Industrial-like vehicle platforms for postgraduate laboratory courses on robotics

The interdisciplinary nature of robotics allows mobile robots to be used successfully in a broad range of courses at the postgraduate level and in Ph.D. research. Practical industrial-like mobile robotic demonstrations encourage students and increase their motivation by providing them with learning benefits not achieved with traditional educational robotic platforms. This paper presents VEGO, an industrial-like modular vehicle platform for robotic education with an appropriate infrastructure that has been demonstrated to be very useful at the postgraduate level. Besides learning engineering concepts, in performing industrial-like exercises, students develop valuable skills such as teamwork and the capacity to solve problems similar to those they may encounter in a real industrial environment. The developed infrastructure represents a valuable platform for robotic education that can be used in many different disciplines as a way to demonstrate how to cope with the difficulties and challenges related to the development of industrial infrastructure systems. The platform evaluation proved its ability to inculcate the expected engineering skills. A novel approach is adopted through the use of multidisciplinary and close-to-industrial-reality platforms developed under an incremental approach and using an open and customizable structure.This work was supported in part by the Fundación Séneca of the Murcia Region under Grant 15374/PI/10, the CICYT EXPLORE under Grant TIN2009-08572, and the INNPLANTA SiveLab, Ministry of Science and Innovation, Spain, under Grant INP-2011-0022-PCT-430000-ACT9

A machine learning approach to pedestrian detection for autonomous vehicles using High-Definition 3D Range Data

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).This work was partially supported by ViSelTR (ref. TIN2012-39279) and cDrone (ref. TIN2013-45920-R) projects of the Spanish Government, and the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia—19895/GERM/15). 3D LIDAR has been funded by UPCA13-3E-1929 infrastructure projects of the Spanish Government. Diego Alonso wishes to thank the Spanish Ministerio de Educación, Cultura y Deporte, Subprograma Estatal de Movilidad, Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016 for grant CAS14/00238

A systematic review of perception system and simulators for autonomous vehicles research

This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.This work was partially supported by DGT (ref. SPIP2017-02286) and GenoVision (ref. BFU2017-88300-C2-2-R) Spanish Government projects, and the “Research Programme for Groups of Scientific Excellence in the Region of Murcia" of the Seneca Foundation (Agency for Science and Technology in the Region of Murcia – 19895/GERM/15)

Development of bioinformatics tools for phenotyping with artificial vision

[SPA] El fenotipado de alta resolución mediante visión artificial está en pleno desarrollo, ya que nos permite obtener información de características no apreciables con otros métodos. Además, presenta ventajas como que es una técnica no invasiva, con un bajo impacto en el objeto de estudio. Con ella podríamos conocer el efecto de mutaciones en genes del reloj circadiano sobre la velocidad de crecimiento de los órganos laterales, como hojas, flores y frutos. El principal objetivo de mi doctorado sería el desarrollo de un programa automático de análisis de imagen para el fenotipado vegetal. Para ello se va a trabajar con distintos materiales vegetales (Petunia, Antirrhinum majus, Arabidopsis y Fresa) utilizando las herramientas bioinformáticas adecuadas (lenguajes informáticos como Perl, Python, SQL o R); así como un sistema de visión, que dependerá del objetivo específico del experimento (infrarrojo, RGB e hiperespectrales) con un control de temperatura e iluminación. [ENG] High-throughput phenotyping with artificial vision is becoming central in biology. It provides information about non-appreciable characteristics. Furthermore, it is a non-invasive technique, with a low impact on the subject of study. We could discover the effect of mutations of circadian clock genes affecting growth speed of lateral organs, such as leaves, flowers and fruits. The main aim of my PhD would be the development of an automatic program of image analysis for plant phenotyping. Different plant materials are going to be studied (Petunia, Antirrhinum majus, Arabidopsis and strawberry) using the adequate bioinformatic tools (informatic languages such as as Perl, Python, SQL or R); as well as a vision system, which will depend on the specific object of the experiment ( infrared, RGB and hyperspectral) with a temperature and lightning control.Este trabajo se enmarca dentro del proyecto CDTI 5117/17CTA-P y PROYECTO BFU 2017 88300-C2-1-

Development of ground truth dataset with phenotypic data analysis and their application in agriculture and research

[SPA] Actualmente, nuevas técnicas para el fenotipado vegetal, tales como la visión artificial, han permitido mejorar la detección de características. La inteligencia artificial necesita de datos masivos para la generación de algoritmos. Por ello, la creación de ground truth datasets es altamente relevante en investigación. Un dataset fue desarrollado usando 114 flores de Antirrhinum majus (variedad comercial Vilmorin long variee). Además, las características fenotípicas como la longitud, peso, anchura y contenido de antocianinas fueron medidas. Se confirmó que el peso es la mejor característica para determinar el estado de desarrollo floral. Además, este conocimiento permite la creación en un futuro de programas de machine learning utilizando estos datos para un fenotipado automática y no invasivo. [ENG] Currently, new techniques to plant phenotyping, such as computer vision, have enabled to improve the detection of several parameters. Artificial intelligence needs data to train algorithms, so the creation of ground truth datasets is highly relevant to research. A dataset was developed comprising 114 flowers of Antirrhinum majus (commercial variety Vilmorin long variee). Furthermore, several phenotypic features were measured, such as length, width, weight, and anthocyanin content. This study confirms that weight is the best parameter to determine flower development, as well as this knowledge enables to create machine learning algorithms to an automatic and non-invasive phenotyping

A lighting system to increase the early flowering in Fragaria x ananassa

[SPA] La integración de señales ambientales a los sistemas biológicos ocurre en muchos casos a través del reloj circadiano. Hemos llevado a cabo una manipulación del mismo en fresa comercial (Fragaria x ananassa) con condiciones de iluminación divergentes. En comparación con el control sin iluminado hemos obtenido aumentos de hasta un 270% de aumento de floración temprana en uno de los tratamientos luminosos. Los tratamientos no afectaron de forma significativa al tamaño del fruto. Los resultados indican el potencial de las diferentes longitudes de onda para mejorar las producciones tempranas de fresa. [ENG] The integration of environmental signals to biological systems occurs in many cases via the circadian. We have modified it using commercial strawberry (Fragaria x ananassa) with several lighting conditions. We obtained an increase up to 270% in the number of early flowers with a specific light treatment in comparison with non-artificial lighting control. The treatments do not affect the fruit size. The results indicate the importance of different wavelengths in order to improve the early productions of Strawberry.Este trabajo ha sido financiado por el proyecto CDTI-5717/17CTA-P

Analysis of growth kinetics of Petunia sp. using a computer vision based phenotyping system

[SPA] El fenotipado masivo de plantas es un importante reto para tanto para mejorar nuestra comprensión sobre el funcionamiento de estos sistemas biológicos como para la mejora del rendimiento de los cultivos. Los sistemas de fenotipado son herramientas que pueden ayudar a mejorar nuestra comprensión en este campo. En este trabajo mostramos el desarrollo de un sistema de fenotipado basado en visión artificial aplicado al estudio del crecimiento en flores de Petunia, mostrando su robustez y flexibilidad. Se ha podido observar que, a nivel de tejido reproductivo y en base a los datos analizados, no se observan diferencias significativas en la velocidad de crecimiento de la flor de Petunia. [ENG] High throughput phenotyping in plants, is a major challenge to improve our understanding of how these biological systems work and to improve crop yields. Phenotyping systems are tools that can help improve our understanding in this field. In this work, we show the development of a system of computer vision based phenotyping system applied to the study of growth in Petunia's flowers, showing its robustness and flexibility. As a result, it has been observed that, at the reproductive tissue level and based on the data analyzed, there are no significant differences in the growth rate of the Petunia flower.Agradezco a mis directores de tesis su interés, dedicación, consejos y críticas (siempre constructivas). El trabajo realizado se enmarca dentro de los proyectos MICINN BFU-2013-45148-R y ViSel-TR (TIN2012-39279)

Desarrollo de una herramienta software para el control de un sistema de fenotipado basado en visión artificial

[ESP] Los sistemas basados en visión artificial permiten automatizar el proceso de fenotipado de los sistemas biológicos. Estos sistemas permiten capturar grandes cantidades de datos de forma rápida y con un bajo coste asociado. Hemos desarrollado una herramienta software flexible para el fenotipado basada en visión artificial. La herramienta controla los parámetros del experimento: días de experimento, horas día/noche, permite la utilización de diferentes tipos de cámaras, etc. La herramienta ha sido programada en C++ lo que ha permitido integrar y ejecutar diferentes algoritmos de procesado de imagen de librerías como OPENCV y MIL. [ENG] Computer vision systems allow to automate the process of obtaining phenotypic features in plants. These systems produce large amounts of data in a quick fashion and with a low associated cost. In this work we present a flexible software tool for phenotyping analysis based on computer vision. The tool allows a total management of the experiment parameters such as experiment time, hours of night-time and daytime periods or use of different cameras with time of image acquisition. The system has been programmed in C++ allowing it to be applied in different computer environments, using different computer vision algorithms to perform image processing.Escuela Técnica Superior de Ingeniería de Telecomunicación (ETSIT), Escuela Técnica Superior de Ingeniería Agronómica (ETSIA), Escuela Técnica Superior de Ingeniería Industrial (ETSII), Escuela Técnica Superior de Arquitectura y Edificación (ETSAE), Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos y de Ingeniería de Minas (ETSICCPIM), Facultad de Ciencias de la Empresa (FCCE), Parque Tecnológico de Fuente Álamo (PTFA), Vicerrectorado de Estudiantes y Extensión de la UPCT, Vicerrectorado de Investigación e Innovación de la UPCT, y Vicerrectorado de Internacionalización y Cooperación al Desarrollo de la UPCT

Development of software tools to control an artificial vision-based phenotyping system

[SPA] Los sistemas basados en visión artificial permiten automatizar el proceso de fenotipado de los sistemas biológicos. Estos sistemas permiten capturar grandes cantidades de datos de forma rápida y con un bajo coste asociado. Hemos desarrollado una herramienta software flexible para el fenotipado basada en visión artificial. La herramienta controla los parámetros del experimento: días de experimento, horas día/noche, permite la utilización de diferentes tipos de cámaras, etc. La herramienta ha sido programada en C++ lo que ha permitido integrar y ejecutar diferentes algoritmos de procesado de imagen de librerías como OPENCV y MIL. [ENG] Computer vision systems allow to automate the process of obtaining phenotypic features in plants. These systems produce large amounts of data in a quick fashion and with a low associated cost. In this work we present a flexible software tool for phenotyping analysis based on computer vision. The tool allows a total management of the experiment parameters such as experiment time, hours of nighttime and daytime periods or use of different cameras with time of image acquisition. The system has been programmed in C++ allowing it to be applied in different computer environments, using different computer vision algorithms to perform image processing.El trabajo realizado se enmarca dentro de los proyectos MICINN BFU-2013-45148-R y ViSel-TR(TIN2012-39279) y ha sido presentado en el II Simposio Nacional de Ingeniería Hortícola, celebrado en Almería del 10-12 de febrero de 2016

Machine learning for leaf segmentation in NIR images based on wavelet transform

[SPA] En este trabajo se presenta un algoritmo de segmentación basado en máquinas de aprendizaje para la segmentación de hojas sobre imágenes NIR (Near-Infra-Red). El método de segmentación utiliza un vector de características extraído de diferentes niveles de la transformada wavelet. Para el desarrollo del algoritmo se han probado tres clasificadores: el vecino más cercano (KNN), un clasificador Bayesiano (NBC) y las máquinas de soporte compacto (SVM). Los métodos de aprendizaje han sido validados mediante el análisis de las curvas ROC y el máximo rendimiento fue obtenido por la SVM con un 98.33%. [ENG] In this work we present an algorithm to segment leaves in NIR images captured inside a growth chamber. The proposed method uses a features vector composed by four elements extracted from different levels of wavelets transform. We have tested three classifiers: k-nearest neighbour (kNN), Naive Bayes classifier (NBC) and Support Vector Machine to determine the optimal machine learning algorithm to carry out the leaf segmentation. Method developed has been validated by means of the Receiver Operating Characteristic (ROC) curve and it has obtained a maximum performance of 98.33% in the leaf segmentation using SVM classifier.Este trabajo ha sido parcialmente soportado por el Ministerio de Economía y Competitividad (MINECO) bajo los proyectos ViSelTR (TIN2012-39279) y cDrone (TIN2013-45920-R)