Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computer
Scalable Parallel Programming with CUDA
Queue - GPU Computing
A Survey: Genetic Algorithms and the Fast Evolving World of Parallel Computing
HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
Automation, Production Systems, and Computer-Integrated Manufacturing
Automation, Production Systems, and Computer-Integrated Manufacturing
Some computer organizations and their effectiveness
IEEE Transactions on Computers
Parallel genetic algorithm on the CUDA architecture
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Parallel genetic algorithms on programmable graphics hardware
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Computing 2d robot workspace in parallel with CUDA
SIMPAR'12 Proceedings of the Third international conference on Simulation, Modeling, and Programming for Autonomous Robots
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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.