3D path planning for unmanned aerial vehicles

Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path, the objectives are to maximize the collected information from Desired Regions (DR) while avoiding flying over Forbidden Regions (FR) and reaching the destination. In this paper, the path planning problem for a multiple Unmanned Air Vehicles (UAVs) is studied with the proposal of novel evolutionary operators. The initial populations seed-path for each UAV have been obtained both by utilizing the Pattern Search method and solving the multiple Traveling Salesman Problem (mTSP). Utilizing the mTSP solves the assignment problem of which DR should be visited by which UAV. It should be emphasized that all of the paths in population in any generation of the evolutionary algorithm (EA) have been constructed using the dynamical mathematical model of an UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm has been tested with different scenarios, and the results are presented.