, G. (2010). Models and Control Strategies for Visual Servoing. In R.-F. Fung (Ed.), Visual Servoing. InTech.
https://doi.org/10.5772/8550

Nagrath, M. Robotics, and Control. (Tata McGraw-Hill 2003).

El Farnane, A., Youssefi, M., Mouhsen, A., Kachmar, M., Oumouh, A., El Aissaoui, A., Trajectory Tracking of Autonomous Driving Tricycle Robot with Fuzzy Control, (2022) International Review of Automatic Control (IREACO), 15 (2), pp. 80-86.
https://doi.org/10.15866/ireaco.v15i2.21719

Bouhalassa, L., Benchikh, L., Ahmed-Foitih, Z., Bouzgou, K., Path Planning of the Manipulator Arm FANUC Based on Soft Computing Techniques, (2020) International Review of Automatic Control (IREACO), 13 (4), pp. 171-181.
https://doi.org/10.15866/ireaco.v13i4.18506

Jumaa Alkurawy, L., Saleh, M., Humood, K., Modeling, Identification and Control of Inverse Kinematic of PUMA Robots, (2020) International Journal on Engineering Applications (IREA), 8 (4), pp. 140-147.
https://doi.org/10.15866/irea.v8i4.18742

Product manual, ABB, IRB, 1200:
https://library.e.abb.com/public/6c36b5c760d1463e9343006caf09babe/3HAC046983%20PM%20IRB%201200-en.pdf


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize