Autonomous Robot Path Planning Using Comparison Particle Swarm Optimization Method and Genetic algorithm

Miftah Rahmalia Ariyati, Ahmad Reza Musthafa


Research on Robot path planning has been widely researched and developed. In general, path planning that desired is a safe path, without obstacles, and short distances. There are several methods that can be applied in planning this path include the method of particle swarm optimization and genetics algorithm. In this research, both methods of optimization are applied. Both methods of optimization are compared to obtain the best possible method. The particle swarm optimization method utilizes the particle population movement and the genetic algorithm method of searching a population of a number of individuals who are problem solvers. The result of this research is the method of particle swarm optimization better than the genetic algorithm method. This is based on the computational time required by the particle swarm optimization method is shorter than the genetic algorithm method and the comparison of the particle swarm optimization pathway is also shorter than the genetic algorithm method.

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