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A Comparative Study for an Inverse Kinematics Solution of an Aerial Manipulator Based on the Differential Evolution Method and the Modifi ed Shuffl ed Frog-Leaping Algorithm

https://doi.org/10.17587/mau.19.714-724

Полный текст:

Аннотация

This paper focuses on the real-time kinematics solution of an aerial manipulator mounted on an aerial vehicle, the vehicle’s motion isn’t considered in this study. Robot kinematics using Denavit-Hartenberg model  was presented. The fundamental scope of this paper is to obtain a global online solution of design configurations with a weighted specific objective function and imposed constraints are fulfilled. Acknowledging the forward kinematics equations of the manipulator; the trajectory planning issue is consequently assigned to on an optimization issue. Several types of computing methods are documented in the literature and are well-known for solving complicated nonlinear functions. Accordingly, this study suggests two kinds of artificial intelligent techniques which are regarded as search methods; they are differential evolution (DE) method and modified shuffled frog-leaping algorithm (MSFLA). These algorithms are constrained metaheuristic and population-based approaches. moreover, they are able to solve the inverse kinematics problem taking into account the mobile platform additionally avoiding singularities since it doesn’t demand the inversion of a Jacobian matrix. Simulation results are carried out for trajectory planning of 6 degree-of-freedom (DOF) kinematically aerial manipulator and confirmed the feasibility and effectiveness of the supposed methods.

Об авторе

I. N. Ibrahim
Department of Mechatronics and Robotics, Kalashnikov Izhevsk State Technical University.
Россия

 Ph. D. Student.

Izhevsk, 426069.



Список литературы

1. Buss S. R. (2004). Introduction to inverse kinematics with jacobian transpose, pseudoinverse and damped least squares methods. IEEE Journal of Robotics and Automation, 17 (1—19), 16.

2. Dulęba I., & Opałka M. (2013). A comparison of Jacobian- based methods of inverse kinematics for serial robot manipulators, International Journal of Applied Mathematics and Computer Science, 23(2), 373—382.

3. Wang X., Zhang D., Zhao C. (2017). The inverse kinematics of a 7R 6-degree-of-freedom robot with non-spherical wrist, Advances in Mechanical Engineering, 9(8), 1687814017714985.

4. Ananthanarayanan H., & Ordóñez R. (2015). Real-time Inverse Kinematics of (2n + 1) DOF hyper-redundant manipulator arm via a combined numerical and analytical approach, Mechanism and Machine Theory, 91, 209—226.

5. Tolani D., Badler N. I. (1996). Real-time inverse kinematics of the human arm, Presence: Teleoperators & Virtual Environments, 5(4), 393—401.

6. Toshani H., & Farrokhi M. (2014). Real-time inverse kine matics of redundant manipulators using neural networks and quadratic programming: a Lyapunov-based approach, Robotics and Autonomous Systems, 62(6), 766—781.

7. Reiter A., Müller A., Gattringer H. (2016, October). In verse kinematics in minimum-time trajectory planning for kinematically redundant manipulators, In Industrial Electronics Society, IECON 2016 — 42nd Annual Conference of the IEEE (pp. 6873—6878). IEEE.

8. Geitle M. (2017). Improving differential evolution using inductive programming (Master’s thesis).

9. Bodily D. M., Allen T. F., Killpack M. D. (2017, May). Motion planning for mobile robots using inverse kinematics branching, In Robotics and Automation (ICRA), 2017 IEEE International Conference on (pp. 5043—5050). IEEE.

10. Ayyıldız M., Çetinkaya K. (2016). Comparison of four different heuristic optimization algorithms for the inverse ki nema tics solution of a real 4-DOF serial robot manipulator, Neural Computing and Applications, 27(4), 825—836.

11. Rokbani N., Alimi A. M. (2013). Inverse kinematics using particle swarm optimization, a statistical analysis, Procedia Engineering, 64, 1602—1611.

12. Collinsm T. J., Shen W. M. (2017, April). Particle swarm optimization for high-DOF inverse kinematics, In Control, Automation and Robotics (ICCAR), 2017 3rd International Conference on (pp. 1—6). IEEE.

13. Mao B., Xie Z., Wang Y., Handroos H., Wu H., Shi S. (2017). A hybrid differential evolution and particle swarm optimization algorithm for numerical kinematics solution of remote maintenance manipulators, Fusion Engineering and Design, 124, 587—590.

14. Kachitvichyanukul V. (2012). Comparison of three evolutionary algorithms: GA, PSO, and DE, Industrial Engineering and Management Systems, 11(3), 215—223.

15. López-Franco C., Hernández-Barragán J., Alanis A. Y., Arana-Daniel N., López-Franco M. (2018). Inverse kinematics of mobile manipulators based on differential evolution, International Journal of Advanced Robotic Systems, 15(1), 1729881417752738.

16. Shiakolas P. S., Koladiya D., Kebrle J. (2005). On the optimum synthesis of six-bar linkages using differential evolution and the geometric centroid of precision positions technique, Mechanism and Machine Theory, 40(3), 319—335.

17. Juang C. F., Chen Y. H., Jhan Y. H. (2015). Wall-following control of a hexapod robot using a data-driven fuzzy controller learned through differential evolution, IEEE Transactions on Industrial electronics, 62(1), 611—619.

18. Pierezan J., Freire R. Z., Weihmann L., Reynoso-Meza G., dos Santos Coelho L. (2017). Static force capability optimization of humanoids robots based on modified self-adaptive differential evolution, Computers & Operations Research, 84, 205—215.

19. Ngoc Son N., Anh H. P. H., Thanh Nam N. (2016). Robot manipulator identification based on adaptive multiple-input and multiple-output neural model optimized by advanced differential evolution algorithm, International Journal of Advanced Robotic Systems, 14(1), 1729881416677695.

20. Wang M., Luo J., Fang J., Yuan J. (2018). Optimal Trajectory Planning of Free-Floating Space Manipulator Using Differential Evolution Algorithm, Advances in Space Research.

21. Eusuff M., Lansey K., Pasha F. (2006). Shuffled frogleaping algorithm: a memetic meta-heuristic for discrete optimization, Engineering optimization, 38(2), 129—154.

22. Li X., Luo J., Chen M. R., Wang N. (2012). An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimization, Information Sciences, 192, 143—151.

23. Samuel G. G., Rajan C. C. A. (2014). A modified shuffled frog leaping algorithm for long-term generation maintenance scheduling, In Proceedings of the Third International Conference on Soft Computing for Problem Solving (pp. 11—24). Springer, New Delhi.

24. Afzalan E., Taghikhani M. A., Sedighizadeh M. (2012). Optimal placement and sizing of DG in radial distribution networks using SFLA, International Journal of Energy Engineering, 2(3), 73—77.

25. Ibrahim I. N. (2018). Ultra Light-Weight Robotic Manipulator. Bulletin of Kalashnikov ISTU, 2018, vol. 21, no. 1, pp. 12—18, DOI: 10.22213/2413-1172-2018-1-12-18 (in Russian).

26. Simon D. (2013). Evolutionary optimization algorithms. John Wiley & Sons.


Для цитирования:


Ibrahim I.N. A Comparative Study for an Inverse Kinematics Solution of an Aerial Manipulator Based on the Differential Evolution Method and the Modifi ed Shuffl ed Frog-Leaping Algorithm. Мехатроника, автоматизация, управление. 2018;19(11):714-724. https://doi.org/10.17587/mau.19.714-724

For citation:


Ibrahim I.N. A Comparative Study for an Inverse Kinematics Solution of an Aerial Manipulator Based on the Differential Evolution Method and the Modifi ed Shuffl ed Frog-Leaping Algorithm. Mekhatronika, Avtomatizatsiya, Upravlenie. 2018;19(11):714-724. https://doi.org/10.17587/mau.19.714-724

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