Comparative Analysis of Gwo and Mtde Algorithms for Inverse Kinematic Analysis of Six Dof Robot Manipulator

Year 2024, Volume: 26 Issue: 78, 449 - 457, 27.09.2024

Gökçe Sena Hocaoğlu , Nazlıcan Çavli , Emrah Benli

https://doi.org/10.21205/deufmd.2024267811

Abstract

With the increase in the number of joints of robot manipulators or due to their geometric structures, inverse kinematic analysis of the manipulator becomes difficult. In this case, intelligent algorithms are used. In this study, inverse kinematic analysis of the six-degrees-of-freedom (DOF) Mitsubishi Melfa RV-7FL-D robot manipulator was performed using Grey Wolf Optimization (GWO) algorithm and Multi-trial Vector-based Differential Evolution (MTDE) algorithm. The initial step involved designing three scenarios and determining their respective targeted position values. Subsequently, the mathematical modelling of the robot manipulator used in the study was conducted on MATLAB. Intelligent algorithms were employed to determine the axis angles necessary to ensure that the manipulator's end-effector reaches the targeted position. The results obtained with the traditional and improved GWO algorithm were compared with the results obtained with the MTDE algorithm. When the data obtained as a result of the optimization were evaluated, it has been observed that the MTDE algorithm gave much faster joint angle values. When the literature was searched for the inverse kinematics analysis made with optimization algorithms, no study was found in which the MTDE algorithm was used. The study aims to contribute to the literature in inverse kinematics analysis with optimization using the MTDE algorithm.

Keywords

Grey wolf optimization, multi-trial vector-based differential evolution, inverse kinematic analysis, robot manipulator

Altı Serbestlik Dereceli Robot Manipülatörün Ters Kinematik Analizi Için Gko Ve Çdde Algoritmalarının Karşılaştırmalı Analizi

Abstract

Robot manipülatörlerinin eklem sayıları artmasıyla veya geometrik yapılarından kaynaklı manipülatörün ters kinematik analizinin yapılması zorlaşır. Bu durumda akıllı algoritmalara başvurulur. Bu çalışmada altı serbestlik derecesine (SD) sahip Mitsubishi Melfa RV-7FL-D robot manipülatörün ters kinematik analizi Gri Kurt Optimizasyon (GKO) algoritması ve Çoklu Denemeli Diferansiyel Evrim (ÇDDE) algoritması kullanılarak yapılmıştır. İlk adım için 3 adet senaryo tasarlanarak bu senaryolara ait hedeflenen konum değerleri belirlenmiştir. Ardından MATLAB üzerinde, çalışmada kullanılan robot manipülatörünün kinematiği matematiksel olarak modellenmiştir. Manipülatörün uç-efektörünün hedeflenen konuma gelmesini sağlamak için gerekli eksen açıları akıllı algoritmalar ile bulunmuştur. Geleneksel ve Geliştirilmiş GKO algoritmasıyla elde edilen sonuçlar ÇDDE algoritmasıyla elde edilen sonuçlarla karşılaştırılmıştır. Optimizasyon sonucu alınan veriler değerlendirildiğinde ÇDDE algoritmasının çok daha hızlı eklem açı değerlerini verdiği sonucuna varılmıştır. Optimizasyon algoritmaları ile yapılan ters kinematik analizi için literatür taraması yapıldığında ÇDDE algoritmasının kullanıldığı bir çalışmaya rastlanmamıştır. Çalışma, ÇDDE algoritmasını kullanarak optimizasyonla ters kinematik analizinde literatüre katkı sağlamayı amaçlamaktadır.

Keywords

Gri kurt optimizasyonu, çoklu denemeli diferansiyel evrim, ters kinematik analizi, robot manipülatör

References

• [1] Zhang Q., Zhang Y., Gao L. and Cao H. 2021. Analysis of the effect of objective function on the performance of the algorithm for the inverse kinematic of manipulator, 2021 14th International Symposium on Computational Intelligence and Design (ISCID), 11-12 December, Hangzhou, China, 91-95.
• [2] Yiyang L., Xi J., Hongfei B., Zhining W. and Liangliang S. 2021. A General Robot Inverse Kinematics Solution Method Based on Improved PSO Algorithm, IEEE Access, vol. 9, pp. 32341-32350. DOI: 10.1109/ACCESS.2021.3059714.
• [3] Alkayyali M. and Tutunji T. A. 2019. PSO-based Algorithm for Inverse Kinematics Solution of Robotic Arm Manipulators, 2019 20th International Conference on Research and Education in Mechatronics (REM), 23-24 May, Wels, Austria, 1-6.
• [4] Dereli S. and Köker R. 2020. Calculation Of The Inverse Kinematics Solution Of The 7-DOF Redundant Robot Manipulator By The Firefly Algorithm And Statistical Analysis Of The Results In Terms Of Speed And Accuracy, Inverse Probl. Sci. Eng., vol. 28(5), pp. 601-613. DOI: 10.1080/17415977.2019.1602124
• [5] Ram R.V., Pathak P.M. and Junco S.J. 2019. Inverse kinematics of mobile manipulator using bidirectional particle swarm optimization by manipulator decoupling, Mech. Mach. Theory, vol. 131, pp. 385-405. DOI: 10.1016/j.mechmachtheory.2018.09.022
• [6] Dereli S. and Köker R. 2018. IW-PSO Approach To The Inverse Kinematics Problem Solution Of A 7-DOF Serial Robot Manipulator, Sigma J. Eng. Nat. Sci., vol. 36(1), pp. 77-85.
• [7] Marić F., Giamou M., Hall A. W., Khoubyarian S., Petrović I. and Kelly J. 2022. Riemannian Optimization for Distance-Geometric Inverse Kinematics. IEEE Transactions on Robotics, vol. 38(3), pp. 1703-1722. DOI: 10.1109/TRO.2021.3123841.
• [8] Dereli S. 2021. A new modified grey wolf optimization algorithm proposal for a fundamental engineering problem in robotics, Neural Computing and Applications., vol. 33, pp. 14119–14131. DOI: 10.1007/s00521-021-06050-2
• [9] Amiri M.S. and Ramli R. 2021. Intelligent Trajectory Tracking Behavior of a Multi-Joint Robotic Arm via Genetic–Swarm Optimization for the Inverse Kinematic Solution, Sensors, vol. 21(9), pp. 3171. DOI: 10.3390/s21093171
• [10] Özen F., Tukel D. and Dimirovski G. 2017. Synchronized dancing of an industrial manipulator and humans with arbitrary music, Acta Polytech. Hung., vol. 14(2), pp. 151-169. DOI: 10.12700/APH.14.2.2017.2.8
• [11] K. Zhao, Y. Liu and K. Hu, 2022. Optimal Pattern Synthesis of Array Antennas Using Improved Grey Wolf Algorithm, 2022 IEEE 12th International Conference on Electronics Information and Emergency Communication (ICEIEC), 15-17 July, Beijing, China, 172-175.
• [12] Gai W., Qu C., Liu J. and Zhang J. 2018. An improved grey wolf algorithm for global optimization, 2018 Chinese Control And Decision Conference (CCDC), pp. 2494-2498, Shenyang, China, DOI: 10.1109/CCDC.2018.8407544.
• [13] Nadimi-Shahraki M. H., Taghian S., Mirjalili S. and Faris H. 2020. MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems, Appl. Soft Comput., vol. 97(A), pp. 106761. DOI: 10.1016/j.asoc.2020.106761