Composite prototyping and vision based hierarchical control of a quad tilt-wing UAV

As the attention to unmanned systems is increasing, unmanned aerial vehicles (UAVs) are becoming more popular based on the rapid advances in technology and growth in operational experience. The main motivation in this vast research field is to diminish the human driven tasks by employing UAVs in critical civilian and military tasks such as traffic monitoring, disasters, surveillance, reconnaissance and border security. Researchers have been developing featured UAVs with intelligent navigation and control systems on more efficient designs aiming to increase the functionality, flight time and maneuverability. This thesis focuses on the composite prototyping and vision based hierarchical control of a quad tilt-wing aerial vehicle (SUAVI: Sabanci University Unmanned Aerial VehIcle). With the tilt-wing mechanism, SUAVI is one of the most challenging UAV concepts by combining advantages of vertical take-off and landing (VTOL) and horizontal flight. Various composite materials are tested for their mechanical properties and the most suitable one is used for prototyping of the aerial vehicle. A hierarchical control structure which consists of high-level and low-level controllers is developed. A vision based high-level controller generates attitude references for the low-level controllers. A Kalman filter fuses data from low-cost inertial sensors to obtain reliable orientation information. Low-level controllers are typically gravity compensated PID controllers. An image based visual servoing (IBVS) algorithm for VTOL, hovering and trajectory tracking is successfully implemented in simulations. Real flight tests demonstrate satisfactory performance of the developed control algorithms

Learning and Execution of Object Manipulation Tasks on Humanoid Robots

Equipping robots with complex capabilities still requires a great amount of effort. In this work, a novel approach is proposed to understand, to represent and to execute object manipulation tasks learned from observation by combining methods of data analysis, graphical modeling and artificial intelligence. Employing this approach enables robots to reason about how to solve tasks in dynamic environments and to adapt to unseen situations

Maritime Augmented Reality mit a prioriWissen aus Seekarten

The main objective of this thesis is to provide a concept to augment mar- itime sea chart information into the camera view of the user. The benefit is the simpler navigation due to the offered 3D information and the overlay onto the real 3D environment. In the maritime context special conditions hold. The sensor technologies have to be reliable in the environment of a ship’s ferrous construction. The aug- mentation of the objects has to be very precise due to the far distances of observable objects on the sea surface. Furthermore, the approach has to be reliable due to the wide range of light conditions. For a practical solution, the system has to be mobile, light-weight and with a real-time performance. To achieve this goal, the requirements are set, the possible measurement units and the data base structure are presented. First, the requirements are analyzed and a suitable system is designed. By the combination of proper sensor techniques, the local position and orienta- tion of the user can be estimated. To verify the concept, several prototypes with exchangeable units have been evaluated. This first concept is based on a marker-based approach which leads to some drawbacks. To overcome the drawbacks, the second aspect is the improvement of the sys- tem and the analysis of markerless approaches. One possible strategy will be presented. The approach uses the statistical technique of Bayesian networks to vote for single objects in the environment. By this procedure it will be shown, that due to the a priori information the underlying sea chart system has the most benefit. The analysis of the markerless approach shows, that the sea charts structure has to be adapted to the new requirements of interactive 3D augmentation scenes. After the analysis of the chart data concept, an approach for the optimization of the charts by building up an object-to-object topology within the charts data and the Bayesian object detection approach is presented. Finally, several evaluations show the performance of the imple- mented evaluation application.Diese Arbeit stellt ein Konzept zur Verfügung, um Seekarteninformationen in eine Kamera so einzublenden, dass die Informationen lagerichtig im Sichtfeld des Benutzers erscheinen. Der Mehrwert ist eine einfachere Navigation durch die Nutzung von 3D-Symbolen in der realen Umgebung. Im maritimen Umfeld gelten besondere Anforderungen an die Aufgabenstellung. Die genutzten Sensoren müssen in der Lage sein, robuste Daten in Anwesenheit der eisenhaltigen Materialien auf dem Schiff zu liefern. Die Augmentierung muss hoch genau berechnet werden, da die beobachtbaren Objekte zum Teil sehr weit entfernt auf der Meeresoberfläche verteilt sind. Weiterhin gelten die Bedingungen einer Außenumgebung, wie variierende Wetter- und Lichtbedingungen. Um eine praktikable Anwendung gewährleisten zu können, ist ein mobiles, leicht-gewichtiges und echtzeitfähiges System zu entwickeln. In dieser Arbeit werden die Anforderungen gesetzt und Konzepte für die Hardware- und Softwarelösungen beschrieben. Im ersten Teil werden die Anforderungen analysiert und ein geeignetes Hardwaresystem entwickelt. Durch die passende Kombination von Sensortechnologien kann damit die lokale Position und Orientierung des Benutzers berechnet werden. Um das Konzept zu evaluieren sind verschiedene modulare Hardware- und Softwarekonzepte als Prototypen umgesetzt worden. Das erste Softwarekonzept befasst sich mit einem markerbasierten Erkennungsalgorithmus, der in der Evaluation einige Nachteile zeigt. Dementsprechende Verbesserungen wurden in einem zweiten Softwarekonzept durch einen markerlosen Ansatz umgesetzt. Dieser Lösungsansatz nutzt Bayes'sche Netzwerke zur Erkennung einzelner Objekte in der Umgebung. Damit kann gezeigt werden, dass mit der Hilfe von a priori Informationen die dem System zugrunde liegenden Seekarten sehr gut zu diesem Zweck genutzt werden können. Die Analyse des Systemkonzeptes zeigt des weiteren, dass die Datenstruktur der Seekarten für die Anforderungen einer interaktiven, benutzergeführten 3D- Augmentierungsszene angepasst werden müssen. Nach der ausführlichen Analyse des Seekarten-Datenkonzeptes wird ein Lösungsansatz zur Optimierung der internen Seekartenstruktur aufgezeigt. Dies wird mit der Erstellung einer Objekt-zu-Objekt-Topologie in der Datenstruktur und der Verbindung zum Bayes'schen Objekterkennungsalgorithmus umgesetzt. Anschließend zeigen Evaluationen die Fähigkeiten des endgültigen Systems

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Artificial Intelligence based Robotic Platforms for Autonomous Precision Agriculture

Robotic applications are continuously expanding into every aspect of human livelihood, it becomes paramount to leverage this trend for precision agriculture. The agricultural sector despite being an important sector for human is slowly evolving in terms of technology. Crude and manual processes which are conventionally used for agriculture have severe economic and social impacts. The inefficiencies and less productiveness of these methods results to food wastage amidst food shortage, inconsistencies, time consumption, higher labour expenses, and low yield. The world will benefit from automating the processes in agriculture. In bid of addressing such, it becomes necessary to build on existing platforms and develop intelligent autonomous vehicles for precision agriculture. This should include development of intelligent drones for precision agriculture, development of intelligent ground robots for precision agriculture, and other systems working cooperatively. To achieve this, we leverage on Artificial Intelligence (AI) and mathematical methods to impact sufficient intelligence on robotic platforms to make them suitable for precision agriculture. This thesis explores the capabilities of AI for weed classification and detection, weed relative position estimation, fruit 6D pose estimation and virtual reality for teleoperated systems in fruit picking. Infestation of weeds diminishes the yield of crops in agriculture. Deep learning is becoming a more popular approach for identifying weeds on farmlands. However, precision agriculture requires that the object of interest (weed) is precisely classified and detected to facilitate removal or spraying. An approach for this is presented and involves cascading a classification network (ResNet-50) with a detection network (YOLO) for weed classification and detection which we termed Fused-YOLO. Thus, weeds can precisely be located and classified (type) within an image frame. Inspired by the precision of this detection model, the work extends to presenting a novel monocular vision-based approach for drones to detect multiple types of weeds and estimate their positions autonomously for precision agriculture applications. A drone is subjected to an elliptical trajectory while acquiring images from an onboard monecular camera. The images are fed to the fused-YOLO model in real-time. The centre of the detection bounding boxes is leveraged to be the centre of the detected object of interest (weeds). The centre pixels are extracted and converted into world coordinates forming azimuth and elevation angles from the target to the UAV and are effectively used in an estimation scheme that adopts the Unscented Kalman Filteration to estimate the exact relative positions of the weeds. The robustness of this algorithm allows for both indoor and outdoor implementation while achieving a competitive result with affordable off-the-shelf sensors. Artificial intelligence for autonomous 6D pose estimation has valuable contributions to agricultural practices rallying around fruit picking, harvesting, remote operations and other contact-related applications. Conventionally, Convolutional Neural Networks (CNNs) based approaches are adopted for pose estimation. However, precision agriculture applications are demanding on higher accuracy at lower computational costs for real-time applications. Motivated by this, a novel architecture called Transpose is proposed based on transformers. TransPose is an improved Transformer-based 6D pose estimation with a depth refinement. More modalities often result in higher accuracy at the expense of computational cost. TransPose takes in a single RGB image as input without extra modality. However, an innovative light-weight depth estimation network architecture is incorporated into the model to estimate depth from an RGB image using a feature pyramid with an up-sampling method. A transformer model having proven to be efficient, regress the 6D pose directly and also outputs object patches. The depth and the patches are utilised to further refine the regressed 6D pose. The performance of the model is extensively assessed and compared with state-of-the-art methods. As part of this research, a first-ever fruit-oriented 6D pose dataset was acquired. Lastly, a seamless teleoperation pipeline that interfaces virtual reality with robots for precision agriculture tasks is proposed to pave the way for virtual agriculture. This utilises the Transpose model to estimate the 6D pose of a fruit and render it in a virtual reality environment. A robotic manipulator is which is then controlled from within the virtual reality environment to pick/harvest the fruit while being guided by the Transpose AI model. The robustness of the pipeline is tested over simulation and real-time implementation with a physical robotic manipulator is also investigated

XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja)

[Resumen] Las Jornadas de Automática (JA) son el evento más importante del Comité Español de Automática (CEA), entidad científico-técnica con más de cincuenta años de vida y destinada a la difusión e implantación de la Automática en la sociedad. Este año se celebra la cuadragésima tercera edición de las JA, que constituyen el punto de encuentro de la comunidad de Automática de nuestro país. La presente edición permitirá dar visibilidad a los nuevos retos y resultados del ámbito, y su uso en un gran número de aplicaciones, entre otras, las energías renovables, la bioingeniería o la robótica asistencial. Además de la componente científica, que se ve reflejada en este libro de actas, las JA son un punto de encuentro de las diferentes generaciones de profesores, investigadores y profesionales, incluyendo la componente social que es de vital importancia. Esta edición 2022 de las JA se celebra en Logroño, capital de La Rioja, región mundialmente conocida por la calidad de sus vinos de Denominación de Origen y que ha asumido el desafío de poder ganar competitividad a través de la transformación verde y digital. Pero también por ser la cuna del castellano e impulsar el Valle de la Lengua con la ayuda de las nuevas tecnologías, entre ellas la Automática Inteligente. Los organizadores de estas JA, pertenecientes al Área de Ingeniería de Sistemas y Automática del Departamento de Ingeniería Eléctrica de la Universidad de La Rioja (UR), constituyen un pilar fundamental en el apoyo a la región para el estudio, implementación y difusión de estos retos. Esta edición, la primera en formato íntegramente presencial después de la pandemia de la covid-19, cuenta con más de 200 asistentes y se celebra a caballo entre el Edificio Politécnico de la Escuela Técnica Superior de Ingeniería Industrial y el Monasterio de Yuso situado en San Millán de la Cogolla, dos marcos excepcionales para la realización de las JA. Como parte del programa científico, dos sesiones plenarias harán hincapié, respectivamente, sobre soluciones de control para afrontar los nuevos retos energéticos, y sobre la calidad de los datos para una inteligencia artificial (IA) imparcial y confiable. También, dos mesas redondas debatirán aplicaciones de la IA y la implantación de la tecnología digital en la actividad profesional. Adicionalmente, destacaremos dos clases magistrales alineadas con tecnología de última generación que serán impartidas por profesionales de la empresa. Las JA también van a albergar dos competiciones: CEABOT, con robots humanoides, y el Concurso de Ingeniería de Control, enfocado a UAVs. A todas estas actividades hay que añadir las reuniones de los grupos temáticos de CEA, las exhibiciones de pósteres con las comunicaciones presentadas a las JA y los expositores de las empresas. Por último, durante el evento se va a proceder a la entrega del “Premio Nacional de Automática” (edición 2022) y del “Premio CEA al Talento Femenino en Automática”, patrocinado por el Gobierno de La Rioja (en su primera edición), además de diversos galardones enmarcados dentro de las actividades de los grupos temáticos de CEA. Las actas de las XLIII Jornadas de Automática están formadas por un total de 143 comunicaciones, organizadas en torno a los nueve Grupos Temáticos y a las dos Líneas Estratégicas de CEA. Los trabajos seleccionados han sido sometidos a un proceso de revisión por pares

Manipulador aéreo con brazos antropomórficos de articulaciones flexibles

[Resumen] Este artículo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difícil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, así como el sistema de control integrado de la plataforma y los brazos. Una de las principales características del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones físicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de Economía y Competitividad; DPI2014-5983-C2-1-

Dispositivo portátil semi-automatizado de monda de frutos

A qualidade dos frutos está muito dependente da carga de uma árvore. Se a poda for menos severa para garantir maior produção e o vingamento dos frutos se revelar elevado pela ocorrência de condições climáticas favoráveis, resultará num excesso de carga de frutos na árvore, restando aos produtores fazer o correto ajustamento da carga através da monda dos frutos. A monda manual é o método mais utilizado mas, requer elevada disponibilidade de mão-de-obra, fator muitas vezes limitante, e por ser moroso torna-se bastante dispendioso. As soluções alternativas mecanizadas ou automatizadas encontradas na literatura não demonstraram ainda ser economicamente viáveis. Isto deriva do facto de essas soluções apresentarem uma operação em malha aberta, sem qualquer tipo de sensorização e com formatos de atuação que não permitem exercer a seletividade de remoção de frutos necessária para que a realização da monda mecanizada ou automatizada resulte num impacto positivo consistente. Com a presente dissertação de mestrado, pretende-se contribuir para o desenvolvimento e investigação de soluções para a mecanização e automatização de processos agrícolas que requeiram técnicas avançadas e inovadoras de sensorização e atuação, tendo como objetivo último o desenvolvimento de um dispositivo portátil para a automatização da monda de frutos. O dispositivo desenvolvido tem por base a utilização de um conjunto de transdutores ultrassónicos dispostos radialmente para a deteção e avaliação de frutos, e um conjunto de atuadores lineares, também estes dispostos radialmente, responsáveis pela remoção dos frutos pela execução de uma força de impacto. É apresentado o dimensionamento do hardware envolvido nestes sistemas, descrito o software de controlo implementado no formato de sistema embutido, assim como a investigação realizada em torno da aplicação de processamento de sinal e algoritmos de aprendizagem de máquina aos sinais adquiridos pelos transdutores ultrassónicos. O funcionamento atual do protótipo do dispositivo de monda ainda não é satisfatório, requerendo a utilização de dispositivos de sensorização e atuação mais avançados tecnologicamente, o que se configura inatingível no momento pela especificação de baixo custo que norteou todo o desenvolvimento do projeto.The quality of fruits is highly dependent on the load of the fruit trees. If the pruning process is not severe, as a way to ensure a high production level and the fruit setting rate turns out to be high as well, due to favourable climate conditions, it will result in a excessive fruit-load. In this case, the producers have to adjust the fruit-load by performing fruit thinning. Manual thinning is the most commonly used technique in fruit thinning, although it requires a high availability of labour force, which is frequently a limiting factor, and as it is a time-consuming activity it also becomes quite onerou. The alternative mechanized and automated solutions found in literature have not yet shown to be economically viable. This results from that fact that those solutions operate as open-loop systems, without any kind of sensor and with actuation strategies that do not enable to remove the fruits with the level of selectivity required for achieving a consistent positive economic impact. This master’s dissertation aims to contribute to the research and development of automated solutions of agricultural processes that require advanced and innovative sensing and actuation technologies and techniques, having as its ultimate purpose the development of a hand-held automatic fruit thinning device. The device is based upon the use of an array of radially arranged ultrasonic transducers, for the detection and evaluation and a set of linear actuators, also radially disposed, for the removal of the fruits. The design of these systems’ hardware is presented, the corresponding software which was implemented as an embedded system is then described, as well as the research that was performed involving the application of signal processing and machine learning algorithms to signals acquired by the ultrasonic transducers. The resulting prototype, in its current state, does not yet present an adequate level of functionality, requiring the use of more advanced sensors and actuators, which as of this moment is not attainable due to the low budget specification that led over the development of this project