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| Intelligent optimization of path control and accuracy enhancement of industrial robots using the DE algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 09 آبان 1404 اصل مقاله (424.76 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.35433.5278 | ||
| نویسندگان | ||
| Mohsen Madadi* ؛ Reza Etesami | ||
| Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| تاریخ دریافت: 04 مهر 1403، تاریخ پذیرش: 24 آبان 1403 | ||
| چکیده | ||
| This paper introduces the optimization of path control and accuracy enhancement of industrial robots using the Differential Evolution (DE) algorithm. DE, known for its simplicity and adaptability, efficiently minimizes path deviation, energy consumption, and accuracy errors, outperforming traditional methods like PID and MPC. Using a two-degree-of-freedom articulated robot model, the DE algorithm demonstrated superior performance in reducing time and energy costs while improving accuracy in complex environments. The results highlight DE’s potential for precision-critical tasks such as assembly and welding, offering a robust and flexible alternative for industrial applications. Future research will focus on extending DE’s capabilities to more complex robotic systems and refining its parameter tuning for enhanced performance in diverse operational settings. | ||
| کلیدواژهها | ||
| Differential Evolution؛ Industrial Robots؛ Path Control Optimization؛ Accuracy Enhancement؛ Metaheuristic Algorithms؛ Robotics Performance؛ Automation | ||
| مراجع | ||
| [1] C. Belta, A. Bicchi, M. Egerstedt, E. Frazzoli, E. Klavins, and G.J. Pappas, Symbolic planning and control of robot motion [grand challenges of robotics], IEEE Robotics Autom. Mag. 14 (2007), no. 1, 61–70. [2] R. Benotsmane and G. Kovacs, Optimization of energy consumption of industrial robots using classical PID and MPC controllers, Energies 16 (2023), no. 8, 3499. [3] T. Brito, J. Queiroz, L. Piardi, L.A. Fernandes, J. Lima, and P. Leitao, A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems, Procedia Manufact. 51 (2020), 11–18. [4] O. Ciszak, J. Juszkiewicz, and M. Suszynski, Programming of industrial robots using the recognition of geometric signs in flexible welding process, Symmetry 12 (2020), no. 9, 1429. [5] S. Dipta Das, V. Bain, and P. Rakshit, Energy optimized robot arm path planning using differential evolution in dynamic environment, Second Int. Conf. Intell. Comput. Control Syst., IEEE, 2018, pp. 1267–1272. [6] J. de Dios F. Mendez, H. Schieler, S. Bai, and O. Madsen, Force estimation and control of delta robot for assembly, IEEE Conf. Control Technol. Appl., IEEE, 2021, pp. 640–647. [7] A. Dzedzickis, J. Subacintie-Zemaitiene, E. Sutinys, U. Samukaite-Bubniene, and V. Bučinskas, Advanced applications of industrial robotics: New trends and possibilities, Appl. Sci. 12 (2021), no. 1, 135. [8] R. Etesami, M. Madadi, and F. Keynia, A new improved fruit fly optimization algorithm based on particle swarm optimization algorithm for function optimization problems, J. Mahani Math. Res. Center 13 (2024), no. 2. [9] R. Etesami, M. Madadi, F. Keynia, and A. Arabpour, Gaussian combined arms algorithm: A novel meta-heuristic approach for solving engineering problems, Evol. Intell. 18 (2025), no. 2, 1–36. [10] V. Gopinath, K. Johansen, M. Derelov, A. Gustafsson, and S. Axelsson, Safe collaborative assembly on a continuously moving line with large industrial robots, Robotics Comput.-Integrated Manufact. 67 (2021), 102048. [11] X. Luo, S. Li, S. Liu, and G. Liu, An optimal trajectory planning method for path tracking of industrial robots, Robotica 37 (2019), no. 3, 502–520. [12] A.T. Sadig, F.A. Raheem, and N. Abbas, Ant colony algorithm improvement for robot arm path planning optimization based on D strategy*, Int. J. Mech. Mechat. Engin. 21 (2021), no. 1, 96–111. [13] M. Soori, B. Arezoo, and R. Dastres, Optimization of energy consumption in industrial robots, a review, Cognitive Robotics 3 (2023), 142–157. [14] R. Storn and K. Price, Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim. 11 (1997), 341–359. [15] R. Sujith, R. Ajith Kumar, H. Vishnu, S. Dhanesh, and A.P. Sudheer, Experimental investigation and numerical validation of neuro fuzzy-based cartesian robot for soft material cutting, J. Appl. Res. Technol. 19 (2021), no. 5, 420–436. [16] S.H, Tay, W.H. Choong, and Hou Pin Yoong, A review of SCARA robot control system, IEEE Int. Conf. Artific. Intell. Engin. Technol., IEEE, 2022, pp. 1–6. [17] C. Yao, Y. Li, M. Dilshad Ansari, M. Ahmed Talab, and A. Verma, Optimization of industrial process parameter control using improved genetic algorithm for industrial robot, Paladyn J. Behav. Robotics 13 (2022), no. 1, 67–75. [18] T. Yifei, Z. Meng, L. Jingwei, L. Dongbo, and W. Yulin, Research on intelligent welding robot path optimization based on GA and PSO algorithms, IEEE Access 6 (2018), 65397–65404. [19] Kh. Zbiss, A. Kacem, M. Santillo, and A. Mohammadi, Automatic collision-free trajectory generation for collaborative robotic car-painting, IEEE Access 10 (2022), 9950–9959. [20] J.-H. Zhang, Y. Zhang, and Y. Zhou, Path planning of mobile robot based on hybrid multi-objective bare bones particle swarm optimization with differential evolution, IEEE Access 6 (2018), 44542–44555. | ||
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