Control de vuelo de un cuadricóptero AR-Drone

[ES]En este TFG se obtendrá un algoritmo de generación de trayectorias para el cuadricóptero AR-Drone capaz de conseguir trazados lo más rápidos posibles teniendo en cuenta la dinámica del modelo y las limitaciones del entorno. Así mismo se propondrá un control primitivo para la detección de obstáculos. Mediante las comunicaciones con el cuadricóptero y el control de los parámetros característicos del medio, se establecerá usando el lenguaje C# los bucles de programación necesarios para discriminar todas las trayectorias desfavorables y obtener una que se adecue a nuestros criterios.

Módulo de generación de trayectorias para el vuelo autónomo de un cuadricóptero.

[ES]La finalidad de este TFG es dotar a un Parrot AR Drone de un módulo que, en conjunto con otros desarrollos anteriores, sea capaz de realizar trayectorias evitando obstáculos. Después de realizar las simulaciones necesarias, utilizando un software de comunicación con el cuadricóptero, se pretenden obtener pruebas físicas de los avances en la programación.

Criação de uma estação de testes para um micro-helicóptero autónomo

Os UAV (Unmaned Air Vehicles) têm sido cada vez mais utilizados em vários ramos, como a geologia, a segurança pública, a robótica e na obtenção de imagens. Visto existir grande interesse no uso do equipamento, foi comprado um quadcopter com o objetivo poder tornálo o mais autónomo possível. Esta tese contém informações dos elementos utilizados nesse Arducopter e ainda a informação dos sensores que podem ser adaptados ao mesmo. Explica também como está estruturado todo o código e como o UAV pode ser configurado antes do voo. São também apresentados todos os resultados obtidos dos primeiros voos e as evoluções e alterações após cada teste.

Teoria del consenso e applicazione al problema del coordinamento del moto di robot.

Il sempre crescente numero di applicazioni di reti di sensori, robot cooperanti e formazioni di veicoli, ha fatto sì che le problematiche legate al coordinamento di sistemi multi-agente (MAS) diventassero tra le più studiate nell’ambito della teoria dei controlli. Esistono numerosi approcci per affrontare il problema, spesso profondamente diversi tra loro. La strategia studiata in questa tesi è basata sulla Teoria del Consenso, che ha una natura distribuita e completamente leader-less; inoltre il contenuto informativo scambiato tra gli agenti è ridotto al minimo. I primi 3 capitoli introducono ed analizzano le leggi di interazione (Protocolli di Consenso) che permettono di coordinare un Network di sistemi dinamici. Nel capitolo 4 si pensa all'applicazione della teoria al problema del "loitering" circolare di più robot volanti attorno ad un obiettivo in movimento. Si sviluppa a tale scopo una simulazione in ambiente Matlab/Simulink, che genera le traiettorie di riferimento di raggio e centro impostabili, a partire da qualunque posizione iniziale degli agenti. Tale simulazione è stata utilizzata presso il “Center for Research on Complex Automated Systems” (CASY-DEI Università di Bologna) per implementare il loitering di una rete di quadrirotori "CrazyFlie". I risultati ed il setup di laboratorio sono riportati nel capitolo 5. Sviluppi futuri si concentreranno su algoritmi locali che permettano agli agenti di evitare collisioni durante i transitori: il controllo di collision-avoidance dovrà essere completamente indipendente da quello di consenso, per non snaturare il protocollo di Consenso stesso.

Sviluppo di una architettura di controllo per velivoli di tipo multi-rotore

Studio e Progettazione di un sistema di sicurezza ed una legge di controllo per UAV di tipo quadcopter.

Cross-entropy optimization for scaling factors of a fuzzy controller: a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand- avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

Towards Mapping of Rock Walls Using a UAV-Mounted 2D Laser Scanner in GPS Denied Environments

In geotechnical engineering, the stability of rock excavations and walls is estimated by using tools that include a map of the orientations of exposed rock faces. However, measuring these orientations by using conventional methods can be time consuming, sometimes dangerous, and is limited to regions of the exposed rock that are reachable by a human. This thesis introduces a 2D, simulated, quadcopter-based rock wall mapping algorithm for GPS denied environments such as underground mines or near high walls on surface. The proposed algorithm employs techniques from the field of robotics known as simultaneous localization and mapping (SLAM) and is a step towards 3D rock wall mapping. Not only are quadcopters agile, but they can hover. This is very useful for confined spaces such as underground or near rock walls. The quadcopter requires sensors to enable self localization and mapping in dark, confined and GPS denied environments. However, these sensors are limited by the quadcopter payload and power restrictions. Because of these restrictions, a light weight 2D laser scanner is proposed. As a first step towards a 3D mapping algorithm, this thesis proposes a simplified scenario in which a simulated 1D laser range finder and 2D IMU are mounted on a quadcopter that is moving on a plane. Because the 1D laser does not provide enough information to map the 2D world from a single measurement, many measurements are combined over the trajectory of the quadcopter. Least Squares Optimization (LSO) is used to optimize the estimated trajectory and rock face for all data collected over the length of a light. Simulation results show that the mapping algorithm developed is a good first step. It shows that by combining measurements over a trajectory, the scanned rock face can be estimated using a lower-dimensional range sensor. A swathing manoeuvre is introduced as a way to promote loop closures within a short time period, thus reducing accumulated error. Some suggestions on how to improve the algorithm are also provided.