An Improved Robot Path Planning Algorithm Based on Genetic Algorithm

Xuesong Yan, Qinghua Wu, Hammin Liu


Robot path planning is a NP problem; traditional optimization methods are not very effective to solve it. Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improve the convergence speed to form a new genetic algorithm. Through the series of numerical experiments, the new algorithm has been proved to be efficiency. We also use the proposed algorithm to solve the robot path planning problem and the experiment results indicated that the new algorithm is efficiency for solving the robot path planning problems and the best path usually can be found.




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