The development in Multi-Robot Systems (MRS) has become one of the most exploited fields of research in robotics in recent years. This is due to the robustness and versatility they present to effectively undertake a set of tasks autonomously. One of the essential elements for several vehicles, in this case, Unmanned Aerial Vehicles (UAVs), to perform tasks autonomously and cooperatively is trajectory planning, which is necessary to guarantee the safe and collision-free movement of the different vehicles. This document includes the planning of multiple trajectories for a swarm of UAVs based on 3D Probabilistic Roadmaps (PRM). This swarm is capable of reaching different locations of interest in different cases (labeled and unlabeled), supporting of an Emergency Response Team (ERT) in emergencies in urban environments. In addition, an architecture based on Robot Operating System (ROS) is presented to allow the simulation and integration of the methods developed in a UAV swarm. This architecture allows the communications with the MavLink protocol and control via the Pixhawk autopilot, for a quick and easy implementation in real UAVs. The proposed method was validated by experiments simulating building emergences. Finally, the obtained results show that methods based on probability roadmaps create effective solutions in terms of calculation time in the case of scalable systems in different situations along with their integration into a versatile framework such as ROS.
The advances in autonomous technologies and microelectronics have increased the use of Autonomous Unmanned Aerial Vehicles (UAVs) in more critical applications, such as forest fire monitoring and fighting. In addition, implementing surveillance methods that provide rich information about the fires is considered a great tool for Emergency Response Teams (ERT). From this aspect and in collaboration with Telefónica Digital España, Dronitec S.L, and Divisek Systems, this paper presents a fire monitoring system based on perception algorithms, implemented on a UAV, to perform surveillance tasks allowing the monitoring of a specific area, in which several algorithms have been implemented to perform the tasks of autonomous take-off/landing, trajectory planning, and fire monitoring. This UAV is equipped with RGB and thermal cameras, temperature sensors, and communication modules in order to provide full information about the fire and the UAV itself, sending these data to the ground station in real time. The presented work is validated by performing several flights in a real environment, and the obtained results show the efficiency and the robustness of the proposed system, against different weather conditions.
Advances in the field of unmanned aerial vehicles (UAVs) have led to an exponential increase in their market, thanks to the development of innovative technological solutions aimed at a wide range of applications and services, such as emergencies and those related to fires. In addition, the expansion of this market has been accompanied by the birth and growth of the so-called UAV swarms. Currently, the expansion of these systems is due to their properties in terms of robustness, versatility, and efficiency. Along with these properties there is an aspect, which is still a field of study, such as autonomous and cooperative navigation of these swarms. In this paper we present an architecture that includes a set of complementary methods that allow the establishment of different control layers to enable the autonomous and cooperative navigation of a swarm of UAVs. Among the different layers, there are a global trajectory planner based on sampling, algorithms for obstacle detection and avoidance, and methods for autonomous decision making based on deep reinforcement learning. The paper shows satisfactory results for a line-of-sight based algorithm for global path planner trajectory smoothing in 2D and 3D. In addition, a novel method for autonomous navigation of UAVs based on deep reinforcement learning is shown, which has been tested in 2 different simulation environments with promising results about the use of these techniques to achieve autonomous navigation of UAVs.
En primer lugar, agradecer a mis directores académicos, Arturo y David y, a Pablo en Drone Hopper, por acompañarme a lo largo de esta etapa que hoy está más cerca de culminarse. Gracias por la ayuda prestada, por hacerme crecer, en el ámbito profesional y en el personal. Gracias por conar en mí y permitir que, junto a Drone Hopper, haya podido cerrar una etapa tan importante.Aunque si algo me ha llevado hoy a escribir estas líneas es formar parte, durante 5 años, de una familia como el LSI, incluida esa nueva generación de chavalitos, que han hecho del B16 un lugar donde nuca sentirse solo. El principal culpable de ello es Fernando. Fue él quien me brindó la oportunidad de trabajar con gente tan buena como la que aquí trabaja y ha trabajado, porque echando la vista atrás, no puedo olvidarme de personas cómo Noelia, Ricardo o Dani con las que ha sido un verdadero placer trabajar y, que junto al resto, han contribuido a que cada día uno se sienta afortunado de formar parte de un grupo humano como este.Dentro de esta gran familia hay otra persona que merece una mención aparte, no es otro que Abdulla, un amigo más que un compañero de trabajo. Sólo él sabe lo duro que, durante muchos momentos, ha sido esto y, sus palabras, su ayuda y su trabajo han hecho posible que las dudas se despejaran y continuara trabajando por llegar hoy a escribir estas líneas.Al escribir estas líneas no puedo olvidarme de un grupo de personas que durante esta etapa han sido más que compañeros de trabajo, han sido amigos, aquellas personas con las que compartir las alegrías y penas del día a día, gente como Carlos, María, Irene, Fran o Jorge que me han ayudado y apoyado de manera especial a lo largo de esta dura etapa, la cual Jorge advirtió y yo no hice caso.Tampoco puedo terminar este párrafo sin agradecer a Sergio y Alejandro todo el trabajo de estos últimos meses, por hacer más fácil y llevadera la vuelta a la normalidad y por estar codo con codo en las últimas fases de este trabajo.Por último, gracias a mi verdadera familia, a unos padres y hermanas que han sido un espejo dónde mirarse, a los que les debo y, sin los cuáles, no habría llegado hoy a escribir este trabajo. Y gracias a ti, Olga, por comprenderme, soportarme y ayudarme en estos duros meses y, sobre todo, por dar sentido a mi vida de la manera en la que lo has hecho.
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