12 research outputs found
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
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