Nowadays, swarm robotics research is having a great increase due to the benefits derived from its use, such as robustness, parallelism, and flexibility. Unlike distributed robotic systems, swarm robotics emphasizes a large number of robots, and promotes scalability. Among the multiple applications of such systems we could find are exploring unstructured environments, resource monitoring, or distributed sensing. Two of these applications, monitoring, and perimeter/area detection of a given resource, have several ecological uses. One of them is the detection and monitoring of pollutants to delimit their perimeter and area accurately. Maritime activity has been increasing gradually in recent years. Many ships carry products such as oil that can adversely affect the environment. Such products can produce high levels of pollution in case of being spilled into sea. In this paper we will present a distributed system which monitors, covers, and surrounds a resource by using a swarm of homogeneous low cost drones. These drones only use their local sensory information and do not require any direct communication between them. Taking into account the properties of this kind of oil spills we will present a microscopic model for a swarm of drones, capable of monitoring these spills properly. Furthermore, we will analyse the proper macroscopic operation of the swarm. The analytical and experimental results presented here show the proper evolution of our system.

This article aims to model the thermal behaviour of a wall using deep learning techniques. The Fourier theoretical model which is traditionally used to model such enclosures is not capable of considering several factors that affect a prediction that is often incorrect. These results motivate us to try to obtain a better thermal model of the enclosure. For this reason, a connexionist model is provided capable of modelling the behaviour of the enclosure from actual observed temperature data. For the training of this model, several measurements have been obtained over the course of more than one year in a specific enclosure, distributing the readings among the different layers of it. In this work, the predictions of both the theoretical model and the connexionist model have been tested, contrasting them with the measurements obtained previously. It has been observed that the connexionist model substantially improves the theoretical predictions of the Fourier method, thus allowing better approximations to be made regarding the real energy consumption of the building and, in general, the prediction of the energy behaviour of the enclosure.

In this work, a swarm behaviour for multi-rotor Unmanned Aerial Vehicles (UAVs) deployment will be presented. The main contribution of this behaviour is the use of a virtual device for quantitative sematectonic stigmergy providing more adaptable behaviours in complex environments. It is a fault tolerant highly robust behaviour that does not require prior information of the area to be covered, or to assume the existence of any kind of information signals (GPS, mobile communication networks …), taking into account the specific features of UAVs. This behaviour will be oriented towards emergency tasks. Their main goal will be to cover an area of the environment for later creating an ad-hoc communication network, that can be used to establish communications inside this zone. Although there are several papers on robotic deployment it is more difficult to find applications with UAV systems, mainly because of the existence of various problems that must be overcome including limitations in available sensory and on-board processing capabilities and low flight endurance. In addition, those behaviours designed for UAVs often have significant limitations on their ability to be used in real tasks, because they assume specific features, not easily applicable in a general way. Firstly, in this article the characteristics of the simulation environment will be presented. Secondly, a microscopic model for deployment and creation of ad-hoc networks, that implicitly includes stigmergy features, will be shown. Then, the overall swarm behaviour will be modeled, providing a macroscopic model of this behaviour. This model can accurately predict the number of agents needed to cover an area as well as the time required for the deployment process. An experimental analysis through simulation will be carried out in order to verify our models. In this analysis the influence of both the complexity of the environment and the stigmergy system will be discussed, given the data obtained in the simulation. In addition, the macroscopic and microscopic models will be compared verifying the number of predicted individuals for each state regarding the simulation.

Building an ad-hoc network in emergency situations can be crucial as a primary tool or even when used prior to subsequent operations. The use of mini and micro Unmanned Aerial Vehicles (UAVs) is increasing because of the wide range of possibilities they offer. Moreover, they have been proven to bring sustainability to many applications, such as agriculture, deforestation and wildlife conservation, among others. Therefore, creating a UAV network for an unknown environment is an important task and an active research field. In this article, a mobility model for the creation of ad-hoc networks using UAVs will be presented. This model will be based on pheromones for robust navigation. We will focus mainly on developing energy-efficient behavior, which is essential for this type of vehicle. Although there are in the literature several models of mobility for ad-hoc network creation, we find that either they are not adapted to the specific energy requirements of UAVs or the proposed motion models are unrealistic or not sufficiently robust for final implantation. We will present and analyze the operation of a distributed swarm behavior able to create an ad-hoc network. Then, an analytical model of the swarm energy consumption will be proposed. This model will provide a mechanism to effectively predict the energy consumption needed for the deployment of the network prior to its implementation. Determining the use of the mobility behavior is a requirement to establish and maintain a communication channel for the required time. Finally, this analytical model will be experimentally validated and compared to the Random Waypoint (RWP) mobility strategy.