## 3 Conclusions and Future Work  
This work presents a software architecture in which a set of methods are combined to allow autonomous and coordinated navigation of a swarm of UAVs in environments of various kinds, such as fires declared in forest or urban environments. The main contribution of this work is to develop a software architecture consisting of multiple layers in which, through the implementation of a set of methods and algorithms we seek to generate solutions that allow the autonomous and coordinated navigation of a swarm of UAVs in order to be able to carry out a joint mission. The proposed solutions seek to establish a robust, scalable, and secure navigation system for UAVs groups that solves problems such as safe and effective path planning, obstacle detection and avoidance, coordinated navigation under a given training, or autonomous decision-making based on captured environment training. The global centralized architecture allows to have a greater number of computational resources in the central node located on the ground, in order to execute those methods that require a greater computational expenditure, leaving the embedded computer on board the UAV for the decentralized layer, which is formed by a set of algorithms whose implementation can be supported by embedded computers such as the Jetson AGX Xavier [44], which has been chosen to be embarked on real aerial platforms with which to begin to validate and test the proposed architecture. Throughout this work, the potential application of UAV swarms has been demonstrated to act autonomously and coordinately different environments, through the implementation of path planning and autonomous navigation algorithms. The algorithms and approaches proposed in this work have shown different solutions, with high levels of robustness and precision, in autonomous and coordinated navigation applications of UAVs. However, to increase the performance of these algorithms, different areas will be investigated in future work. Such future research focuses on the following areas:  
* In the development and construction of a real swarm of UAVs, with structure like that presented throughout this work, to be used as a demonstrator of the algorithms and methods implemented. * One of the crucial aspects that limits the use of UAVs is the ability to charge and the autonomy of systems. For this reason, it is essential to be able to count software tools that allow to obtain the most information of the environment from the use of low weight and consumption systems. Currently, work is under way on the development of DL-based methods that allow the generation of three-dimensional occupancy maps from the visual information obtained by a monocular camera. The output of this method may be used as input to the methods of re-planning paths for obstacle avoidance proposed in this work. * One of the problems detected in the literature in DRL methods is the lack of standardization of the models and the need to adapt the workouts to the environments in which you want to work. Although advances and research in the field of DL are offering solutions to this issue. For this reason, consideration is given to including more complex ANNs to include visual information from sensors on board the UAV in the observation state. In this way, increase and improve the learning of the agent to achieve a robust, heterogeneous and standard model capable of working autonomously on various types of environments. To achieve this objective, it will be advisable to carry out training of the agent in a wider range of environments, in which the complexity is increasing. In addition, increasing input information to the agent will not only bring changes in the structural complexity of the networks used, but will involve the use of more complex DRL algorithms. Conceptualization, A.M. and A.A.-K.; Investigation, A.M., A.A.-K. and D.M. ; Methodology, A.M.; Resources, P.F. ; Software, A.M.; Supervision, P.F., D.M. and A.d.I.E. ; Writing--original draft, A.M. and A.A.-K.; Writing--eview & editing, D.M. and A.d.I.E.