2026-06-05T12:55:13Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/275762026-02-03T12:28:19Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Martín Izquierdo, Adrián author De la Bandera Cascales, Isabel author Barco-Moreno, Raquel author 2023 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. https://hdl.handle.net/10630/27576 Aviones sin piloto Optimización de las trayectorias Telecomunicaciones Ruta óptima para vehículos aéreos no tripulados para la recolección de datos en entornos IoT.