Industrial robotics industry is presently experiencing significant growth and is generally recognized as a crucial element within the industrial sector. The technology offered by this system is standardized and well-suited for a wide range of automated operations. This research investigates the industrial robotics industry and its use of standardized technologies to automate diverse operational procedures. This article explores the two primary tactics used in the process of robotization, with the diverse levels of cooperation seen between human beings and robots. The present study examines the control and programming approaches used in the field of information retrieval, together with the notable technological advancements that have arisen within this area. Moreover, it incorporates the many challenges and limitations faced during the installation of automated industrial robot systems. This research places particular emphasis on the use of computer vision-based approaches, deep reinforcement learning techniques, simulations, and synthetic data within the domain of industrial robotics. The article ends by providing an analysis of novel control methodologies and the use of external coordinators in the programming of industrial robots.
Keywords
Industrial Robot, Computer Vision-Based Technologies, Industrial Robot Control System, Automation Systems, Innovative Control Approaches, Open System Architecture.
T. Haidegger et al., “Applied ontologies and standards for service robots,” Robotics and Autonomous Systems, vol. 61, no. 11, pp. 1215–1223, Nov. 2013, doi: 10.1016/j.robot.2013.05.008.
G. O’Regan, “Unimation,” in Springer eBooks, 2015, pp. 219–223. doi: 10.1007/978-3-319-21464-1_34.
N. M. Kumar, K. Sudhakar, M. Samykano, and V. Jayaseelan, “On the technologies empowering drones for intelligent monitoring of solar photovoltaic power plants,” Procedia Computer Science, vol. 133, pp. 585–593, Jan. 2018, doi: 10.1016/j.procs.2018.07.087.
S. Wang, C. Zhang, C. Liu, D. Li, and H. Tang, “Cloud-assisted interaction and negotiation of industrial robots for the smart factory,” Computers & Electrical Engineering, vol. 63, pp. 66–78, Oct. 2017, doi: 10.1016/j.compeleceng.2017.05.025.
L. Gualtieri, E. Rauch, and R. Vidoni, “Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review,” Robotics and Computer-Integrated Manufacturing, vol. 67, p. 101998, Feb. 2021, doi: 10.1016/j.rcim.2020.101998.
H. Golnabi and A. Asadpour, “Design and application of industrial machine vision systems,” Robotics and Computer-Integrated Manufacturing, vol. 23, no. 6, pp. 630–637, Dec. 2007, doi: 10.1016/j.rcim.2007.02.005.
A. Garibaldi, “5 Numbers That Explain The Talent Gap — And What Leaders Can Do About It,” Forbes, May 10, 2023. [Online]. Available: https://www.forbes.com/sites/guild/2023/05/10/5-numbers-that-explain-the-talent-gap---and-what-leaders-can-do-about-it/
R. Poppe, “A survey on vision-based human action recognition,” Image and Vision Computing, vol. 28, no. 6, pp. 976–990, Jun. 2010, doi: 10.1016/j.imavis.2009.11.014.
D. Moreno et al., “Fundamental studies in Design-by-Analogy: A focus on domain-knowledge experts and applications to transactional design problems,” Design Studies, vol. 35, no. 3, pp. 232–272, May 2014, doi: 10.1016/j.destud.2013.11.002.
F. Dimeas and N. Α. Aspragathos, “Online Stability in Human-Robot Cooperation with Admittance Control,” IEEE Transactions on Haptics, vol. 9, no. 2, pp. 267–278, Apr. 2016, doi: 10.1109/toh.2016.2518670.
H. Munz, J. Braumann, and S. Brell-Çokcan, “Direct Robot Control with mxAutomation: A New Approach to Simple Software Integration of Robots in Production Machinery, Automation Systems, and New Parametric Environments,” in Springer eBooks, 2016, pp. 440–447. doi: 10.1007/978-3-319-26378-6_35.
R. G. V. Andrade and L. Vinces, “A Comparative Analysis of Kinematics of Industrial Robot KUKA KR 60–3 Using Scientific Computing Languages,” 2020 Asia Conference on Computers and Communications (ACCC), Sep. 2020, doi: 10.1109/accc51160.2020.9347897.
V. Weistroffer, F. Keith, A. Bisiaux, C. Andriot, and A. Lasnier, “Using Physics-Based digital twins and extended reality for the safety and ergonomics evaluation of cobotic workstations,” Frontiers in Virtual Reality, vol. 3, Feb. 2022, doi: 10.3389/frvir.2022.781830.
Y.-F. Chang, “Buffered DDA command generation in a CNC,” Control Engineering Practice, vol. 11, no. 7, pp. 797–804, Jul. 2003, doi: 10.1016/s0967-0661(02)00187-9.
C. Upchurch, M. Kuby, M. A. Zoldak, and A. Barranda, “Using GIS to generate mutually exclusive service areas linking travel on and off a network,” Journal of Transport Geography, vol. 12, no. 1, pp. 23–33, Mar. 2004, doi: 10.1016/j.jtrangeo.2003.10.001.
A. K. Seth, “A MATLAB toolbox for Granger causal connectivity analysis,” Journal of Neuroscience Methods, vol. 186, no. 2, pp. 262–273, Feb. 2010, doi: 10.1016/j.jneumeth.2009.11.020.
F. Sanfilippo, L. I. Hatledal, H. Zhang, M. Fago, and K. Y. Pettersen, “Controlling KUKA Industrial Robots: Flexible Communication Interface JOPENShowVAR,” IEEE Robotics & Automation Magazine, vol. 22, no. 4, pp. 96–109, Dec. 2015, doi: 10.1109/mra.2015.2482839.
F. Chinello, S. Scheggi, F. Morbidi, and D. Prattichizzo, “KUKA Control Toolbox,” IEEE Robotics & Automation Magazine, vol. 18, no. 4, pp. 69–79, Dec. 2011, doi: 10.1109/mra.2011.942120.
J. Golz, T. Wruetz, D. Eickmann, and R. Biesenbach, “RoBO-2L, a Matlab interface for extended offline programming of KUKA industrial robots,” 2016 11th France-Japan & 9th Europe-Asia Congress on Mechatronics (MECATRONICS) /17th International Conference on Research and Education in Mechatronics (REM), Jun. 2016, doi: 10.1109/mecatronics.2016.7547117.
T. Shu, S. Gharaaty, W. Xie, A. Joubair, and I. A. Bonev, “Dynamic path tracking of industrial robots with high accuracy using photogrammetry sensor,” IEEE-ASME Transactions on Mechatronics, vol. 23, no. 3, pp. 1159–1170, Jun. 2018, doi: 10.1109/tmech.2018.2821600.
S. C. Jagers, J. Martinsson, and S. Matti, “The impact of compensatory measures on public support for carbon taxation: an experimental study in Sweden,” Climate Policy, vol. 19, no. 2, pp. 147–160, May 2018, doi: 10.1080/14693062.2018.1470963.
C. Brecher, A. Verl, A. Lechler, and M. Servos, “Open control systems: state of the art,” Production Engineering, vol. 4, no. 2–3, pp. 247–254, Apr. 2010, doi: 10.1007/s11740-010-0218-5.
C. A. Chung, “A cost-effective approach for the development of an integrated PC-PLC-robot system for industrial engineering education,” IEEE Transactions on Education, Jan. 1998, doi: 10.1109/13.728266.
S. P. Jadhav, A. Srinivas, P. Dipak Raghunath, M. Ramkumar Prabhu, J. Suryawanshi, and A. Haldorai, “Deep learning approaches for multi-modal sensor data analysis and abnormality detection,” Measurement: Sensors, vol. 33, p. 101157, Jun. 2024, doi: 10.1016/j.measen.2024.101157.
A.Haldorai, B. Lincy R, S. M, and M. Balakrishnan, “An improved single short detection method for smart vision-based water garbage cleaning robot,” Cognitive Robotics, vol. 4, pp. 19–29, 2024, doi: 10.1016/j.cogr.2023.11.002.
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Andrea Bersamin
Andrea Bersamin
Department of Mathematics, Kenai Peninsula College, Soldotna, AK, United States.
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Cite this article
Andrea Bersamin and Eugenie Euskirchen, “Programming Methods for Industrial Robotics and Expanding Applications”, Journal of Robotics Spectrum, vol.2, pp. 001-012, 2024. doi: 10.53759/9852/JRS202402001.
