Visión por computador para UAS

La visión por computador es una parte de la inteligencia artificial que tiene una aplicación industrial muy amplia, desde la detección de piezas defectuosas al control de movimientos de los robots para la fabricación de piezas. En el ámbito aeronáutico, la visión por computador es una herramienta de ayuda a la navegación, pudiendo usarse como complemento al sistema de navegación inercial, como complemento a un sistema de posicionamiento como el GPS, o como sistema de navegación visual autónomo.Este proyecto establece una primera aproximación a los sistemas de visión articial y sus aplicaciones en aeronaves no tripuladas. La aplicación que se desarrollará será la de apoyo al sistema de navegación, mediante una herramienta que a través de las imágenes capturadas por una cámara embarcada, dé la orden al autopiloto para posicionar el aparato frente la pista en la maniobra de aterrizaje.Para poder realizar ese cometido, hay que estudiar las posibilidades y los desarrollos que el mercado ofrece en este campo, así como los esfuerzos investigadores de los diferentes centros de investigación, donde se publican multitud soluciones de visión por computador para la navegación de diferentes vehículos no tripulados, en diferentes entornos. Ese estudio llevará a cabo el proceso de la aplicación de un sistema de visión articial desde su inicio. Para ello, lo primero que se realizará será definir una solución viable dentro de las posibilidades que la literatura permita conocer. Además, se necesitará realizar un estudio de las necesidades del sistema, tanto de hardware como de software, y acudir al mercado para adquirir la opción más adecuada que satisfaga esas necesidades. El siguiente paso es el planteamiento y desarrollo de la aplicación, mediante la defnición de un algoritmo y un programa informático que aplique el algoritmo y analizar los resultados de los ensayos y las simulaciones de la solución. Además, se estudiará una propuesta de integración en una aeronave y la interfaz de la estación de tierra que debe controlar el proceso. Para finalizar, se exponen las conclusiones y los trabajos futuros para continuar la labor de desarrollo de este proyecto.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.