Abstract
Inverse kinematics is one of the most basic problems that needs to be solved when using robot manipulators in a work environment. A closed-form solution is heavily dependent on the geometry of the manipulator. A solution may not be possible for certain robots. On the other hand, there may be an infinite number of solutions, as is the case of highly redundant manipulators. We propose a Genetic Algorithm (GA) to approximate a solution to the inverse kinematics problem for both the position and orientation. This algorithm can be applied to different kinds of manipulators. Since typical GAs may take a considerable time to find a solution, a parallel implementation of the same algorithm (PGA) was developed for its execution on a CUDA-based architecture. A computational model of a PUMA 500 robot was used as a test subject for the GA. Results show that the parallel implementation of the algorithm was able to reduce the execution time of the serial GA significantly while also obtaining the solution within the specified margin of error.
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References
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)
Barrientos, A., Peñín, L.F., Balaguer, C., Aracil, R.: Fundamentos de Robótica, 2nd edn. McGraw-Hill, Spain (2007)
Cantú-Paz, E.: A summary of research on parallel genetic algorithms (1995)
Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10(2), 141–171 (1998)
Craig, J.J.: Robótica, 3rd edn. Prentice Hall, Mexico (2006)
Flynn, M.J.: Some computer organizations and their effectiveness. IEEE Transactions on Computers C-21(9), 948–960 (1972)
Groover, M.P.: Automation, Production Systems, and Computer-Integrated Manufacturing, 3rd edn. Prentice Hall, New Jersey (2008)
Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)
Koza, J.R.: Genetic programming (1997)
Macedonia, M.: The gpu enters computing’s mainstream. Computer 36(10), 106–108 (2003)
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)
Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with cuda. Queue 6, 40–53 (2008)
Nvidia: Nvidia cuda c programming guide (September 2010), http://developer.download.nvidia.com/compute/cuda/3_0/toolkit/docs/NVIDIA_CUDA_ProgrammingGuide.pdf
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)
Srinivas, M., Patnaik, L.: Genetic algorithms: a survey. Computer 27(6), 17–26 (1994)
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)
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)
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)
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Aguilar, O.A., Huegel, J.C. (2011). Inverse Kinematics Solution for Robotic Manipulators Using a CUDA-Based Parallel Genetic Algorithm. In: Batyrshin, I., Sidorov, G. (eds) Advances in Artificial Intelligence. MICAI 2011. Lecture Notes in Computer Science(), vol 7094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25324-9_42
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DOI: https://doi.org/10.1007/978-3-642-25324-9_42
Publisher Name: Springer, Berlin, Heidelberg
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