Simulación de Modelos de Sistemas Dinámicos para Sostenibilidad

El principal objetivo de este PFC es la realización de una serie de simulaciones de sistemas dinámicos de temática vinculada al medio ambiente y la sostenibilidad, basándose en los modelos matemáticos que los describen. Estos modelos matemáticos se implementan mediante Easy Java Simulations, una herramienta enmarcada dentro del proyecto Open Source Physics, así como otras bibliotecas de Java. Las simulaciones serán representadas visualmente como Applets. Estos pueden ser tanto ejecutados individualmente como programas o mostrarse dentro de páginas HTML vía navegador –siempre que se cuente con soporte para la máquina virtual de Java-. La finalidad de realizar estas simulaciones es de carácter principalmente educativo y divulgativo, dando a conocer sistemas y modelos que describen aspectos ecológicos o ambientales de interés, así como demostrar el potencial del programario utilizado como herramienta de modelado matemático y visual

Perception for detection and grasping

The final publication is available at link.springer.comThis research presents a methodology for the detection of the crawler used in the project AEROARMS. The approach consisted on using a two-step progressive strategy, going from rough detection and tracking, for approximation maneuvers, to an accurate positioning step based on fiducial markers. Two different methods are explained for the first step, one using efficient image segmentation approach; and the second one using Deep Learning techniques to detect the center of the crawler. The fiducial markers are used for precise localization of the crawler in a similar way as explained in earlier chapters. The methods can run in real-time.Peer ReviewedPostprint (author's final draft

Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments

This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.Peer ReviewedPostprint (published version

Visual-based SLAM configurations for cooperative multi-UAV systems with a lead agent: an observability-based approach

In this work, the problem of the cooperative visual-based SLAM for the class of multi-UA systems that integrates a lead agent has been addressed. In these kinds of systems, a team of aerial robots flying in formation must follow a dynamic lead agent, which can be another aerial robot, vehicle or even a human. A fundamental problem that must be addressed for these kinds of systems has to do with the estimation of the states of the aerial robots as well as the state of the lead agent. In this work, the use of a cooperative visual-based SLAM approach is studied in order to solve the above problem. In this case, three different system configurations are proposed and investigated by means of an intensive nonlinear observability analysis. In addition, a high-level control scheme is proposed that allows to control the formation of the UAVs with respect to the lead agent. In this work, several theoretical results are obtained, together with an extensive set of computer simulations which are presented in order to numerically validate the proposal and to show that it can perform well under different circumstances (e.g., GPS-challenging environments). That is, the proposed method is able to operate robustly under many conditions providing a good position estimation of the aerial vehicles and the lead agent as well.Peer ReviewedPostprint (published version

A hybrid visual-based SLAM architecture: local filter-based SLAM with keyframe-based global mapping

This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approachPostprint (published version

Semi-supervised wildfire smoke detection based on smoke-aware consistency

The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smokeaware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.Peer ReviewedPostprint (published version

All-in-one aerial image enhancement network for forest scenes

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.Award-winningPostprint (published version

UAV visual and laser sensors fusion for detection and positioning in industrial applications

Postprint (published version

Aerial robotics in building inspection and maintenance

Buildings need periodic revision about their state, materials degrade with time and repairs or renewals have to be made driven by maintenance needs or safety requirements. That happens with any kind of buildings and constructions: housing, architecture masterpieces, old and ancient buildings and industrial buildings. Currently, nearly all of these tasks are carried out by human intervention. In order to carry out the inspection or maintenance, humans need to access to roofs, façades or other areas hard to reach and otherwise potentially hazardous location to perform the task. In some cases, it might not be feasible to access for inspection. For instance, in industry buildings operation must be often interrupted to allow for safe execution of such tasks; these shutdowns not only lead to substantial production loss, but the shutdown and start-up operation itself causes risks to human and environment. In touristic buildings, access has to be restricted with the consequent losses and inconveniences to visitors. The use of aerial robots can help to perform this kind of hazardous operations in an autonomous way, not only teleoperated. Robots are able to carry sensors to detect failures of many types and to locate them in a previously generated map, which the robot uses to navigate. Some of those sensors are cameras in different spectra (visual, near-infrared, UV), laser, LIDAR, ultrasounds and inertial sensory system. If the sensory part is crucial to inspect hazardous areas in buildings, the actuation is also important: the aerial robot can carry small robots (mainly crawler) to be deployed to perform more in-depth operation where the contact between the sensors and the material is basic (any kind of metallic part: pipes, roofs, panels…). The aerial robot has the ability to recover the deployed small crawler to be reused again. In this paper, authors will explain the research that they are conducting in this area and propose future research areas and applications with aerial, ground, submarine and other autonomous robots within the construction field.Peer ReviewedPostprint (author's final draft

A solution for robotized sampling in wastewater plants

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work presents a solution to automatize the water sampling process of outdoor basins in a wastewater treatment plant. The system proposed is based on the utilization of collaborative robotics: a team of an UAV and a terrestrial robotic platform make a route along the plant collecting and storing the water samples. The architecture of the designed system is described in terms of functional blocks, and implementation details including software frameworks and hardware on the UAV are provided. As the objective of the system is industry levels of robustness and performance, the UAV use is minimized and subjected to control from the robotic ground platform, reducing risks associated with autonomous UAV. To conclude, results from experiments performed to validate the viability of the system and study several design decisions are presented and briefly discussed, including: estimation of the accuracy of several GNSS technologies on the plant, viability of the landing operation over a mobile robotic platform and controlling a quadrotor over waters.Peer ReviewedPostprint (author's final draft