Automated manufacturing facilities are governed by resilient control systems that need little or negligible human interaction. Broadly speaking, an industrial controller is responsible for transmitting instructions to machinery in order to carry out a designated operation, while also receiving feedback data that enables it to oversee and ascertain the accurate implementation of those instructions. This article examines the several elements and software systems included in the control of industrial robots. This paper examines the significance of sensors, axis controllers, and actuators in attaining accurate control over industrial robots. The use of industrial Ethernet technology is emphasized as a viable approach to mitigate the issues associated with excessive wiring and interference. The essay also highlights the need of offline programming tools and impedance control in order to enhance programming efficiency and facilitate natural contact with robots. Furthermore, this paper examines the difficulties and progress made in the realm of robot control specifically in relation to tasks such as bin picking, assembly, and machining.
Keywords
Automated Manufacturing, Industrial Robot Control System, Robot Control Development, Point-To-Point Control, Automatic Control.
O. Boubaker, “The inverted pendulum: A fundamental benchmark in control theory and robotics,” International Conference on Education and e-Learning Innovations, Jul. 2012, doi: 10.1109/iceeli.2012.6360606.
P. Bondi, G. Casalino, and L. M. Gambardella, “On the iterative learning control theory for robotic manipulators,” IEEE Journal on Robotics and Automation, vol. 4, no. 1, pp. 14–22, Jan. 1988, doi: 10.1109/56.767.
F. A. Candelas et al., “Experiences on using Arduino for laboratory experiments of Automatic Control and Robotics,” IFAC-PapersOnLine, vol. 48, no. 29, pp. 105–110, Jan. 2015, doi: 10.1016/j.ifacol.2015.11.221.
H. Warnecke and G. Schiele, “Performance characteristics and performance testing of industrial robots — state of the art,” in Springer eBooks, 1985, pp. 5–17. doi: 10.1007/978-3-662-02440-9_2.
L. Á. Pérez, E. Diez, R. Usamentiaga, and D. García, “Industrial robot control and operator training using virtual reality interfaces,” Computers in Industry, vol. 109, pp. 114–120, Aug. 2019, doi: 10.1016/j.compind.2019.05.001.
J. Rose, A. E. Gamal, and A. Sangiovanni-Vincentelli, “Architecture of field-programmable gate arrays,” Proceedings of the IEEE, vol. 81, no. 7, pp. 1013–1029, Jul. 1993, doi: 10.1109/5.231340.
J. Eyre, “The digital signal processor Derby,” IEEE Spectrum, vol. 38, no. 6, pp. 62–68, Jun. 2001, doi: 10.1109/6.925269.
S. Prasad and S. Sanyal, “Design of Arbitrary Waveform Generator based on Direct Digital Synthesis Technique using Code Composer Studio Platform,” 2007 International Symposium on Signals, Circuits and Systems, Jul. 2007, doi: 10.1109/isscs.2007.4292709.
Z. Li-Qiu, J. An, R. Zhao, and M. Hairong, “Trajectory planning and simulation of industrial robot based on MATLAB and RobotStudio,” 2021 IEEE 4th International Conference on Electronics Technology (ICET), May 2021, doi: 10.1109/icet51757.2021.9451021.
J. Song, A. Pramanik, A. K. Basak, C. Prakash, S. Shankar, and S. Debnath, “Burr formation and its treatments—a review,” The International Journal of Advanced Manufacturing Technology, vol. 107, no. 5–6, pp. 2189–2210, Mar. 2020, doi: 10.1007/s00170-020-05203-2.
K. Nilsson, R. Johansson, A. Robertsson, R. Bischoff, T. Brogårdh, and M. Hägele, “Productive Robots and the SMErobot Project,” Computer Science , Lund Inst. Of Technology, Jan. 2005, [Online]. Available: https://lucris.lub.lu.se/ws/files/5958734/625583.pdf
D. Hemanand, N. P. G. Bhavani, S. Ayub, M. W. Ahmad, S. Narayanan, and A. Haldorai, “Multilayer vectorization to develop a deeper image feature learning model,” Automatika, vol. 64, no. 2, pp. 355–364, Dec. 2022, doi: 10.1080/00051144.2022.2157946.
H. D. Jeong, B. S. Park, S. T. Park, H. C. Min, and S. Lee, “Fault detection and identification method using observer-based residuals,” Reliability Engineering & System Safety, vol. 184, pp. 27–40, Apr. 2019, doi: 10.1016/j.ress.2018.02.007.
K. Dwivedi, U. Kaliyaperumal Subramanian, J. Kuruvilla, A. Thomas, D. Shanthi, and A. Haldorai, “Time-series data prediction problem analysis through multilayered intuitionistic fuzzy sets,” Soft Computing, vol. 27, no. 3, pp. 1663–1671, Apr. 2022, doi: 10.1007/s00500-022-07053-4.
C. Thomas, F. Busch, B. Kuhlenkötter, and J. Deuse, “Ensuring Human Safety with Offline Simulation and Real-time Workspace Surveillance to Develope a Hybrid Robot Assistance System for Welding of Assemblies,” in Springer eBooks, 2011, pp. 464–470. doi: 10.1007/978-3-642-23860-4_76.
N. Zaretskaya, “Zooming-in on higher-level vision: High-resolution fMRI for understanding visual perception and awareness,” Progress in Neurobiology, vol. 207, p. 101998, Dec. 2021, doi: 10.1016/j.pneurobio.2021.101998.
R. Benotsmane, L. Dudás, and G. Kovács, “Survey on New trends of robotic tools in the automotive industry,” in Lecture notes in mechanical engineering, 2020, pp. 443–457. doi: 10.1007/978-981-15-9529-5_38.
A. Song, J. Wu, Q. Gang, and W. Huang, “A novel self-decoupled four degree-of-freedom wrist force/torque sensor,” Measurement, vol. 40, no. 9–10, pp. 883–891, Nov. 2007, doi: 10.1016/j.measurement.2006.11.018.
M. Javaid, A. Haleem, R. P. Singh, and R. Suman, “Substantial capabilities of robotics in enhancing industry 4.0 implementation,” Cognitive Robotics, vol. 1, pp. 58–75, Jan. 2021, doi: 10.1016/j.cogr.2021.06.001.
R. W. Longman, “Iterative/Repetitive Learning Control: Learning from Theory, Simulations, and Experiments,” in Springer eBooks, 2012, pp. 1652–1657. doi: 10.1007/978-1-4419-1428-6_1640.
Acknowledgements
Author(s) thanks to University of Galway for research lab and equipment support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Gerry Adams
Gerry Adams
College of Science and Engineering, University of Galway, Galway, Ireland.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Mary Robinson and Gerry Adams, “Exploring Industrial Robot Control Systems: Components, Software and Applications”, Journal of Robotics Spectrum, vol.2, pp. 046-055, 2024. doi: 10.53759/9852/JRS202402005.
