Ruta óptima para vehículos aéreos no tripulados para la recolección de datos en entornos IoT.

Abstract

In this article, a novel approach is presented for dynamic route planning of unmanned aerial vehicles (UAVs) for efficient data collection in industrial environments using UAVs connected to 5G and ultra-low power device networks. The proposed approach is based on bio-inspired algorithms, which draw inspiration from the behavior of animals and plants. The algorithm uses a combination of genetic algorithm (GA) and rapid exploration random tree (RRT) to optimize the UAV’s route and reduce energy consumption, while complying with the UAV’s flight limitations, considering the energy consumed by the UAV. This solution focuses on achieving higher energy efficiency and better data collection capability in industrial environments. The combination of GA and RRT is capable of finding optimal routes for data collection, reducing the UAV’s energy consumption while ensuring that all flight constraints are met.