Developing a Simulation Model for Autonomous Driving Education in the Robobo SmartCity Framework

Abstract: This paper focuses on long-term education in Artificial Intelligence (AI) applied to robotics. Specifically, it presents the Robobo SmartCity educational framework. It is based on two main elements: the smartphone-based robot Robobo and a real model of a smart city. We describe the development of a simulation model of Robobo SmartCity in the CoppeliaSim 3D simulator, implementing both the real mock-up and the model of Robobo. In addition, a set of Python libraries that allow teachers and students to use state-of-the-art algorithms in their education projects is described too.Ministerio de Ciencia, Innovación y Universidades of Spain/FEDER; t RTI2018-101114-B-I00 Erasmus+ Programme of the European Union; 2019-1-ES01-KA201-065742, Centro de Investigación de Galicia “CITIC”; ED431G 2019/01

Robobo SmartCity: An Autonomous Driving Model for Computational Intelligence Learning through Educational Robotics

© 2023 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.[Abstract]: This paper presents the Robobo SmartCity model, an educational resource to introduce students in Computational Intelligence (CI) topics using educational robotics as the core learning technology. Robobo SmartCity allows educators to train learners in Artificial Intelligence (AI) fundamentals from a feasible and practical perspective, following the recommendations of digital education plans to introduce AI at all educational levels. This resource is based on the Robobo educational robot and an autonomous driving setup. It is made up of a city mockup, simulation models, and programming libraries adapted to the students' skill level. In it, students can be trained in CI topics that support robot autonomy, as computer vision, machine learning, or human-robot interaction, while developing solutions in the motivating and challenging scope of autonomous driving. The main details of this open resource are provided with a set of possible challenges to be faced in it. They are organized in terms of the educational level and students’ skills. The resource has been mainly tested with secondary and high school students, obtaining successful learning outcomes, presented here to inspire other teachers in taking advantage of this learning technology in their classes.Xunta de Galicia; ED431G 2019/01This work has been partially funded by the Erasmus+ Programme of the European Union through grant number 2019-1-ES01-KA201-065742, and the Centro de Investigación de Galicia "CITIC", funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01. In addition, the “Programa de ayudas a la etapa predoctoral” from Xunta de Galicia (Consellería de Cultura, Educación y Universidad) supported this work through Sara Guerreiro’s grant

AI curriculum for european high schools: an embedded intelligence approach

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUGXunta de Galicia ; ED431G 2019/0

Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system

Pseudo-nitzschia Blooms in a Coastal Upwelling System: Remote Sensing Detection, Toxicity and Environmental Variables

The NW coast of the Iberian Peninsula is dominated by extensive shellfish farming, which places this region as a world leader in mussel production. Harmful algal blooms in the area frequent lead to lengthy harvesting closures threatening food security. This study developed a framework for the detection of Pseudo-nitzschia blooms in the Galician rias from satellite data (MERIS full-resolution images) and identified key variables that affect their abundance and toxicity. Two events of toxin-containing Pseudo-nitzschia were detected (up to 2.5 μg L−1 pDA) in the area. This study suggests that even moderate densities of Pseudo-nitzschia in this area might indicate high toxin content. Empirical models for particulate domoic acid (pDA) were developed based on MERIS FR data. The resulting remote-sensing model, including MERIS bands centered around 510, 560, and 620 nm explain 73% of the pDA variance (R2 = 0.73, p < 0.001). The results show that higher salinity values and lower Si(OH)4/N ratios favour higher Pseudo-nitzschia spp. abundances. High pDA values seem to be associated with relatively high PO43, low NO3− concentrations, and low Si(OH)4/N. While MERIS FR data and regionally specific algorithms can be useful for detecting Pseudo-nitzschia blooms, nutrient relationships are crucial for predicting the toxicity of these blooms

Sentinels of Seabed (SoS) indicator: Assessing benthic habitats condition using typical and sensitive species

Indicators are key tools used to assess the ecological status of the environment for ecosystem based management. Anthropogenic disturbances produce changes to habitat condition, which include modifications in species composition and their functions. Monitoring a group of sentinel species (from a taxonomic and functional point of view) provides useful insights into benthic habitat condition. Here, a new indicator, Sentinels of the Seabed (SoS) is proposed to assess state of benthic habitats using “sentinel” species (species which are characteristic of a habitat and sensitive to a given pressure). The selection of these sentinel species has two stages. First, a ‘typical species set’ is computed using intra-habitat similarity and frequency under reference conditions. Second, the ‘sentinel species set’ is generated by selecting the most sensitive species from the typical species set. This selection is made using specific indexes able to assess species sensitivity to a particular pressure. The SoS indicator method was tested on six case studies and two different pressure types (trawling disturbance and pollution), using data from otter trawl, box-corer and Remote Operate Vehicle images. In each scenario, the SoS indicator was compared to the Shannon-Wiener diversity index, Margalef index and total biomass, being the only metric, which showed the expected significant negative response to pressure in all cases. Our results shows that SoS was highly effective in assessing benthic habitats status under both physical and chemical pressures, regardless of the sampling gear, the habitat, or the case study, showing a great potential to be a useful tool in the management of marine ecosystems.Versión del editor2,69

Estrategia marina demarcación marina levantino-balear parte IV. Descriptores del buen estado ambiental. Descriptor 1: biodiversidad evaluación inicial y buen estado ambiental

El descriptor 1 de la Ley 41/2010 de protección del medio marino, trasposición a la ley española de la Directiva Marco sobre la Estrategia Marina (DMEM: 2008/56/CE) dice textualmente "Se mantiene la biodiversidad. La calidad y la frecuencia de los hábitats y la distribución y abundancia de las especies están en consonancia con las condiciones fisiográficas, geográficas y climáticas". Según el Convenio sobre la Diversidad Biológica (UNCED, 1992), ésta se define como: "La variabilidad de organismos vivos de cualquier fuente, incluidos, entre otras cosas, los ecosistemas terrestres y marinos y otros ecosistemas acuáticos y los complejos ecológicos de los que forman parte; comprende la diversidad dentro de cada especie, entre especies y de los ecosistemas"

Understanding Marine Mussel Adhesion

In addition to identifying the proteins that have a role in underwater adhesion by marine mussels, research efforts have focused on identifying the genes responsible for the adhesive proteins, environmental factors that may influence protein production, and strategies for producing natural adhesives similar to the native mussel adhesive proteins. The production-scale availability of recombinant mussel adhesive proteins will enable researchers to formulate adhesives that are water-impervious and ecologically safe and can bind materials ranging from glass, plastics, metals, and wood to materials, such as bone or teeth, biological organisms, and other chemicals or molecules. Unfortunately, as of yet scientists have been unable to duplicate the processes that marine mussels use to create adhesive structures. This study provides a background on adhesive proteins identified in the blue mussel, Mytilus edulis, and introduces our research interests and discusses the future for continued research related to mussel adhesion