Células fotovoltaicas orgánicas y su integración en cultivos de invernadero

55 páginas.Trabajo Fin de Máster en Tecnología de los Sistemas de Energía Solar Fotovoltaica (2014/15). Tutora: Paula Sánchez Friera. En este proyecto se exponen tanto las necesidades del cultivo protegido como las del agricultor, focalizando la atención en las características específicas de la zona mediterránea. También se verá la importancia que tiene la elección de la cubierta, estructura y orientación del invernadero para el desarrollo del cultivo. Se describirán las características básicas de las células plásticas orgánicas así como su estado actual de desarrollo, potencial de innovación y técnicas de fabricación para comprender las posibilidades que éstas nos brindan. En el último capítulo del proyecto se buscarán posibles configuraciones de integración que afecten en la menor medida de lo posible al cultivo. Al mismo tiempo se evaluarán tanto la capacidad de producción eléctrica como los costes de la misma para dos localizaciones específicas

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

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