A framework for autonomous mission and guidance control of unmanned aerial vehicles based on computer vision techniques

A computação visual é uma área do conhecimento que estuda o desenvolvimento de sistemas artificiais capazes de detectar e desenvolver a percepção do meio ambiente através de informações de imagem ou dados multidimensionais. A percepção visual e a manipulação são combinadas em sistemas robóticos através de duas etapas "olhar"e depois "movimentar-se", gerando um laço de controle de feedback visual. Neste contexto, existe um interesse crescimente no uso dessas técnicas em veículos aéreos não tripulados (VANTs), também conhecidos como drones. Essas técnicas são aplicadas para posicionar o drone em modo de vôo autônomo, ou para realizar a detecção de regiões para vigilância aérea ou pontos de interesse. Os sistemas de computação visual geralmente tomam três passos em sua operação, que são: aquisição de dados em forma numérica, processamento de dados e análise de dados. A etapa de aquisição de dados é geralmente realizada por câmeras e sensores de proximidade. Após a aquisição de dados, o computador embarcado realiza o processamento de dados executando algoritmos com técnicas de medição (variáveis, índice e coeficientes), detecção (padrões, objetos ou áreas) ou monitoramento (pessoas, veículos ou animais). Os dados processados são analisados e convertidos em comandos de decisão para o controle para o sistema robótico autônomo Visando realizar a integração dos sistemas de computação visual com as diferentes plataformas de VANTs, este trabalho propõe o desenvolvimento de um framework para controle de missão e guiamento de VANTs baseado em visão computacional. O framework é responsável por gerenciar, codificar, decodificar e interpretar comandos trocados entre as controladoras de voo e os algoritmos de computação visual. Como estudo de caso, foram desenvolvidos dois algoritmos destinados à aplicação em agricultura de precisão. O primeiro algoritmo realiza o cálculo de um coeficiente de reflectância visando a aplicação auto-regulada e eficiente de agroquímicos, e o segundo realiza a identificação das linhas de plantas para realizar o guiamento dos VANTs sobre a plantação. O desempenho do framework e dos algoritmos propostos foi avaliado e comparado com o estado da arte, obtendo resultados satisfatórios na implementação no hardware embarcado.Cumputer Vision is an area of knowledge that studies the development of artificial systems capable of detecting and developing the perception of the environment through image information or multidimensional data. Nowadays, vision systems are widely integrated into robotic systems. Visual perception and manipulation are combined in two steps "look" and then "move", generating a visual feedback control loop. In this context, there is a growing interest in using computer vision techniques in unmanned aerial vehicles (UAVs), also known as drones. These techniques are applied to position the drone in autonomous flight mode, or to perform the detection of regions for aerial surveillance or points of interest. Computer vision systems generally take three steps to the operation, which are: data acquisition in numerical form, data processing and data analysis. The data acquisition step is usually performed by cameras or proximity sensors. After data acquisition, the embedded computer performs data processing by performing algorithms with measurement techniques (variables, index and coefficients), detection (patterns, objects or area) or monitoring (people, vehicles or animals). The resulting processed data is analyzed and then converted into decision commands that serve as control inputs for the autonomous robotic system In order to integrate the visual computing systems with the different UAVs platforms, this work proposes the development of a framework for mission control and guidance of UAVs based on computer vision. The framework is responsible for managing, encoding, decoding, and interpreting commands exchanged between flight controllers and visual computing algorithms. As a case study, two algorithms were developed to provide autonomy to UAVs intended for application in precision agriculture. The first algorithm performs the calculation of a reflectance coefficient used to perform the punctual, self-regulated and efficient application of agrochemicals. The second algorithm performs the identification of crop lines to perform the guidance of the UAVs on the plantation. The performance of the proposed framework and proposed algorithms was evaluated and compared with the state of the art, obtaining satisfactory results in the implementation of embedded hardware

Vision-Based navigation system for unmanned aerial vehicles

Mención Internacional en el título de doctorThe main objective of this dissertation is to provide Unmanned Aerial Vehicles (UAVs) with a robust navigation system; in order to allow the UAVs to perform complex tasks autonomously and in real-time. The proposed algorithms deal with solving the navigation problem for outdoor as well as indoor environments, mainly based on visual information that is captured by monocular cameras. In addition, this dissertation presents the advantages of using the visual sensors as the main source of data, or complementing other sensors in providing useful information; in order to improve the accuracy and the robustness of the sensing purposes. The dissertation mainly covers several research topics based on computer vision techniques: (I) Pose Estimation, to provide a solution for estimating the 6D pose of the UAV. This algorithm is based on the combination of SIFT detector and FREAK descriptor; which maintains the performance of the feature points matching and decreases the computational time. Thereafter, the pose estimation problem is solved based on the decomposition of the world-to-frame and frame-to-frame homographies. (II) Obstacle Detection and Collision Avoidance, in which, the UAV is able to sense and detect the frontal obstacles that are situated in its path. The detection algorithm mimics the human behaviors for detecting the approaching obstacles; by analyzing the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. Then, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, the algorithm extracts the collision-free zones around the obstacle, and combining with the tracked waypoints, the UAV performs the avoidance maneuver. (III) Navigation Guidance, which generates the waypoints to determine the flight path based on environment and the situated obstacles. Then provide a strategy to follow the path segments and in an efficient way and perform the flight maneuver smoothly. (IV) Visual Servoing, to offer different control solutions (Fuzzy Logic Control (FLC) and PID), based on the obtained visual information; in order to achieve the flight stability as well as to perform the correct maneuver; to avoid the possible collisions and track the waypoints. All the proposed algorithms have been verified with real flights in both indoor and outdoor environments, taking into consideration the visual conditions; such as illumination and textures. The obtained results have been validated against other systems; such as VICON motion capture system, DGPS in the case of pose estimate algorithm. In addition, the proposed algorithms have been compared with several previous works in the state of the art, and are results proves the improvement in the accuracy and the robustness of the proposed algorithms. Finally, this dissertation concludes that the visual sensors have the advantages of lightweight and low consumption and provide reliable information, which is considered as a powerful tool in the navigation systems to increase the autonomy of the UAVs for real-world applications.El objetivo principal de esta tesis es proporcionar Vehiculos Aereos no Tripulados (UAVs) con un sistema de navegacion robusto, para permitir a los UAVs realizar tareas complejas de forma autonoma y en tiempo real. Los algoritmos propuestos tratan de resolver problemas de la navegacion tanto en ambientes interiores como al aire libre basandose principalmente en la informacion visual captada por las camaras monoculares. Ademas, esta tesis doctoral presenta la ventaja de usar sensores visuales bien como fuente principal de datos o complementando a otros sensores en el suministro de informacion util, con el fin de mejorar la precision y la robustez de los procesos de deteccion. La tesis cubre, principalmente, varios temas de investigacion basados en tecnicas de vision por computador: (I) Estimacion de la Posicion y la Orientacion (Pose), para proporcionar una solucion a la estimacion de la posicion y orientacion en 6D del UAV. Este algoritmo se basa en la combinacion del detector SIFT y el descriptor FREAK, que mantiene el desempeno del a funcion de puntos de coincidencia y disminuye el tiempo computacional. De esta manera, se soluciona el problema de la estimacion de la posicion basandose en la descomposicion de las homografias mundo a imagen e imagen a imagen. (II) Deteccion obstaculos y elusion colisiones, donde el UAV es capaz de percibir y detectar los obstaculos frontales que se encuentran en su camino. El algoritmo de deteccion imita comportamientos humanos para detectar los obstaculos que se acercan, mediante el analisis de la magnitud del cambio de los puntos caracteristicos detectados de referencia, combinado con los ratios de expansion de los contornos convexos construidos alrededor de los puntos caracteristicos detectados en frames consecutivos. A continuacion, comparando la proporcion del area del obstaculo y la posicion del UAV, el metodo decide si el obstaculo detectado puede provocar una colision. Por ultimo, el algoritmo extrae las zonas libres de colision alrededor del obstaculo y combinandolo con los puntos de referencia, elUAV realiza la maniobra de evasion. (III) Guiado de navegacion, que genera los puntos de referencia para determinar la trayectoria de vuelo basada en el entorno y en los obstaculos detectados que encuentra. Proporciona una estrategia para seguir los segmentos del trazado de una manera eficiente y realizar la maniobra de vuelo con suavidad. (IV) Guiado por Vision, para ofrecer soluciones de control diferentes (Control de Logica Fuzzy (FLC) y PID), basados en la informacion visual obtenida con el fin de lograr la estabilidad de vuelo, asi como realizar la maniobra correcta para evitar posibles colisiones y seguir los puntos de referencia. Todos los algoritmos propuestos han sido verificados con vuelos reales en ambientes exteriores e interiores, tomando en consideracion condiciones visuales como la iluminacion y las texturas. Los resultados obtenidos han sido validados con otros sistemas: como el sistema de captura de movimiento VICON y DGPS en el caso del algoritmo de estimacion de la posicion y orientacion. Ademas, los algoritmos propuestos han sido comparados con trabajos anteriores recogidos en el estado del arte con resultados que demuestran una mejora de la precision y la robustez de los algoritmos propuestos. Esta tesis doctoral concluye que los sensores visuales tienen las ventajes de tener un peso ligero y un bajo consumo y, proporcionar informacion fiable, lo cual lo hace una poderosa herramienta en los sistemas de navegacion para aumentar la autonomia de los UAVs en aplicaciones del mundo real.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando García Fernández.- Vocal: Pascual Campoy Cerver

Study of artificial intelligence and computer vision methods for tracking transmission lines with the AID of UAVs

Currently, Unmanned Aerial Vehicles (UAVs) have been used in the most diverse applications in both the civil and military sectors. In the civil sector, aerial inspection services have been gaining a lot of attention, especially in the case of inspections of high voltage electrical systems transmission lines. This type of inspection involves a helicopter carrying three or more people (technicians, pilot, etc.) flying over the transmission line along its entire length which is a dangerous service especially due to the proximity of the transmission line and possible environmental conditions (wind gusts, for example). In this context, the use of UAVs has shown considerable interest due to their low cost and safety for transmission line inspection technicians. This work presents research results related to the application of UAVs for transmission lines inspection, autonomously, allowing the identification of invasions of the transmission line area as well as possible defects in components (cables, insulators, connection, etc.) through the use of Convolutional Neural Networks (CNN) for fault detection and identification. This thesis proposes the development of an autonomous system to track power transmission lines using UAVs efficiently and with low implementation and operation costs, based exclusively on rea-time image processing that identifies the structure of the towers and transmission lines durin the flight and controls the aircraft´s movements, guiding it along the closest possible path. A sumary of the work developed will be presented in the next sections.Atualmente, os Veículos Aéreos Não Tripulados – VANTs têm sido utilizados nas mais diversas aplicações tanto no setor civil quanto militar. No setor civil, os serviços de inspeção aérea vêm ganhando bastante atenção, principalmente no caso de inspeções de linhas de transmissão de sistemas elétricos de alta tensão. Este tipo de inspeção envolve um helicóptero transportando três ou mais pessoas (técnicos, pilotos, etc.) sobrevoando a linha de transmissão em toda a sua extensão, o que constitui um serviço perigoso principalmente pela proximidade da linha de transmissão e possíveis condições ambientais (rajadas de vento, por exemplo). Neste contexto, a utilização de VANTs tem demonstrado considerável interesse devido ao seu baixo custo e segurança para técnicos de inspeção de linhas de transmissão. Este trabalho apresenta resultados de pesquisas relacionadas à aplicação de VANTs para inspeção de linhas de transmissão, de forma autônoma, permitindo a identificação de invasões da área da linha de transmissão bem como possíveis defeitos em componentes (cabos, isoladores, conexões, etc.) através do uso de Convolucional. Redes Neurais - CNN para detecção e identificação de falhas. Esta tese propõe o desenvolvimento de um sistema autônomo para rastreamento de linhas de transmissão de energia utilizando VANTs de forma eficiente e com baixos custos de implantação e operação, baseado exclusivamente no processamento de imagens em tempo real que identifica a estrutura das torres e linhas de transmissão durante o voo e controla a velocidade da aeronave. movimentos, guiando-o pelo caminho mais próximo possível. Um resumo do trabalho desenvolvido será apresentado nas próximas seções

Artificial intelligence and image processing applications for high-throughput phenotyping

Doctor of PhilosophyDepartment of Computer ScienceMitchell L NeilsenThe areas of Computer Vision and Scientific Computing have witnessed rapid growth in the last decade with the fields of industrial robotics, automotive and healthcare acting as the primary vehicles for research and advancement. However, related research in other fields, such as agriculture, remains an understudied problem. This dissertation explores the application of Computer Vision and Scientific Computing in an agricultural domain known as High-throughput Phenotyping (HTP). HTP is the assessment of complex seed traits such as growth, development, tolerance, resistance, ecology, yield, and the measurement of parameters that form more complex traits. The dissertation makes the following contributions: The first contribution is the development of algorithms to estimate morphometric traits such as length, width, area, and seed kernel count using 3-D graphics and static image processing, and the extension of existing algorithms for the same. The second contribution is the development of lightweight frameworks to aid in synthetic image dataset creation and image cropping for deep neural networks in HTP. Deep neural networks require a plethora of training data to yield results of the highest quality. However, no such training datasets are readily available for HTP research, especially on seed kernels. The proposed synthetic image generation framework helps generate a profusion of training data at will to train neural networks from a meager samples of seed kernels. Besides requiring large quantities of data, deep neural networks require the input to be a certain size. However, not all available data are in the size required by the deep neural networks. The proposed image cropper helps to resize images without resulting in any distortion, thereby, making image data fit for consumption. The third contribution is the design and analysis of supervised and self-supervised neural network architectures trained on synthetic images to perform the tasks of seed kernel classification, counting and morphometry. In the area of supervised image classification, state-of-the-art neural network models of VGG-16, VGG-19 and ResNet-101 are investigated. A Simple framework for Contrastive Learning of visual Representations (SimCLR) [137], Momentum Contrast (MoCo) [55] and Bootstrap Your Own Latent (BYOL) [123] are leveraged for self-supervised image classification. The instance-based segmentation deep neural network models of Mask R-CNN and YOLO are utilized to perform the tasks of seed kernel classification, segmentation and counting. The results demonstrate the feasibility of deep neural networks for their respective tasks of classification and instance segmentation. In addition to estimating seed kernel count from static images, algorithms that aid in seed kernel counting from videos are proposed and analyzed. Proposed is an algorithm that creates a slit image which can be analyzed to estimate seed count. Upon the creation of the slit image, the video is no longer required to estimate seed count, thereby, significantly lowering the computational resources required for the estimation. The fourth contribution is the development of an end-to-end, automated image capture system for single seed kernel analysis. In addition to estimating length and width from 2-D images, the proposed system estimates the volume of a seed kernel from 2-D images using the technique of volume sculpting. The relative standard deviation of the results produced by the proposed technique is lower (better) than the relative standard deviation of the results produced by volumetric estimation using the ellipsoid slicing technique. The fifth contribution is the development of image processing algorithms to provide feature enhancements to mobile applications to improve upon on-site phenotyping capabilities. Algorithms for two features of high value namely, leaf angle estimation and fractional plant cover estimation are developed. The leaf angle estimation feature estimates the angle between stem and leaf for images captured using mobile phone cameras whereas fractional plant cover is to determine companion plants i.e., plants that are able to co-exist and mutually benefit. The proposed techniques, frameworks and findings lay a solid foundation for future Computer Vision and Scientific Computing research in the domain of agriculture. The contributions are significant since the dissertation not only proposes techniques, but also develops low-cost end-to-end frameworks to leverage the proposed techniques in a scalable fashion

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Development and evaluation of a smartphone-based system for inspection of road maintenance work

Abstract. In the road construction industry, doing work inspection is a laborious and resource-consuming job because of the distributed work site. Contractors in Finland require to capture photos of every road fix they have done as proof of their work. It is well-established that with the help of smartphone technology, these kinds of manual work can be reduced. This thesis aims to develop and evaluate a smartphone-based system to capture video evidence of task completion. The system, designed and developed in this thesis, consists of an Android application named ’Road Recorder’ and a web tool for managing the content collected by Road Recorder. While mounted to a vehicle’s dashboard used in construction work, the Road Recorder can record the videos of road surface and geo-location information and some other metadata and send them to a remote server that is inspected using the web tool. Users of different backgrounds were given the system to accomplish some tasks and were observed closely. The users were interviewed at the end, and responses were analyzed to find the usability of the applications. The results indicate the high usability of the Road Recorder application and reveal possible improvements for the Road Recorder management web application. Overall, Road Recorder is a great step towards the automation of such construction work inspection. Though there were some limitations in the evaluation process, it demonstrates that Road Recorder is easy to use and can be a useful tool in the industry

Autocalibrating vision guided navigation of unmanned air vehicles via tactical monocular cameras in GPS denied environments

This thesis presents a novel robotic navigation strategy by using a conventional tactical monocular camera, proving the feasibility of using a monocular camera as the sole proximity sensing, object avoidance, mapping, and path-planning mechanism to fly and navigate small to medium scale unmanned rotary-wing aircraft in an autonomous manner. The range measurement strategy is scalable, self-calibrating, indoor-outdoor capable, and has been biologically inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals (particularly bats), designed to assume operations in previously unknown, GPS-denied environments. It proposes novel electronics, aircraft, aircraft systems, systems, and procedures and algorithms that come together to form airborne systems which measure absolute ranges from a monocular camera via passive photometry, mimicking that of a human-pilot like judgement. The research is intended to bridge the gap between practical GPS coverage and precision localization and mapping problem in a small aircraft. In the context of this study, several robotic platforms, airborne and ground alike, have been developed, some of which have been integrated in real-life field trials, for experimental validation. Albeit the emphasis on miniature robotic aircraft this research has been tested and found compatible with tactical vests and helmets, and it can be used to augment the reliability of many other types of proximity sensors

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections