FLIGHT PATH PLANNING FOR FIXED-WING UNMANNED AERIAL VEHICLES, USING RANDOM TREE AND IMPROVED GENETIC ALGORITHM METHODS

Abstract

This article presents the results of the research on flight path planning method for fixed-wing UAVs operating at sea in complex environments. A method of mapping the operational space of UAVs and the constraints on a feasible flight path are studied. On the basis of the constraints, the article presents a flight path planning method using intelligent random trees and improved genetic algorithm. In particular, the random tree method combined with nonlinear constraints is applied to initiate feasible flight paths, and improved genetic algorithm operators are applied to find the optimal flight path. The simulation results show the effectiveness and feasibility of the proposed method, which is the basis for application to planning systems, creating tasks for UAVs to operate in complex environments.