This research work addresses the problem of path planning for unmanned aerial vehicles (UAVs) in complex environments such as a maze. A genetic algorithm (GA) with variable chromosomes and gene change conditions is designed when the decision point criteria and a collision with a wall are presented; without considering large chromosomes. Our proposed GA obtains the sequence of minimum movements required to solve the maze. Then, the trajectories are generated by high-order polynomials. Finally, numerical simulations and real-time tests are carried out to validate the proposed algorithm.


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    Title :

    Genetic algorithm for path planning of UAVs as a maze-solving problem




    Publication date :

    2022-06-21


    Size :

    2730148 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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