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.

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.

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.

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Background. Tool path generation problem is one of the most complexes in computer aided manufacturing. Although some efficient algorithms have been developed to solve it, their technological dependency makes them efficient in only a limited number of cases. Method of Approach. Our aim is to propose a model that will set apart the geometrical issues involved in the manufacturing process from the purely technology-dependent physical issues by means of a topological system. This system applies methods and concepts used in mathematical morphology paradigms. Thus, we will obtain a geometrical abstraction which will not only provide solutions to typically complex problems but also the possibility of applying these solutions to any machining environment regardless of the technology. Presented in the paper is a method for offsetting any kind of curve. Specifically, we use parametric cubic curves, which is one of the most general and popular models in computer aided design (CAD)/computer aided manufacturing (CAM) applications. Results. The resulting method avoids any constraint in object or tool shape and obtains valid and optimal trajectories, with a low temporal cost of O(n∙m), which is corroborated by the experiments. It also avoids some precision errors that are present in the most popular commercial CAD/CAM libraries. Conclusions. The use of morphology as the base of the formulation avoids self-intersections and discontinuities and allows the system to machine free-form shapes using any tool without constraints. Most numerical and geometrical problems are also avoided. Obtaining a practical algorithm from the theoretical formulation is straightforward. The resulting procedure is simple and efficient.