Optimal Route Planning of Unmanned Aerial Vehicles for Efficient Coverage of a Given Area

This article addresses the problem of optimizing routes for unmanned aerial vehicles (UAVs) for the inspection of solar panels, which is a specific case of the Traveling Salesman Problem on a complete graph. The aim of the work is to develop a mathematical model for planning UAV routes with the minimization of route length and energy consumption, while ensuring complete coverage of all inspection points. A program has been developed that implements a UAV routing model, taking into account energy consumption constraints. Four algorithms were applied to solve the problem: greedy algorithm, 2-opt, Ant Colony Optimization, and Genetic Algorithm. Each method was evaluated based on three criteria: execution speed, route length, and energy efficiency. The experimental results showed that the greedy algorithm is the fastest, but it yields less optimal routes compared to other methods. The 2-opt algorithm did not provide satisfactory results due to a significant increase in energy consumption and route length. The Ant Colony Optimization and Genetic Algorithms showed the best results, providing optimal routes in terms of energy efficiency and minimization of path length. As a result of the analysis, it can be argued that the choice of algorithm depends on the specific requirements of the problem. For quickly obtaining an initial solution, it is advisable to use the greedy algorithm, for local optimization – the 2-opt algorithm, and for achieving the best results in minimizing energy consumption and route length – the Ant Colony Optimization or Genetic Algorithms.