Optimal UAV path planning in a 3D threat environment by using parallel evolutionary algorithms

In recent years, unmanned aerial vehicles-UAVs represent one of the most demanding technologies in aeronautics, and they have tremendous appeal because of their operability with considerable autonomy (by using minimal human intervention). UAVs have to operate in complex environments with different constraints such as obstacles, threatening zones, UAV kinematics, etc. In this technology, path planning plays a crucial role for high autonomy operations, although absolute autonomy is still an open question. In this paper, we tried to discuss, how a feasible path planning for a UAV can be done in the 3-dimensional environment by avoiding threats such as a radar network which contains several radars with different detection ranges. The proposed methodology is implemented with using genetic algorithms, and a parallel approach is used for reducing path planning calculations. The environment is represented as 3 dimensional structure by using World Wind, which is an open-source and accurate 3D environment browser. The developed methodology can provide fast and safe routes for autonomous single UAVs or operator-assisted flight.