A Comprehensive Research on The Use of Swarm Algorithms in The Inverse Kinematics Solution

Öz

A comprehensive study was presented on swarm algorithms used in the inverse kinematic solution which is the basis of robot control in this paper. Because it is a complex and difficult problem group, the inverse kinematic solution is an important problem especially in robot arms with a lot of joints. So, swarm optimization techniques which were inspired by the animals in the nature, are often used by researchers, because these techniques find the best solution in a particular solution space. Artificial bee colony, firefly algorithm and particle swarm algorithm are the swarm techniques mentioned in this study. Since, these algorithms are frequently used in inverse kinematic solution in the literature.

Anahtar Kelimeler

• Robotic,inverse kinematics,swarm algorithms

A Comprehensive Research on The Use of Swarm Algorithms in The Inverse Kinematics Solution

Öz

Bu çalışmada, robot kontrolünün temelinde bulunan ters kinematik çözümün bulunması için kullanılan sürü algoritmalarının kapsamlı bir araştırması sunulmuştur. Ters kinematik çözüm karmaşık ve zor problem grupları arasında olduğundan önemli bir problemdir. Özellikle bu durum robot kolunun çok fazla sayıda eklemi bulunduğunda çok daha zorlaşmaktadır. Bu nedenle doğadaki hayvanların davranışlarından esinlenilen sürü optimizasyonu teknikleri araştırmacılar tarafından sık sık kullanılmaktadır. Çünkü bu teknikler parçacık çözüm uzayındaki en iyi değeri elde etmektedirler. Yapay arı kolonisi, ateş böceği algoritması ve parçacık sürü algoritması bu çalışmada bahsi geçen tekniklerdir ve literatürde sıklıkla tercih edilmişlerdir.   

Anahtar Kelimeler

• Robotik,ters kinematik,sürü algoritmaları

Kaynakça

1. [1] Kennedy J. and Eberhart R.C., “Particle Swarm Optimization”, IEEE International Conference on Neural Networks, 1942-1948, (1995).
2. [2] Karaboğa D., Gorkemli B., Ozturk C. and Karaboga N, “A comprehensive survey: artificial bee colony (ABC) algorithm and applications”, Artif Intell Rev, 42: 21-57, (2014).
3. [3] Yang X.S., “Nature-Inspired Metaheuristic Algorithms, Firefly Algorithm”, Luniver Press Publishing, (2010).
4. [4] Albinati J., Oliveira S.E.L., Otero F.E.B. and Pappa G.L., “An ant colony-based semi-supervised approach for learning classification rules”, Swarm Intelligence, 9:315-341, (2015).
5. [5] Blum C. and Li X., “Swarm Intelligence in Optimization, Swarm Intelligence”, Springer Verlag Publishing, (2008). [6] Gao W., Liu S. and Huang L., “A global best artificial bee colony algorithm for global optimization”, Computational and Applied Mathematics, Vol.236, 2012, pp. 2741-2753.
6. [7] Yang X.S. and He X., “Firefly Algorithm: Recent Advances and Application”, Swarm Intelligence, 1:36-50, (2013).
7. [8] Dereli S. and Köker R, “In a research on how to use inverse kinematics solution of actual intelligent optimization method”, International Symposium on Innovative Technologies in Engineering and Science (ISITES 2016), Alanya, (2016).
8. [9] Wolf B., “Inspiration by Swarm, Swarm Intelligence Based Optimization”, Siarry P, Idoumghar L, Lepagnot J., Springer Publisher, (2016).

9. [10] Bogue R., “Swarm Intelligence and Robotics”, Industrial Robot: An International Journal, 35-488-495, (2008).
10. [11] Jevtic A. and Andına D., “Swarm Intelligence and Its Applications in Swarm Robotics”, Sixth International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, 2007.
11. [12] Küçük S. and Bingül Z., “Robot Kinematics: Forward and Inverse Kinematics, Industrial Robotics: Theory, Modelling and Control”, Pro Literatur Verlag, (2006).
12. [13] Çavdar T., Mohammed M. and Milani R.A., “A New Heuristic Approach for Inverse Kinematics of Robot Arms”, Advanced Science Letters, 19:329-333, (2013).
13. [14] Pham D.T., Castellani M. and Fahmy A.A., “Learning the Inverse Kinematics of a Robot Manipulator using the Bees Algorithm”, IEEE International Conference on Industrial Informatics, 493-498, (2008).
14. [15] Rokbani N., Casals A. and Alimi A.M., “IK-FA, a New Heuristic Inverse Kinematics Solver Using Firefly Algorithm”, Computational Intelligence Applications in Modeling and Control, 575: 369-395, (2015).
15. [16] Durmuş B, Timurtaş H., and Gün A., “An Inverse Kinematics Solution using Particle Swarm Optimization”, International Advanced Technologies Symposium, 4: 193-197, (2011).
16. [17] Ayyıldız M. and Çetinkaya K., “Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator”, Neural Computing and Applications, 27: 835-836, (2016).
17. [18] Huang H.C., Chen C.P. and Wang P.R., “Particle Swarm Optimization for Solving the Inverse Kinematics of 7-DOF Robotic Manipulators”, IEEE International Conference on Systems, Man, and Cybernetics, 2012.
18. [19] Dereli S. and Köker R., “Design and Analysis of Multi-Layer Artificial Neural Network Used for Training in Inverse Kinematic Solution of 7-DOF Serial Robot”, Gaziosmanpasa Journal of Scientific Research, 6: 60-71, (2017).
19. [20] Corke P., “A Simple and Systematic Approach to Assigning Denavit–Hartenberg Parameters”, IEEE Transactions on Robotics, 23: 590-594, (2007).
20. [21] Yang G., Mustafa S.K., Yeo S.H., Lin W. and Lim W.B., “Kinematic design of an anthropomimetic 7-DOF cable-driven robotic arm”, Frontiers of Mechanical Engineering, 6: 45-60, (2011).
21. [22] Rokbani N. and Alimi A.M., “Inverse Kinematics Using Particle Swarm Optimization, a Statistical Analysis”, International Conference On Design And Manufacturing, 1602-1611, (2013).
22. [23] Sun J., Fang W., Palade V., Wu X. and Xu W., “Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point”, Applied Mathematics and Computation, 218: 3763-3775, (2011).
23. [24] Karaboğa D., “An idea based on honey bee swarm for numerical optimization”, Erciyes University, Technicial Report, 2005.
24. [25] Akay B. and Karaboğa D., “A Modified Artificial Bee Colony algorithm for real-parameter optimization”, Information Science, 192: 120-142, (2012).
25. [26] Wang H., Cui Z., Sun H., Rahnamayan S. and Yang X.S., “Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism”, Soft Computing, 20: 1-15, (2016).
26. [27] Yang X.S., “Firefly Algorithm. Nature-Inspired Metaheuristic Algorithm”, Luniver Press Publishing, 79-90, (2008).