Inverse kinematics solution for robotic manipulators using a CUDA-based parallel genetic algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7094 LNAI, Issue PART 1, 2011, Pages 490-503

  Aguilar, Omar Alejandro ^a   Huegel, Joel Carlos ^a  

^a SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

CLOSED FORM SOLUTIONS; COMPUTATIONAL MODEL; EXECUTION TIME; HIGHLY REDUNDANT MANIPULATORS; INFINITE NUMBERS; INVERSE KINEMATICS PROBLEM; INVERSE KINEMATICS SOLUTIONS; MARGIN OF ERROR; PARALLEL GENETIC ALGORITHMS; PARALLEL IMPLEMENTATIONS; ROBOT MANIPULATOR; ROBOTIC MANIPULATORS; WORK ENVIRONMENTS;

ARTIFICIAL INTELLIGENCE; FLEXIBLE MANIPULATORS; HUMAN ENGINEERING; INTELLIGENT ROBOTS; INVERSE KINEMATICS; INVERSE PROBLEMS; PROGRAMMED CONTROL SYSTEMS; ROBOT APPLICATIONS;

GENETIC ALGORITHMS;

EID: 82555191201     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-25324-9_42     Document Type: Conference Paper

References (18)

1. Arora, R., Tulshyan, R., Deb, K.: Parallelization of binary and real-coded genetic algorithms on gpu using cuda. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8 (July 2010)
2. Barrientos, A., Peñín, L.F., Balaguer, C., Aracil, R.: Fundamentos de Robótica, 2nd edn. McGraw-Hill, Spain (2007)
3. Cantú-Paz, E.: A summary of research on parallel genetic algorithms (1995)
4. Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10(2), 141-171 (1998)
5. Craig, J.J.: Robótica, 3rd edn. Prentice Hall, Mexico (2006)
6. Flynn, M.J.: Some computer organizations and their effectiveness. IEEE Transactions on Computers C-21(9), 948-960 (1972)
7. Groover, M.P.: Automation, Production Systems, and Computer-Integrated Manufacturing, 3rd edn. Prentice Hall, New Jersey (2008)
8. Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)
9. Koza, J.R.: Genetic programming (1997)
10. Macedonia, M.: The gpu enters computing's mainstream. Computer 36(10), 106-108 (2003)
11. Munawar, A.,Wahib, M., Munetomo, M., Akama, K.: A survey: Genetic algorithms and the fast evolving world of parallel computing. In: 10th IEEE International Conference on High Performance Computing and Communications 2008, HPCC 2008, pp. 897-902 (2008)
12. Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with cuda. Queue 6, 40-53 (2008)
13. Nvidia: Nvidia cuda c programming guide (September 2010), http://developer.download.nvidia.com/compute/cuda/3 0/toolkit/docs/NVIDIA CUDA ProgrammingGuide.pdf
14. Pospichal, P., Jaros, J., Schwarz, J.: Parallel Genetic Algorithm on the Cuda Architecture. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 442-451. Springer, Heidelberg (2010)
15. Srinivas, M., Patnaik, L.: Genetic algorithms: a survey. Computer 27(6), 17-26 (1994)
16. Wong, M.L., Wong, T.T.: Parallel hybrid genetic algorithms on consumer-level graphics hardware. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 2973-2980 (2006)
17. Wong, M.L., Wong, T.T., Fok, K.L.: Parallel evolutionary algorithms on graphics processing unit. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2286-2293 (September 2005)
18. Yu, Q., Chen, C., Pan, Z.: Parallel Genetic Algorithms on Programmable Graphics Hardware. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 1051-1059. Springer, Heidelberg (2005)