Using MATLAB as a tool for focusing the lab work in engineering courses

Computer programming is a fundamental requirement when applying many theoretical concepts of engineering in practice, even in non-computer-engineering areas. Unfortunately, the hardware devices used in the engineering work are rarely standardized in their programming frameworks; it is therefore very common that we need to provide background to the students of engineering courses on several programming languages and environments, which often consumes an important part of the time of a subject during the semester and is not directly related to the syllabus. Here we address this problem by concentrating all the programming required for this kind of subjects on Matlab®. We describe how we have established this software as a common and single prototyping workbench in some subjects for producing, quickly and easily, algorithms that manage a number of different hardware devices in the lab, and to analyse the data obtained from the lab experiments with the powerful tools already included in its scientific toolboxes, eliminating the need of changing from one environment to another at any phase of the exercise. This has optimized the learning curve and concentrated the limited lab time on the really important topics of our subjects, requiring a reasonable effort from the professor if he or she is an expert programmer.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Solving Large-Scale Markov Decision Processes on Low-Power Heterogeneous Platforms

Markov Decision Processes (MDPs) provide a framework for a machine to act autonomously and intelligently in environments where the effects of its actions are not deterministic. MDPs have numerous applications. We focus on practical applications for decision making, such as autonomous driving and service robotics, that have to run on mobile platforms with scarce computing and power resources. In our study, we use Value Iteration to solve MDPs, a core method of the paradigm to find optimal sequences of actions, which is well known for its high computational cost. In order to solve these computationally complex problems efficiently in platforms with stringent power consumption constraints, high-performance accelerator hardware and parallelised software come to the rescue. We introduce a generalisable approach to implement practical applications for decision making, such as autonomous driving on mobile and embedded low-power heterogeneous SoC platforms that integrate an accelerator (GPU) with a multicore. We evaluate three scheduling strategies that enable concurrent execution and efficient use of resources on a variety of SoCs embedding a multicore CPU and integrated GPU, namely Oracle, Dynamic, and LogFit. We compare these strategies for solving an MDP modelling the use-case of autonomous robot navigation in indoor environments on four representative platforms for mobile decision-making applications with a power use ranging from 4 to 65 Watts. We provide a rigorous analysis of the results to better understand their behaviour depending on the MDP size and the computing platform. Our experimental results show that by using CPU-GPU heterogeneous strategies, the computation time and energy required are considerably reduced with respect to multicore implementation, regardless of the computational platform.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work was partially supported by the Spanish project TIN 2016-80920-R

LEGO© Mindstorms NXT and Q-Learning: a teaching approach for robotics in engineering

Robotics has become a common subject in many engineering degrees and postgraduate programs. Although at undergraduate levels the students are introduced to basic theoretical concepts and tools, at postgraduate courses more complex topics have to be covered. One of those advanced subjects is Cognitive Robotics, which covers aspects like automatic symbolic reasoning, decision-making, task planning or machine learning. In particular, Reinforcement Learning (RL) is a machine learning and decision-making methodology that does not require a model of the environment where the robot operates, overcoming this limitation by making observations. In order to get the greatest educational benefit, RL theory should be complemented with some hands-on RL task that uses a real robot, so students get a complete vision of the learning problem, as well as of the issues that arise when dealing with a physical robotic platform. There are several RL techniques that can be studied in such a subject; we have chosen Q-learning, since is a simple, effective and well-known RL algorithm. In this paper we present a minimalist implementation of the Q-learning method for a Lego Mindstorms NXT mobile robot, focused on simplicity and applicability, and flexible enough to be adapted to several tasks. Starting from a simple wandering problem, we first design an off-line model of the learning process in which the Q-learning parameters are studied. After that, we implement the algorithm on the robot, gradually enlarging the number of states-actions of the problem. The final result of this work is a teaching framework for developing practical activities regarding Q-learning in our Robotics subjects, which will improve our teaching.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Campus Virtual y una asignatura masificada adaptada al EEES. Logros y retos pendientes

En este trabajo exponemos las ventajas e inconvenientes del uso de una plataforma educativa virtual para la docencia de una asignatura masificada adaptada al EEES. Presentamos resultados basados en estadísticos de uso del Campus Virtual de la UMA y en la opinión de los alumnos, que nos permiten reconocer aspectos positivos de esta herramienta y cuestiones mejorables que será necesario abordar bajo otras perspectivas

A Safety System based on Bluetooth Low Energy (BLE) to prevent the misuse of Personal Protection Equipment (PPE) in construction

In this paper we address the issue of safety in the use of Personal Protection Equipment (PPE) in construction, industrial, or similar sites where power tools are used. We propose a novel solution that can control actively the power of the tool depending on the worker–tool distance. It is based on RSSI information transmitted by BLE devices arranged in a particular rig, combined with a Bayesian distance estimator. Such an approach minimizes the required instrumentation of the workplace and also the number of configuration parameters; therefore it enables a wide range of applications. Our aim is not only to signal risky situations caused by the misuse of the PPE (either due to its bad fitting or a wrong distance to the tool), but to intervene in a fast and robust way to avoid the safety risk. This solution is built upon previous results on the statistically sound measurement of distances and closeness in construction sites. Here, we contribute with a thorough analysis of collocating several BLE transmitters near orthogonally, which reduces interferences while avoiding the cost of more advanced technologies. We study how many transmitters are needed and what parameters are the best in the Bayesian filter for the optimal performance of the system. Real experiments with a prototype have been conducted in a construction workshop where a person operates a miter saw. The results show how the correct use of the PPE (an earmuff equipped with the BLE transmitters) can be inferred from the distance estimation in a robust and reliable way.This research received funding from Plan Propio-Universidad de Málaga and it is associated to the Proyecto Puente “Integración de dispositivos basados en el paradigma loT para la mejora de seguridad laboral en proyectos de contrucción (IoTcons)”. Funding for open access charge: Universidad de Málaga / CBU

On the parallelization of a three-parametric log-logistic estimation algorithm

Networked telerobots transmit data from its sensors to the remote controller. To provide guarantees on the time requirements of these systems it is mandatory to keep the transmission time delays below a given threshold, and to that end we should predict them. In this paper we tackle the parallelization of a procedure that models these stochastic time delays. More precisely, we focus on fitting the time delay signal using a three-parametrical log-logistic distribution. Since, the robot and the controller are powered by multicore processors and, mainly on the robot, the energy consumption is a relevant issue, we study different alternatives to optimize both performance and energy usage of the aforesaid algorithm. Two quad-core processors are considered: a low power Intel Core i7 (45W TDP) and a ultra low power Samsung Exynos 5 (6W TDP). Results show that parallelism is beneficial, but that not all the cores should be exploited if the system is targeted at optimizing a performance-energy tradeoff.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Hierarchical regulation of sensor data transmission for networked telerobots

Networked telerobots carry sensors that send data, with stochastic transmission times, to a remote human operator, who must execute some real-time control task (e.g., navigation). In this paper we propose to regulate the sensory information being transmitted in order to guarantee soft real-time requirements and also optimize the quality of control, through a novel two-level hierarchical controller that both varies the amount of transmitted sensor data and dynamically reconfigures active sensors. Our controller has been implemented in a web-based teleoperator interface that is highly portable (it runs on desktop PCs, tablets, smartphones, etc.) and non-invasive, i.e., requires minimal modifications in the existing components of the system, thus being suitable for many applications. Here we present our regulation methods and the results of some experiments. They demonstrate the maximization of the transmitted data while guaranteeing the real-time requirements.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Optimizing subject design, timing, and focus in a diversity of engineering courses through the use of a low-cost Arduino shield

This paper describes the design, implementation and evaluation of a novel circuitry that extends the popular Arduino UNO microcontroller board to facilitate multiple educational activities in engineering courses. In particular, the aim of this board, the UMA-AEB, is to minimize the overhead that is usually imposed on the students before they can conduct the actual exercises, yet retain the valuable experiences that could otherwise not be acquired with simulated experiments or inflexible electronic training-benches.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work has been supported by the University of Málaga (Spain) through the educational innovation project PIE-15-093 “Innovación en el trabajo en laboratorio de una diversidad de asignaturas de ingeniería mediante el diseño y aplicación de una extensión de la plataforma de hardware abierto Arduino”

Experiencias de una red docente: aprendizaje significativo en ciencias y tecnología mediante gamificación

En el aprendizaje de los contenidos de materias científico-técnicas es especialmente importante la motivación del estudiante mediante referencias y ejercicios de casos reales donde se trata de reducir la memorización de muchos conceptos reforzando el aprendizaje significativo. De este modo, el método de aprendizaje significativo basado en gamificación se lleva a cabo mediante el uso de técnicas, elementos y dinámicas propias de los juegos y el ocio en actividades docentes con el fin de potenciar la motivación, así como de reforzar la conducta para solucionar problemas, mejorar la productividad, obtener objetivos, activar el aprendizaje y evaluar a los alumnos. En esta comunicación se explicará el proceso de puesta en común, diseño, desarrollo y evaluación de las experiencias de una Red Docente que ha empleado la gamificación como metodología para conseguir el aprendizaje significativo de los alumnos. La acción se ha llevado a cabo en las asignaturas de segundo cuatrimestre en los Estudios de Grado de Química, Biología, Ciencias Ambientales, Ingeniería de la salud, Bioquímica e Ingeniería de Telecomunicación de la Universidad de Málaga que imparten los profesores que componen la Red Docente. Para llevarla a cabo los profesores han conseguido una ayuda para fomentar la creación e implantación de redes docentes de excelencia gracias al Plan Propio Integral de Docencia de la Universidad de Málaga.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A multidimensional Bayesian architecture for real-time anomaly detection and recovery in mobile robot sensory systems

peer reviewedFor mobile robots to operate in an autonomous and safe manner they must be able to adequately perceive their environment despite challenging or unpredictable conditions in their sensory apparatus. Usually, this is addressed through ad-hoc, not easily generalizable Fault Detection and Diagnosis (FDD) approaches. In this work, we leverage Bayesian Networks (BNs) to propose a novel probabilistic inference architecture that provides generality, rigorous inferences and real-time performance for the detection, diagnosis and recovery of diverse and multiple sensory failures in robotic systems. Our proposal achieves all these goals by structuring a BN in a multidimensional setting that up to our knowledge deals coherently and rigorously for the first time with the following issues: modeling of complex interactions among the components of the system, including sensors, anomaly detection and recovery; representation of sensory information and other kinds of knowledge at different levels of cognitive abstraction; and management of the temporal evolution of sensory behavior. Real-time performance is achieved through the compilation of these BNs into feedforward neural networks. Our proposal has been implemented and tested for mobile robot navigation in environments with human presence, a complex task that involves diverse sensor anomalies. The results obtained from both simulated and real experiments prove that our architecture enhances the safety and robustness of robotic operation: among others, the minimum distance to pedestrians, the tracking time and the navigation time all improve statistically in the presence of anomalies, with a diversity of changes in medians ranging from ≃20% to ≃500%