Wind-Effected Dynamic Quadrotor Route Planning With Metaheuristic Methods in Different Weather Conditions

Abstract

In cases where Quadrotors, which are increasingly important rotary-wing Unmanned Aerial Vehicles (UAVs), are required to visit more than one location, route planning should be done to reduce the cost of flight and increase the efficiency. In this study, it is aimed to reduce the flight time and increase the efficiency of Quadrotor Route Planning (QRP) based on the changes in wind speed and wind angle. To achieve this, a dynamic QRP application which can generate routes which are suitable for changing environmental conditions by using instantaneous wind data and real location coordinates has been developed. In this application, Genetic Algorithm (GA), Tabu Search and Traveling Salesman Problem (TSP) with GA metaheuristic methods were used comparatively to optimize QRP according to flight time. Among these methods, the TSP with GA method is the metaheuristic method that gave the most optimal results. When the results are examined, it is seen that wind effect dynamic QRP that uses TSP and GA method provides up to 26% improvements in flight time compared to Standard QRP that uses TSP with GA method.