Modares Mechanical Engineering

Modares Mechanical Engineering

Using Edge Computing in Improving the Performance of Machining Robots

Authors
Mohammad Reza Karimianrizi
Saeid Farahat
10.22034/mme.23.10.263
Abstract
Optimizing energy consumption in industrial robots can reduce operating costs, improve performance, and extend the life of the robot during manufacturing. In recent years, with the progress of science and technology, new technologies such as cloud computing, big data, etc. have continuously emerged, and in particular, cloud computing technology has been used in robot research that improves the real-time performance of the designed robot. It can also provide high energy efficiency, low cost, etc. One of the most important aspects of this technology is its use in continuous monitoring of robots' performance, which can guarantee its optimal performance. In this research, first, an overview of the methods of reducing energy consumption is presented, and then the effectiveness of using edge computing technology in reducing energy is analyzed. For this purpose, the use of algorithms to optimize the performance of the robot, including its trajectory and working times, is controlled by the edge. The results of the simulations show that the energy consumption can be significantly reduced by using edge technology.
Keywords
Machining Robots
Robot Monitoring and Control
Edge Computing
Energy Optimization
[1] M. Pellicciari, G. Berselli, F. Leali, A. Vergnano, A method for reducing the energy consumption of pick-and-place industrial robots, Mechatronics 23 (2013) 326–334.
[2] L. Bukata, P. Šůcha, Z. Hanzálek, P. Burget, Energy optimization of robotic cells, IEEE Trans. Ind. Inf. 13 (2016) 92–102.
[3] D. Meike, M. Pellicciari, G. Berselli, Energy efficient use of multirobot production lines in the automotive industry: detailed system modeling and optimization, IEEE Trans. Autom. Sci. Eng. 11 (2013) 798–809.
[4] B. Zhou, Q. Wu, Decomposition-based bi-objective optimization for sustainable robotic assembly line balancing problems, J. Manuf. Syst. 55 (2020) 30–43
[5] E. Coronado, T. Kiyokawa, G.A.G. Ricardez, I.G. Ramirez-Alpizar, G. Venture, N. Yamanobe, Evaluating quality in human-robot interaction: a systematic search and classification of performance and human-centered factors, measures and metrics towards an industry 5.0, J. Manuf. Syst. 63 (2022) 392–410.
[6] M. Gadaleta, G. Berselli, M. Pellicciari, F. Grassia, Extensive experimental investigation for the optimization of the energy consumption of a high payload industrial robot with open research dataset, Robot. Comput. Integr. Manuf. 68 (2021) 102046.
[7] M. Brossog, M. Bornschlegl, J. Franke, Reducing the energy consumption of industrial robots in manufacturing systems, Int. J. Adv. Manuf. Technol. 78 (2015) 1315–1328.
[8] J. Liu, W. Xu, J. Zhang, Z. Zhou, D.T. Pham, Industrial cloud robotics towards sustainable manufacturing, International Manufacturing Science and Engineering Conference, American Society of Mechanical Engineers, 2016 V002T004A017
[9] A. Vergnano, C. Thorstensson, B. Lennartson, P. Falkman, M. Pellicciari, F. Leali, S. Biller, Modeling and optimization of energy consumption in cooperative multi-robot systems, IEEE Trans. Autom. Sci. Eng. 9 (2012) 423–428.
[10] L. Wang, A. Mohammed, X.V. Wang, B. Schmidt, Energy-efficient robot applications towards sustainable manufacturing, Int. J. Comput. Integr. Manuf. 31 (2018) 692–700.
[11] G. Pastras, A. Fysikopoulos, G. Chryssolouris, A theoretical investigation on the potential energy savings by optimization of the robotic motion profiles, Robot. Comput. Integr. Manuf. 58 (2019) 55–68.
[12] V. Zanotto, A. Gasparetto, A. Lanzutti, P. Boscariol, R. Vidoni, Experimental validation of minimum time-jerk algorithms for industrial robots, J. Intell. Rob. Syst. 64 (2011) 197–219.
[13] A. Mohammed, B. Schmidt, L. Wang, L. Gao, Minimizing energy consumption for robot arm movement, Procedia Cirp 25 (2014) 400–405
[14] L. Scalera, I. Palomba, E. Wehrle, A. Gasparetto, R. Vidoni, Natural motion for energy saving in robotic and mechatronic systems, Appl. Sci. 9 (2019) 3516.
[15] Wang, Gang, Wenlong Li, Cheng Jiang, Dahu Zhu, Zhongwei Li, Wei Xu, Huan Zhao, and Han Ding. "Trajectory planning and optimization for robotic machining based on measured point cloud." IEEE transactions on robotics 38, no. 3 (2021): 1621-1637.
Modares Mechanical Engineering
Volume 23, Issue 10
October 2023
Pages 263-268