Development of a ROS environment for researching machine learning techniques applied to drones

The first part of this dissertation presents ROS-MAGNA, a general framework for the definition and management of cooperative missions for multiple Unmanned Aircraft Systems (UAS) based on the Robot Operating System (ROS) [42]. This framework makes transparent the type of autopilot on-board and creates the state machines that control the behaviour of the different UAS from the specification of the multi-UAS mission. In addition, it integrates a virtual world generation tool to manage the information of the environment and visualize the geometrical objects of interest to properly follow the progress of the mission. The framework supports the coexistence of software-in-the-loop, hardware-in-the-loop and real UAS cooperating in the same arena, being a very useful testing tool for the developer of UAS advanced functionalities. To the best of our knowledge, it is the first framework which endows all these capabilities. The document also includes simulations and real experiments which show the main features of the framework. ROS-MAGNA is used to develop and test a machine learning tool. The information generated during a mission is used to train neural networks of different architecture for navigation purposes. The data treatment and training processes are accomplished in a testbench to select the best solution from different datasets. Tensorflow is the framework selected to implement every deep learning algorithm along with its Tensorboard tool for training understanding.Furthermore, an API with the pre-trained is used during a real mission in real time. The third part of this dissertation is the design and integration of a voice control assistant inside ROSMAGNA. Employing diverse online and offline tools, oral commands are processed to perform changes to the mission state and performance and to retrieve information.Universidad de Sevilla. Máster en Ingeniería Industria

An aerial robot path follower based on the ’Carrot chasing’ algorithm

This paper presents a three-dimensional path follower implementation for an aerial robot based on the carrot-chasing algorithm. The main objective was to improve the performance of the position controller of the PX4 autopilot when following a list of waypoints. This autopilot is widely used in the aerial robotics community, but we needed to improve its performance for navigation in cluttered environments. Different simulations have been carried out under the ROS (Robotic Operating System) environment for the comparison between the position controller of the PX4 and the proposed path follower. In addition, we have implemented different modes to generate the path from the input list of waypoints that are also analyzed in our simulation environment

Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs

peer reviewedIn this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts moving in an environment, we assume it has no knowledge about it, and it estimates an online situational graph representation (S-Graph) of its surroundings. We develop a novel graph-to-graph matching method, in order to relate the S-Graph estimated online from the robot sensors and the A-Graph extracted from the building plans. Note the challenge in this, as the S-Graph may show a partial view of the full A-Graph, their nodes are heterogeneous and their reference frames are different. After the matching, both graphs are aligned and merged, resulting in what we denote as an informed Situational Graph (iS-Graph), with which we achieve global robot localization and exploitation of prior knowledge from the building plans. Our experiments show that our pipeline shows a higher robustness and a significantly lower pose error than several LiDAR localization baselines.Robotic Situational Awareness By Understanding And Reasonin

Graph-based Global Robot Simultaneous Localization and Mapping using Architectural Plans

peer reviewedIn this paper, we propose a solution for graph-based global robot simultaneous localization and mapping (SLAM) using architectural plans. Before the start of the robot operation, the previously available architectural plan of the building is converted into our proposed architectural graph (A-Graph). When the robot starts its operation, it uses its onboard LIDAR and odometry to carry out an online SLAM relying on our situational graph (S-Graph), which includes both, a representation of the environment with multiple levels of abstractions, such as walls or rooms, and their relationships, as well as the robot poses with their associated keyframes. Our novel graph-to-graph matching method is used to relate the aforementioned S-Graph and A-Graph, which are aligned and merged, resulting in our novel informed Situational Graph (iS-Graph). Our iS-Graph not only provides graph-based global robot localization, but it extends the graph-based SLAM capabilities of the S-Graph by incorporating into it the prior knowledge of the environment existing in the architectural planRobotic Situational Awareness By Understanding And Reasonin

Layer-by-Layer Integration of Zirconium Metal-Organic Frameworks onto Activated Carbon Spheres and Fabrics with Model Nerve Agent Detoxification Properties

This research was funded by the Directorate for Planning, Technology, and Innovation (SDG PLATIN) from the Directorate General of Armaments and Material (DGAM) of the Spanish Ministry of Defense, COINCIDENTE Program exp. 1003219007500—NBQD2. The authors also acknowledge EU Feder funding, MINECO (CTQ2017-84692-R and PID2020-113608RB-I00), Universidad de Granada (Plan Propio de Investigación), and Junta de Andalucia (P18-RT-612). Funding for open access charge: Universidad de Granada/CBUA.We report the controlled synthesis of thin films of prototypical zirconium metal-organic frameworks [Zr6O4(OH)4(benzene-1,4-dicarboxylate-2-X)6] (X = H, UiO-66 and X = NH2, UiO-66-NH2) over the external surface of shaped carbonized substrates (spheres and textile fabrics) using a layer-by-layer method. The resulting composite materials contain metal-organic framework (MOF) crystals homogeneously distributed over the external surface of the porous shaped bodies, which are able to capture an organophosphate nerve agent simulant (diisopropylfluorophosphate, DIFP) in competition with moisture (very fast) and hydrolyze the P-F bond (slow). This behavior confers the composite material self-cleaning properties, which are useful for blocking secondary emission problems of classical protective equipment based on activated carbon.CBUADirectorate General of Armaments and MaterialDirectorate for Planning, Technology, and InnovationSpanish Ministry of Defense 1003219007500Universidad de Granad

Better Situational Graphs by Inferring High-level Semantic-Relational Concepts

Recent works on SLAM extend their pose graphs with higher-level semantic concepts exploiting relationships between them, to provide, not only a richer representation of the situation/environment but also to improve the accuracy of its estimation. Concretely, our previous work, Situational Graphs (S-Graphs), a pioneer in jointly leveraging semantic relationships in the factor optimization process, relies on semantic entities such as wall surfaces and rooms, whose relationship is mathematically defined. Nevertheless, excerpting these high-level concepts relying exclusively on the lower-level factor-graph remains a challenge and it is currently done with ad-hoc algorithms, which limits its capability to include new semantic-relational concepts. To overcome this limitation, in this work, we propose a Graph Neural Network (GNN) for learning high-level semantic-relational concepts that can be inferred from the low-level factor graph. We have demonstrated that we can infer room entities and their relationship to the mapped wall surfaces, more accurately and more computationally efficient than the baseline algorithm. Additionally, to demonstrate the versatility of our method, we provide a new semantic concept, i.e. wall, and its relationship with its wall surfaces. Our proposed method has been integrated into S-Graphs+, and it has been validated in both simulated and real datasets. A docker container with our software will be made available to the scientific community

The case of a southern European glacier disappearing under recent warming that survived Roman and Medieval warm periods

Mountain glaciers have generally experienced an accelerated retreat over the last three decades as a rapid response to current global warming. However, the response to previous warm periods in the Holocene is not well-described for glaciers of the of southern Europe mountain ranges, such as the Pyrenees. The situation during the Medieval Climate Anomaly (900-1300 CE) is particularly relevant since it is not certain whether the glaciers just experienced significant ice loss or whether they actually disappeared. We present here the first chronological study of a glacier located in the Central Pyrenees (N Spain), the Monte Perdido Glacier (MPG), carried out by different radiochronological techniques and their comparison with geochemical proxies with neighboring paleoclimate records. The result of the chronological model proves that the glacier endured during the Roman Period and the Medieval Climate Anomaly. The lack of ice from last 600 years indicates that the ice formed during the Little Ice Age has melted away. The analyses of the content of several metals of anthropogenic origin, such as Zn, Se, Cd, Hg, Pb, appear in low amounts in MPG ice, which further supports our age model in which the record from the industrial period is lost. This study confirms the exceptional warming of the last decades in the context of last two millennia. We demonstrate that we are facing an unprecedented retreat of the 55 Pyrenean glaciers which survival is compromised beyond a few decades

Çédille, revista de estudios franceses

Presentació

Reconocimiento gestual para interacción humano-robot basado en ROS

El objeto de esta memoria es la descripción de la solución al problema de identificación y posicionamiento del gesto de señalización de un ser humano en una estación de trabajo mediante una cámara Kinect, todo ello simulado en un entorno virtual en ROS. El sentido de este trabajo es su localización dentro del marco del concurso de robótica a nivel europeo EuRoC, en el que participé junto con mis compañeros de equipo del Centro Avanzado de Tecnologías Aeroespaciales (CATEC) y dentro del cual yo me encargué de la primera tarea (dividida en tres subtareas) que me dispongo a presentar. En primer lugar se describe el contexto en el que se encuentra el trabajo para poder entender bien el porqué de sus objetivos, de sus formas de implementación y de su entorno de trabajo. El segundo gran bloque habla del estado del arte para poder entender cual es el estado actual tanto de la robótica como del tratamiento de nubes de puntos y reconocimiento gestual. Su fin es el de describir con qué tecnología contamos, cuál es el partido que le podemos sacar a ésta y en qué caminos debemos aún seguir avanzando para logar optimizar operaciones de la robótica como la que aquí se presenta u otras muchas. Seguidamente se explican unos conceptos básicos de los desarrollos matemáticos que emplean las herramientas de trabajo con puntos en el espacio aquí usadas. Creo que, aunque éstas nos faciliten tanto el trabajo con algoritmos ya desarrollados y de fácil implementación, siempre es necesario comprender qué es lo que se está empleando para un uso óptimo de las mismas. A continuación, se expone qué es ROS y cuáles son las herramientas que nos ofrece para alcanzar nuestro objetivo para poder entender cuál es la metodología de trabajo que sigo, cuáles son los canales de comunicación de ROS que empleo y qué partes del problema solucionan estas herramientas y cuáles tengo yo que solucionar con ellas. Justo antes de entrar en faena, es necesario analizar el entorno de simulación. Esto es útil para ver bien cuáles son los objetivos de cada tarea y los elementos físicos y sus movimientos con los que tendremos que lidiar durante el desarrollo de las mismas. Una vez comprendido esto y sus objetivos, vemos la forma que tendrá el concurso de corregirnos y cuáles serán los criterios de evaluación. Ahora sí, vemos cuál es la solución que aporto a cada una de las necesidades que van surgiendo durante el desarrollo de cada una de las tareas. Para ello, son desplegados seis diagramas de flujos en los que intento expresar de la mejor forma posible cómo se va tratando la información obtenida mendiante la cámara Kinect, siempre enlazando con el código real que implementa la solución de los diagramas, y cómo van variando algunas de las variables de control más importantes. Para finalizar, son adjuntados dos anexos. En el primero de ellos explicaré cómo se inicia la máquina virtual para que se comprenda dónde está situada y cómo se va a ejecutar la solución a las diferentes tareas aportada por nuestro equipo. En el segundo código incluyo un extracto o selección de la parte del código que considero más importante para la clara comprensión de la implementación de la solución, aunque dejando comentarios de todo el resto de código.Grado en Ingeniería de Tecnologías Industriale

Late breaking results on Graph Reasoning on Situational Graphs for higher-concepts generation

peer reviewedGraphs [1] presented a novel factor graph compounded by robot keyframes associated with a scene graph that includes higher-level semantic-relational concepts. However, it only includes simple and rigid room structures and lacks other useful entities such as walls (as a wall surface to wall surface relationship). To overcome this, we propose to generate new factorized subgraphs using the relational and generalisation power of Graph Neural Networks (GNN