Inverse Kinematics Solution for Robotic Manipulators Using a CUDA-Based Parallel Genetic Algorithm

  • Omar Alejandro Aguilar
  • Joel Carlos Huegel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)

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.

Keywords

Genetic Algorithm Genetic Code Parallel Implementation Robotic Manipulator Thread Block 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Omar Alejandro Aguilar
    • 1
  • Joel Carlos Huegel
    • 1
  1. 1.Biomechatronics LabTecnologico de Monterrey - Campus GuadalajaraZapopan, Jal.México

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