Paths Planning of Unmanned Aerial Vehicles based on Grey Wolf Optimizer

Trajectories planning for Unmanned Aerial Vehicles (UAV) is a essential task in autonomous flight control design. In this paper, a method based on a Grey Wolf Optimizer (GWO) is present and favorably implemented to solve the path planning problem, reformulated as a hard optimization problem with constraints. The objective function is reformulated based on the path length and environmental constraints specific to the UAV while avoiding all the obstacles. The Water Cycle Algorithm (WCA), Crew Search Algorithm (CSA), Salp Swarm Algorithm (SSA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) metaheuristics are retained as comparison tools for statistical analysis. In order to observe the performance of the GWO compared to the other algorithm, many indicators are used as the performance criteria. The obtained results, conducted by numerical simulation, are satisfactory, perfectible and very encouraging in the aim of a future practical implementation on a hardware target of the proposed planning approach.