Abstract
Unmanned aerial vehicle (UAV) path planning is a complex optimization problem, which aims to achieve an optimal or nearly optimal flight path despite various threats and constraints. In this paper, an improved version of Gray Wolf Optimization (GWO) is proposed to solve the UAV 3D path planning problem which considers the dynamics of the UAV. In improved GWO, a variable weighting called "align coefficient" is defined to deal with the problem of waypoint scattering. The parallel GWO is applied to reduce the computation time which makes the possibility of real-time implementation. Given the existence and unique features of CAN bus in UAVs, it is used as a platform to migrate individuals in the parallelization process. The simulation results demonstrate that applying improved GWO generates better performance for UAV 3D path planning problems compared to the conventional GWO, GA, PSO, SA, improved GA and improved PSO.
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