Gestión y monitorización de instalaciones eléctricas trifásicas mediante Arduino y Raspberry Pi

El presente trabajo tiene como objetivo la monitorización de sistemas trifásicos en tiempo real, permitiendo el análisis pormenorizado de las componentes de voltaje e intensidad de cada una de sus fases. En la realización del mismo se hace especial hincapié en el carácter de bajo coste de todos los componentes empleados. Al hacer uso de dispositivos ampliamente utilizados en el entorno “maker”, a saber: un microcontrolador Arduino y una mini CPU Raspberry Pi, se busca también disminuir las complejidades inherentes que tienen muchos sistemas en el momento de configurar el entorno necesario, tanto para desarrollar en los mismos como para desplegar las aplicaciones desarrolladas. Se usará Software y Hardware Open Source, con las ventajas que ello conlleva.Universidad de Sevilla. Grado Ingeniería Electrónica, Robótica y Mecatrónic

Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations

The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected that the Sample Fetch Rover will be in charge of localizing and gathering up to 35 sample tubes over 150 Martian sols. Autonomous capabilities are critical for the success of the overall campaign and for the Sample Fetch Rover in particular. This work proposes a novel system architecture for the autonomous detection and pose estimation of the sample tubes. For the detection stage, a Deep Neural Network and transfer learning from a synthetic dataset are proposed. The dataset is created from photorealistic 3D simulations of Martian scenarios. Additionally, the sample tubes poses are estimated using Computer Vision techniques such as contour detection and line fitting on the detected area. Finally, laboratory tests of the Sample Localization procedure are performed using the ExoMars Testing Rover on a Mars-like testbed. These tests validate the proposed approach in different hardware architectures, providing promising results related to the sample detection and pose estimation.Comment: Preprint version only. Final version at IEEE Xplore. Accepted for IEEE Robotics and Automation Letter

Thermal Vision for Soil Assessment in a Multipurpose Environmental Chamber under Martian Conditions towards Robot Navigation

Soil assessment is important for mobile robot planning and navigation on natural and planetary environments. Terramechanic characteristics can be inferred from the thermal behaviour of soils under the influence of sunlight using remote sensors such as Long-Wave Infrared cameras. However, this behaviour is greatly affected by the low atmospheric pressures of planets such as Mars, so practical models are needed to relate robot remote sensing data on Earth to target planetary exploration conditions. This article proposes a general framework based on multipurpose environmental chambers to generate representative diurnal cycle dataset pairs that can be useful to relate the thermal behaviour of a soil on Earth to the corresponding behaviour under planetary pressure conditions using remote sensing. Furthermore, we present an application of the proposed framework to generate datasets using the UMA-Laserlab chamber, which can replicate the atmospheric \ch{CO2} composition of Mars. In particular, we analyze the thermal behaviour of four soil samples of different granularity by comparing replicated Martian surface conditions and their Earth's diurnal cycle equivalent. Results indicate a correlation between granularity and thermal inertia that is consistent with available Mars surface measurements recorded by rovers. The resulting dataset pairs, consisting of representative diurnal cycle thermal images with heater, air, and subsurface temperatures, have been made available for the scientific community.Comment: 10 pages, 13 figure

Thermal imagery for rover soil assessment using a multipurpose environmental chamber under simulated Mars conditions

Planetary rover missions on Mars have suffered entrapments and serious mobility incidents due to soil assessment limitations of stereo RGB cameras, which cannot characterize relevant physical phenomena such as thermal behavior that depend on granularity and cohesion. In particular, thermal inertia estimations are already being used to assess geophysical properties from 1-D low-resolution measurements by onboard thermopiles. However, no high-resolution measurements are currently available to characterize Martian soils for safer navigation in future missions, so new experimental methods are required to capture and analyze thermal images with planetary conditions in Earth-based experiments. In this work, we propose a novel measurement system configuration and experimental methodology to capture thermal images using isolated multipurpose environmental chambers (MECs) to replicate the temperature and pressure conditions of Mars. Furthermore, the system has allowed to measure diurnal cycles for four soil types of known physical characteristics under Martian and Earth pressures to perform a unique quantitative analysis and comparison of thermal behavior and thermal inertia for soil assessment. Even if no actual Martian infrared (IR) images are available for comparison, results indicate a correlation between granularity and thermal inertia that is consistent with available thermopile measurements recorded by rover’s onsite. Furthermore, the set of measurements acquired in the experiments has been made available to the scientific community.Funding for open access charge: Universidad de Málaga/CBUA. This work was supported in part by the Andalusian Regional Government through the Project Intelligent Multimodal Sensor for Identification of Terramechanic Characteristics in Off-Road Vehicles (IMSITER) under Grant P18-RT-991

Samples detection and retrieval for a sample fetch rover

Future planetary exploration missions are demanding more and more autonomy since these missions are getting more complex. A clear example is the Mars Sample Return mission, where the Sample Fetch Rover needs to collect sample tubes on a remote location, and bring them back to the base station to be launched to Earth. This mission requires to extend the autonomous capabilities onboard. First, the Navigation component needs to be able to detect and locate the sample tubes, and second, the Guidance and Control ones require to place the rover close the sample tubes and move the manipulator to pick them up. These are the main contributions of this paper. The first issue has been solved by the use of Deep Neural Networks, which allow to identify the previously trained sample tubes on images, and the second one has been solved by extending the path planning algorithm within the Guidance component. To demonstrate and validate the proposed methods, two experiments were carried out. A first field test in the Search and Rescue experimental terrain at the University of Malaga, and a second lab test in the Planetary Robotics Lab at the European Space Agency. Both experiments were carried out using the ExoMars Testing Rover owned by the last institution.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

AI-based Multimodal Perception for Planetary Exploration Robotics

Los rovers de exploración planetaria se enfrentan a importantes desafíos al desplazarse por los terrenos desestructurados de Marte, lo que compromete el éxito de las misiones. Los retrasos en las comunicaciones Tierra-Marte hacen que sea imprescindible que los rovers tengan un alto grado de autonomía. Sin embargo, su limitada capacidad de procesamiento dificulta la integración de sistemas avanzados de Inteligencia Artificial (IA). La presente tesis aborda estos desafíos mediante el desarrollo de métodos innovadores que mejoran la percepción en tiempo real, utilizando técnicas multimodales basadas en IA adaptadas a entornos con recursos computacionales limitados. Esta tesis propone el uso de imágenes térmicas para mejorar la evaluación autónoma de la transitabilidad del terreno, ya que pueden revelar características del suelo que otros sensores no detectan. Debido a la baja resolución de los datos obtenidos por los rovers actuales, se desarrolla una metodología que simula la evaluación térmica de distintos terrenos bajo condiciones similares a las de Marte, utilizando una Cámara Ambiental Multifuncional. Este enfoque mejora la caracterización del terreno y permite estimar propiedades clave, como la inercia térmica, con resultados validados mediante datos reales de rovers. Asimismo, se desarrolla un sistema de percepción multimodal que combina imágenes a color, de profundidad y térmicas para segmentar entornos no estructurados. Validado en pruebas de campo, este sistema genera mapas de transitabilidad en tiempo real, lo que facilita la navegación autónoma. También se introduce un sistema autónomo para localizar y estimar la posición de tubos de muestra en Marte, mediante simulaciones virtuales fotorrealistas y aprendizaje profundo, optimizado para hardware de a bordo.Esta investigación mejora significativamente la autonomía de los rovers planetarios, proporcionando soluciones avanzadas para la evaluación del terreno, la navegación y la recuperación autónoma de muestras, elementos clave para optimizar el rendimiento científico de las misiones de exploración planetaria