Keywords
inverse kinematics
genetic algorithm
singularity
robotic manipulator
trajectory generator
genetic algorithm
singularity
robotic manipulator
trajectory generator
How to Cite
Gierlak, P. (2018). Singularity robust trajectory generator for robotic manipulator based on genetic algorithm with dynamic encoding of solutions. Advances in Mechanical and Materials Engineering, 37(298 (4), 465-480. https://doi.org/10.7862/rm.2018.40
Abstract
In this paper a singularity robust trajectory generator for robotic manipulators is presented. The generator contains the procedure of solving the inverse kinematics problem. This issue is defined as an optimization problem, where a genetic algorithm is used for optimizing the fitness function. In order to avoid singularity problem, the generator is based on the direct kinematics problem. The trajectory generator allows to obtain generalized coordinates, velocities and accelerations. Simulation results show that the procedure generates a trajectory of manipulator even in kinematics singularities.
References
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10. Abo-Hammour Z.S., Mirza N.M., Mirza S.M., Arif M.: Cartesian path generation of robot manipulators using continuous genetic algorithms, Rob. Auton. Syst, 41 (2002) 179-223.
11. Renner G., Ekárt A.: Genetic algorithms in computer aided design, Comput. Aided Des., 35 (2003) 709-726.
12. Chakraborty M., Chakraborty U.K.: An analysis of linear ranking and binary tournament selection in genetic algorithms, Proc. ICICS’9 1997.
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15. Zhong J., Hu X., Gu M., Zhang J.: Comparison of performance between different selection strategies on simple genetic algorithms, Proc. CIMCA-IAWTIC’05, 2005.
2. Pires E.J.S., Oliveira P.B.M., Machado J.A.T.: Manipulator trajectory planning using a MOEA, Appl. Soft. Comput., 7 (2007) 659-667.
3. Dulęba I., Hossa R., Mazur A., Muszyński R., Tchoń K.: Manipulatory i roboty mobilne: modele, planowanie ruchu, sterowanie, PLJ, Warszawa 2000.
4. Żylski W., Gierlak P.: Generator zadanej trajektorii ruchu obiektu dynamicznego, ZN PRz, seria: Mechanika, 74 (2008) 421-432.
5. Żylski W., Gierlak P.: Zadanie odwrotne kinematyki manipulatora w ujęciu Rozszerzonej Mapy Kohonena, Prace Naukowe Politechniki Warszawskiej, seria: Elektronika, 166 (2008) 495-504.
6. Hasan A.T., Hamouda A.M.S., Ismail N., Al-Assadi H.M.A.A.: An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 DOF serial robot manipulator, Adv. Eng. Softw., 37 (2006) 432-438.
7. Vargas L.V., Leite A.C., Costa R.R.: Overcoming kinematic singularities with the filtered inverse approach, IFAC Proc. Vols., 47 (2014) 8496-8502.
8. El-Sherbiny A., Elhosseini M.A., Haikal A.Y.: A comparative study of soft computing methods to solve inverse kinematics problem, Ain Shams Eng. J., 2017, http://dx.doi.org/10.1016/j.asej.2017.08.001
9. Köker R.: A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization, Inf. Sci., 222 (2013) 528-543.
10. Abo-Hammour Z.S., Mirza N.M., Mirza S.M., Arif M.: Cartesian path generation of robot manipulators using continuous genetic algorithms, Rob. Auton. Syst, 41 (2002) 179-223.
11. Renner G., Ekárt A.: Genetic algorithms in computer aided design, Comput. Aided Des., 35 (2003) 709-726.
12. Chakraborty M., Chakraborty U.K.: An analysis of linear ranking and binary tournament selection in genetic algorithms, Proc. ICICS’9 1997.
13. Rutkowska D., Piliński M., Rutkowski L.: Sieci neuronowe, algorytmy genetyczne i systemy rozmyte, PWN, Warszawa 1999.
14. Rutkowski L.: Metody i techniki sztucznej inteligencji, PWN, Warszawa 2005.
15. Zhong J., Hu X., Gu M., Zhang J.: Comparison of performance between different selection strategies on simple genetic algorithms, Proc. CIMCA-IAWTIC’05, 2005.