Robot Tutucu Problemi için Çok Stratejili Aritmetik Optimizasyon Algoritması

Yıl 2024, Cilt: 12 Sayı: 1, 108 - 116, 25.03.2024

Mustafa Yusuf Yıldırım , Rüştü Akay

https://doi.org/10.29109/gujsc.1383797

Öz

Günümüzde endüstriyel sistemlerde nesnelerin kavranması, taşınması ve sabitlenmesi için kullanılan robot tutucular önemli araçlar olarak öne çıkmaktadır. Özellikle robotik sistemlerde, bir nesneyi en az manevrayla zarar vermeden tutabilme yeteneği büyük önem taşımaktadır. Bundan dolayı, son yıllarda robot tutucularının tasarım optimizasyonu ilgi çeken bir araştırma konusu haline gelmiştir. Bu çalışmada bu tasarım problemi için aritmetik optimizasyon algoritması (AOA) iyileştirilmiş ve çok stratejili aritmetik optimizasyon algoritması (ÇSAOA) adında yeni bir algoritma önerilmiştir. Bu algoritmada hem orijinal AOA’nın güncelleme mekanizmasını modifiye edilmiş, hem de farklı bir güncelleme mekanizması eklenilerek kendinden uyarlanabilen bir algoritma haline getirilmiştir. Bu yaklaşım, en iyi güncelleme stratejisine odaklanarak problemi daha verimli bir şekilde çözmeye olanak sağlamıştır. ÇSAOA, robot tutucu problemine uygulandığında, orijinal algoritmaya göre hem performans hem de hesaplama süresi açısından daha iyi sonuçlar ürettiği gözlemlenmiştir. Ayrıca, bu yeni algoritma literatürdeki diğer benzer algoritmalarla karşılaştırılmış ve önerilen ÇSAOA’nın daha performanslı algoritma olduğu görülmüştür.

Anahtar Kelimeler

Robot Tutucu, Aritmatik, Optimizasyon, Çoklu Strateji, Metasegisel

Destekleyen Kurum

Erciyes Üniversitesi Bilimsel Araştırma Projeleri

Proje Numarası

FDK-2021-11472

Teşekkür

Bu çalışma "Erciyes Üniversitesi Bilimsel Araştırma Projeleri" programı kapsamında FDK-2021-11472 proje numarası ile desteklenmiştir.

Multi-Strategy Arithmetic Optimization Algorithm for Robot Gripper Problem

Öz

Today, robot grippers used for grasping, moving and fixing objects in industrial systems stand out as important tools. Especially in robotic systems, the ability to hold an object without damaging it with minimal maneuvering is of great importance. Therefore, the design optimization of robot grippers has become an interesting research topic in recent years. In this study, Arithmetic Optimization Algorithm (AOA) has been improved for this design problem and a new algorithm called Multi-Strategy Arithmetic Optimization Algorithm (MSAOA) has been proposed. In this algorithm, the update mechanism of original AOA was modified and a different update mechanism was added, making it a self-adaptive algorithm. This approach allowed solving the problem more efficiently by focusing on the best update strategy. When MSAOA was applied to the robot gripper problem, it was observed that it produced better results in terms of both performance and calculation time compared to the original algorithm. Additionally, this new algorithm was compared with other similar algorithms in the literature and it was found that the proposed MSAOA was the most performant algorithm.

Anahtar Kelimeler

Robot Gripper, Arithmetic, Optimization, Multi-Strategy, Metaheuristic

Proje Numarası

FDK-2021-11472

Kaynakça

• [1] Saravanan, R., Ramabalan, S., Ebenezer, N. G. R., Dharmaraja, C. Evolutionary multi criteria design optimization of robot grippers. Applied Soft Computing. 2009; 9(1): 159-172.
• [2] Avder, A. Robot tutucuların optimum tasarımı için çok amaçlı hibrit bir yöntem önerisi, Yüksek Lisans Tezi, Gazi Üniversitesi Bilişim Enstitüsü, Ankara. 2019.
• [3] Yıldız, B. S., Pholdee, N., Bureerat, S., Yıldız, A. R., Sait, S. M. Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm. Expert Systems. 2021; 38(3): e12666.
• [4] Rao, R. V., Waghmare, G. Design optimization of robot grippers using teaching-learning-based optimization algorithm. Advanced Robotics. 2015; 29(6): 431-447.
• [5] Datta, R., Pradhan, S., Bhattacharya, B. Analysis and design optimization of a robotic gripper using multiobjective genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2015; 46(1): 16-26.
• [6] Mahanta, G. B., Rout, A., B.B.V.L. D., Biswal, B. B. An improved multi-objective antlion optimization algorithm for the optimal design of the robotic gripper. Journal of Experimental & Theoretical Artificial Intelligence. 2020; 32(2): 309-338.
• [7] Dong, H., Asadi, E., Qiu, C., Dai, J., Chen, I. M. Geometric design optimization of an under-actuated tendon-driven robotic gripper. Robotics and Computer-Integrated Manufacturing. 2018; 50: 80-89.
• [8] Zhong, J., Yuan, X., Du, B., Hu, G., Zhao, C. An lévy flight based honey badger algorithm for robot gripper problem. 2022 7th International Conference on Image, Vision and Computing (ICIVC). 2022; 901-905.
• [9] Dörterler, M., Atila, Ü., Durgut, R., Şahin, İ. Analyzing the performances of evolutionary multi-objective optimizers on design optimization of robot gripper configurations. Turkish Journal of Electrical Engineering and Computer Sciences. 2021; 29(1): 349-369.
• [10] Hassan, A., Abomoharam, M. Modeling and design optimization of a robot gripper mechanism. Robotics and Computer-Integrated Manufacturing. 2017; 46: 94-103.
• [11] Datta, R., Deb, K. Optimizing and deciphering design principles of robot gripper configurations using an evolutionary multi-objective optimization method. KanGAL Report 2011002. 2011; 1-10.
• [12] Jia, J., Sun, X., Liu, T., Tang, J., Wang, J., Hu, X. Structural optimization design of dual robot gripper unloading device based on intelligent optimization algorithms and generative design. Sensors. 2023; 23(19): 8298.
• [13] Wang, R., Zhang, X., Zhu, B., Zhang, H., Chen, B., Wang, H. Topology optimization of a cable-driven soft robotic gripper. Structural and Multidisciplinary Optimization. 2020; 62: 2749-2763.
• [14] Liu, C. H., Chen, T. L., Chiu, C. H., Hsu, M. C., Chen, Y., Pai, T. Y., Chiang, Y. P. Optimal design of a soft robotic gripper for grasping unknown objects. Soft Robotics. 2018; 5(4): 452-465.
• [15] Sun, Y., Liu, Y., Pancheri, F., Lueth, T. C. Larg: A lightweight robotic gripper with 3-d topology optimized adaptive fingers. IEEE/ASME Transactions on Mechatronics. 2022; 27(4): 2026-2034.
• [16] Osyczka, A., Krenich, S. Some methods for multicriteria design optimization using evolutionary algorithms. Journal of Theoretical and Applied Mechanics. 2004; 42(3): 565-584.
• [17] Kumar, A., Wu, G., Ali, M. Z., Mallipeddi, R., Suganthan, P. N., Das, S. A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm and Evolutionary Computation. 2020; 56: 100693.
• [18] Hu, G., Zhong, J., Du, B., Wei, G. An enhanced hybrid arithmetic optimization algorithm for engineering applications. Computer Methods in Applied Mechanics and Engineering. 2022; 394: 114901.
• [19] Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., Gandomi, A. H. The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering. 2021; 376: 113609.
• [20] Akay, R., Yildirim, M. Y. Multi-strategy and self-adaptive differential sine-cosine algorithm for multi-robot path planning. Expert Systems with Applications. 2023; 120849.
• [21] Rao, R. V., Savsani, V. J., Vakharia, D. P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design. 2011; 43(3): 303-315.
• [22] Rao, R. V., Waghmare, G. G. A new optimization algorithm for solving complex constrained design optimization problems. Engineering Optimization. 2017; 49(1): 60-83.
• [23] Kumar, A., Das, S., Zelinka, I. A modified covariance matrix adaptation evolution strategy for real-world constrained optimization problems. Genetic and Evolutionary Computation Conference Companion. 2020; 11-12.
• [24] Azizi, M., Talatahari, S., Giaralis, A. Optimization of engineering design problems using atomic orbital search algorithm. IEEE Access. 2021; 9: 102497-102519.
• [25] Uymaz, S. A. Evaluation of the most valuable player algorithm for solving real-world constrained optimization problems. Bilişim Teknolojileri Dergisi. 2021; 14(4): 345-353.
• [26] Wu, X., Li, S., Wu, F., Jiang, X. Teaching-learning optimization algorithm based on the cadre-mass relationship with tutor mechanism for solving complex optimization problems. Biomimetics. 2023; 8(6): 462.