Planning unmanned aerial vehicle's path for maximum information collection using evolutionary algorithms

Abstract

AbstractPath planning is a problem of designing the path the vehicle is supposed to follow in such a way that a certain objective is optimized. In our study the objective is to maximize collected amount of information from Desired Regions (DR), meanwhile flying over the Forbidden Regions is avoided. In this paper, the path planning problem for single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators; Pull-to-Desired-Region (PTDR), Push-From-Forbidden-Region (PFFR), Pull-to-Finish-Point (PTFP). Besides these newly proposed operators, mutation and crossover operators have been used. The algorithm has been tested using two different scenarios and obtained results are presented in section 5. The 6 Degree-of-Freedom equation of motion has been used. The equations of motion of 12 state equations and the autopilot have been simulated in MATLAB/Simulink. Unlike previous studies in this field, we try to maximize collected information, instead of minimizing total mission time.