Vehículos autónomos de superficie : estudio y comparación de planificadores locales de ruta

En el desarrollo de vehículos autónomos de superficie, los planificadores locales de ruta son importantes, ya que permiten calcular o recalcular rutas cuando hay obstáculos presentes en el entorno. Una evaluación del desempeño de las diferentes técnicas de planificadores locales se presenta, usando un simulador que permite verificar, comparar, medir y visualizar las soluciones que entregan las diferentes técnicas. Las técnicas seleccionadas incluyen A*, Potential Fields, y RRT*. Luego, los resultados son comparados explicando las ventajas y desventajas de cada técnicaFil: Peralta, Federico. Universidad Nacional de Asunción (Paraguay)

An Evolutionary Computational Approach for Designing Micro Hydro Power Plants

Micro Hydro Power Plants (MHPP) constitute an effective, environmentally-friendly solution to deal with energy poverty in rural isolated areas, being the most extended renewable technology in this field. Nevertheless, the context of poverty and lack of qualified manpower usually lead to a poor usage of the resources, due to the use of thumb rules and user experience to design the layout of the plants, which conditions the performance. For this reason, the development of robust and efficient optimization strategies are particularly relevant in this field. This paper proposes a Genetic Algorithm (GA) to address the problem of finding the optimal layout for an MHPP based on real scenario data, obtained by means of a set of experimental topographic measurements. With this end in view, a model of the plant is first developed, in terms of which the optimization problem is formulated with the constraints of minimal generated power and maximum use of flow, together with the practical feasibility of the layout to the measured terrain. The problem is formulated in both single-objective (minimization of the cost) and multi-objective (minimization of the cost and maximization of the generated power) modes, the Pareto dominance being studied in this last case. The algorithm is first applied to an example scenario to illustrate its performance and compared with a reference Branch and Bound Algorithm (BBA) linear approach, reaching reductions of more than 70% in the cost of the MHPP. Finally, it is also applied to a real set of geographical data to validate its robustness against irregular, poorly sampled domains.Agencia Española de Cooperación Internacional para el Desarrollo 2014 / ACDE / 00601

Sistema de medida de calidad del agua mediante Pycom Fipy y sensores comerciales

En el presente proyecto se ha llevado a cabo un sistema de monitorización de variables del agua: temperatura, conductividad, pH, oxígeno disuelto y potencial de reducción-oxidación; a partir de sensores comerciales conectados a una placa de desarrollo Fipy (Pycom) de bajo coste. El sistema envía periódicamente los datos medidos, a través de WiFi, hacia una plataforma orientada a la recogida, visualización y procesamiento de datos denominada Thingspeak. Posteriormente, el sistema irá integrado en un vehículo autónomo de superficie. En el transcurso del proyecto se han llevado a cabo labores de diversa índole: - Programación de microcontroladores en lenguaje Micropython (Python para microcontroladores). - Desarrollo de prototipos: elección de elementos según funcionalidad y especificaciones, conexionado, pruebas de funcionamiento, etc. - Calibración de sensores. - Configuración de comunicación WiFi. - Configuración de canales en Thingspeak.In this project, a monitoring system for water variables (temperature, conductivity, pH, dissolved oxygen and oxidation-reduction potential) has been designed and produced. It is based on commercial sensors connected to a low-cost development board: Fipy (Pycom). The system periodically sends the measured data (through WiFi) to Thingspeak, a platform oriented to the collection, visualization and processing of data. The system will be integrated into an autonomous surface vehicle. During the project, different kind of tasks have been made: - Programming in Micropython (Python for microcontrollers). - Prototype development: selection of elements according to function and specifications, connections, functional tests, etc. - Sensor calibration. - WiFi configuration. - Thingspeak’s channel configuration.Universidad de Sevilla. Máster en Ingeniería Industria

Collision and Avoidance Modelling of Autonomous Vehicles using Genetic Algorithm and Neural Network

This thesis is to study the optimisation problems in autonomous vehicles, especially the modelling and optimisation of collision avoidance, and to develop some optimisation algorithms based on genetic algorithms and neural networks to operate autonomous vehicles without any collision. Autonomous vehicles, also called self-driving vehicles or driverless vehicles are completely robotised driving frameworks to allow the vehicle to react to outside conditions within a bunch of calculations to play out the undertakings. This thesis summarised artificial intelligence and optimisation techniques for autonomous driving systems in the literature. The optimisation problems related to autonomous vehicles are categorised into four groups: lane change, motion planner, collision avoidance, and artificial intelligence. A chart had been developed to summarise those research and related optimisation methods to help future researchers in the selection of optimisation methods Collision Avoidance is one of streamlining issues in autonomous vehicles. Several sensors had been used to identify position and dangers and collision avoidance algorithms had been developed to analyse the dangers and to use vehicles to avoid a collision. In this thesis, the current research on collision avoidance has been reviewed and some challenges and future works were presented to select the research direction of this thesis, the aim of this research will be the development of optimisation methods to avoid collisions in a predefined environment. The contributions of this thesis are that (1) a simulation model had been developed using Matlab for collision avoidance and serval scenarios were proposed and experimented with. The sensors are used as the inputs to determine collision in the learning preparation of the algorithm; (2) a neural network was used for collision avoidance of autonomous vehicles; (3) a new method was proposed with the combination of genetic algorithm and neural network. In the proposed frame, the neural network is used for decision making and a genetic algorithm is used for the training of the neural network. The results and experimentation show that the proposed strategies are well in the designed environment