Abstract. The development of autonomous Unmanned Aerial Vehicles (UAVs) is a priority to many civilian and military organizations. An essential aspect of UAV autonomy is the ability for automatic trajectory planning. In this paper, we use a parallel Flower Pollination Algorithm (FPA) to deal with the problem's complexity and compute feasible and quasi-optimal trajectories for fixed-wing UAVs in complex 3D environments, taking into account the vehicle's flight properties. The global optimization algorithm is improved with the addition of 2-opt local search providing a significant improvement. The proposed trajectory planner in implemented and parallelized on a multicore processor (CPU) using OpenMP and a Graphics Processing Unit (GPU) using CUDA resulting in a 9.6x and a 68.5x speedup respectively compared to the sequential implementation on CPU. Index Terms—Flower Pollination Algorithm, Graphics Processing Unit, Parallel Programming, Trajectory Planning, Unmanned Aerial Vehicle.


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    Title :

    Parallel Hybrid 2-Opt Flower Pollination Algorithm for Real-Time UAV Trajectory Planning on GPU


    Contributors:


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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