Path planning strategy for unmanned aerial vehicles based on a grey wolf optimiser

The path planning problems for unmanned aerial vehicles (UAVs) can be considered as large scale global optimisation (LSGO) problems. Collision-free and smoother flyable paths require increased sequences of flight waypoints acting as the decision variables. In this paper, an intelligent path planning strategy based on the partition of the work area into multiple sub-environments and a parameters-free grey wolf optimiser (GWO) is proposed. A collision-free with shorter length sub-paths are optimized under constraints of obstacles avoidance and path's straightness limitation. A cubic spline technique is used to smooth the generated flight route and make the planned path more suitable. A comparative study is carried out to show the superiority of the proposed GWO-based planning technique compared to other homologous metaheuristics. The conducted results are satisfactory and encouraging in the aim of a practical implementation using the real-world prototype Parrot AR. Drone 2.0 and the associated MATLAB/Simulink software toolkit.