UAV Optimal Cooperative Obstacle Avoidance and Target Tracking in Dynamic Stochastic Environments

Cette thèse propose une stratégie de contrôle avancée pour guider une flotte d'aéronefs sans pilote (UAV) dans un environnement à la fois stochastique et dynamique. Pour ce faire, un simulateur de vol 3D a été développé avec MATLAB® pour tester les algorithmes de la stratégie de guidage en fonctions de différents scénarios. L'objectif des missions simulées est de s'assurer que chaque UAV intercepte une cible ellipsoïdale mobile tout en évitant une panoplie d'obstacles ellipsoïdaux mobiles détectés en route. Les UAVs situés à l'intérieur des limites de communication peuvent coopérer afin d'améliorer leurs performances au cours de la mission. Le simulateur a été conçu de façon à ce que les UAV soient dotés de capteurs et d'appareils de communication de portée limitée. De plus, chaque UAV possède un pilote automatique qui stabilise l'aéronef en vol et un planificateur de trajectoires qui génère les commandes à envoyer au pilote automatique. Au coeur du planificateur de trajectoires se trouve un contrôleur prédictif à horizon fuyant qui détermine les commandes à envoyer à l'UAV. Ces commandes optimisent un critère de performance assujetti à des contraintes. Le critère de performance est conçu de sorte que les UAV atteignent les objectifs de la mission, alors que les contraintes assurent que les commandes générées adhèrent aux limites de manoeuvrabilité de l'aéronef. La planification de trajectoires pour UAV opérant dans un environnement dynamique et stochastique dépend fortement des déplacements anticipés des objets (obstacle, cible). Un filtre de Kalman étendu est donc utilisé pour prédire les trajectoires les plus probables des objets à partir de leurs états estimés. Des stratégies de poursuite et d'évitement ont aussi été développées en fonction des trajectoires prédites des objets détectés. Pour des raisons de sécurité, la conception de stratégies d'évitement de collision à la fois efficaces et robustes est primordiale au guidage d'UAV. Une nouvelle stratégie d'évitement d'obstacles par approche probabiliste a donc été développée. La méthode cherche à minimiser la probabilité de collision entre l'UAV et tous ses obstacles détectés sur l'horizon de prédiction, tout en s'assurant que, à chaque pas de prédiction, la probabilité de collision entre l'UAV et chacun de ses obstacles détectés ne surpasse pas un seuil prescrit. Des simulations sont présentées au cours de cette thèse pour démontrer l'efficacité des algorithmes proposés

UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether

This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a flying sensor but also as a tether attachment device. Two robots are connected with a tether, allowing the UAV to anchor the tether to a structure located at the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the poor traversability of the UGV by not only providing a wider range of scanning and mapping from the air, but also by allowing the UGV to climb steep terrains with the winding of the tether. In addition, we present an autonomous framework for the collaborative navigation and tether attachment in an unknown environment. The UAV employs visual inertial navigation with 3D voxel mapping and obstacle avoidance planning. The UGV makes use of the voxel map and generates an elevation map to execute path planning based on a traversability analysis. Furthermore, we compared the pros and cons of possible methods for the tether anchoring from multiple points of view. To increase the probability of successful anchoring, we evaluated the anchoring strategy with an experiment. Finally, the feasibility and capability of our proposed system were demonstrated by an autonomous mission experiment in the field with an obstacle and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1

In-Time UAV Flight-Trajectory Estimation and Tracking Using Bayesian Filters

Rapid increase of UAV operation in the next decade in areas of on-demand delivery, medical transportation services, law enforcement, traffic surveillance and several others pose potential risks to the low altitude airspace above densely populated areas. Safety assessment of airspace demands the need for a novel UAV traffic management (UTM) framework for regulation and tracking of the vehicles. Particularly for low-altitude UAV operations, quality of GPS measurements feeding into the UAV is often compromised by loss of communication link caused by presence of trees or tall buildings in proximity to the UAV flight path. Inaccurate GPS locations may yield to unreliable monitoring and inaccurate prognosis of remaining battery life and other safety metrics which rely on future expected trajectory of the UAV. This work therefore proposes a generalized monitoring and prediction methodology for autonomous UAVs using in-time GPS measurements. Firstly, a typical 4D smooth trajectory generation technique from a series of waypoint locations with associated expected times-of-arrival based on B-spline curves is presented. Initial uncertainty in the vehicle's expected cruise velocity is quantified to compute confidence intervals along the entire flight trajectory using error interval propagation approach. Further, the generated planned trajectory is considered as the prior knowledge which is updated during its flight with incoming GPS measurements in order to estimate its current location and corresponding kinematic profiles. Estimation of position is denoted in dicrete state-space representation such that position at a future time step is derived from position and velocity at current time step and expected velocity at the future time step. A linear Bayesian filtering algorithm is employed to efficiently refine position estimation from noisy GPS measurements and update the confidence intervals. Further, a dynamic re-planning strategy is implemented to incorporate unexpected detour or delay scenarios. Finally, critical challenges related to uncertainty quantification in trajectory prognosis for autonomous vehicles are identified, and potential solutions are discussed at the end of the paper. The entire monitoring framework is demonstrated on real UAV flight experiments conducted at the NASA Langley Research Center

On-board Obstacle Avoidance in the Teleoperation of Unmanned Aerial Vehicles

Teleoperation von Drohnen in Umgebungen ohne GPS-Verbindung und wenig Bewegungsspielraum stellt den Operator vor besondere Herausforderungen. Hindernisse in einer unbekannten Umgebung erfordern eine zuverlässige Zustandsschätzung und Algorithmen zur Vermeidung von Kollisionen. In dieser Dissertation präsentieren wir ein System zur kollisionsfreien Navigation einer ferngesteuerten Drohne mit vier Propellern (Quadcopter) in abgeschlossenen Räumen. Die Plattform ist mit einem Miniaturcomputer und dem Minimum an Sensoren ausgestattet. Diese Ausstattung genügt den Anforderungen an die Rechenleistung. Dieses Setup ermöglicht des Weiteren eine hochgenaue Zustandsschätzung mit Hilfe einer Kaskaden-Architektur, sehr gutes Folgeverhalten bezüglich der kommandierten Geschwindigkeit, sowie eine kollisionsfreie Navigation. Ein Komplementärfilter berechnet die Höhe der Drohne, während ein Kalman-Filter Beschleunigung durch eine IMU und Messungen eines Optical-Flow Sensors fusioniert und in die Softwarearchitektur integriert. Eine RGB-D Kamera stellt dem Operator ein visuelles Feedback, sowie Distanzmessungen zur Verfügung, um ein Roboter-zentriertes Modell umliegender Hindernisse mit Hilfe eines Bin-Occupancy-Filters zu erstellen. Der Algorithmus speichert die Position dieser Hindernisse, auch wenn sie das Sehfeld des Sensors verlassen, mit Hilfe des geschätzten Zustandes des Roboters. Das Prinzip des Ausweich-Algorithmus basiert auf dem Ansatz einer modell-prädiktiven Regelung. Durch Vorhersage der wahrscheinlichen Position eines Hindernisses werden die durch den Operator kommandierten Sollwerte gefiltert, um eine mögliche Kollision mit einem Hindernis zu vermeiden. Die Plattform wurde experimentell sowohl in einer räumlich abgeschlossenen Umgebung mit zahlreichen Hindernissen als auch bei Testflügen in offener Umgebung mit natürlichen Hindernissen wie z.B. Bäume getestet. Fliegende Roboter bergen das Risiko, im Fall eines Fehlers, sei es ein Bedienungs- oder Berechnungsfehler, durch einen Aufprall am Boden oder an Hindernissen Schaden zu nehmen. Aus diesem Grund nimmt die Entwicklung von Algorithmen dieser Roboter ein hohes Maß an Zeit und Ressourcen in Anspruch. In dieser Arbeit präsentieren wir zwei Methoden (Software-in-the-loop- und Hardware-in-the-loop-Simulation) um den Entwicklungsprozess zu vereinfachen. Via Software-in-the-loop-Simulation konnte der Zustandsschätzer mit Hilfe simulierter Sensoren und zuvor aufgenommener Datensätze verbessert werden. Eine Hardware-in-the-loop Simulation ermöglichte uns, den Roboter in Gazebo (ein bekannter frei verfügbarer ROS-Simulator) mit zusätzlicher auf dem Roboter installierter Hardware in Simulation zu bewegen. Ebenso können wir damit die Echtzeitfähigkeit der Algorithmen direkt auf der Hardware validieren und verifizieren. Zu guter Letzt analysierten wir den Einfluss der Roboterbewegung auf das visuelle Feedback des Operators. Obwohl einige Drohnen die Möglichkeit einer mechanischen Stabilisierung der Kamera besitzen, können unsere Drohnen aufgrund von Gewichtsbeschränkungen nicht auf diese Unterstützung zurückgreifen. Eine Fixierung der Kamera verursacht, während der Roboter sich bewegt, oft unstetige Bewegungen des Bildes und beeinträchtigt damit negativ die Manövrierbarkeit des Roboters. Viele wissenschaftliche Arbeiten beschäftigen sich mit der Lösung dieses Problems durch Feature-Tracking. Damit kann die Bewegung der Kamera rekonstruiert und das Videosignal stabilisiert werden. Wir zeigen, dass diese Methode stark vereinfacht werden kann, durch die Verwendung der Roboter-internen IMU. Unsere Ergebnisse belegen, dass unser Algorithmus das Kamerabild erfolgreich stabilisieren und der rechnerische Aufwand deutlich reduziert werden kann. Ebenso präsentieren wir ein neues Design eines Quadcopters, um dessen Ausrichtung von der lateralen Bewegung zu entkoppeln. Unser Konzept erlaubt die Neigung der Propellerblätter unabhängig von der Ausrichtung des Roboters mit Hilfe zweier zusätzlicher Aktuatoren. Nachdem wir das dynamische Modell dieses Systems hergeleitet haben, synthetisierten wir einen auf Feedback-Linearisierung basierten Regler. Simulationen bestätigen unsere Überlegungen und heben die Verbesserung der Manövrierfähigkeit dieses neuartigen Designs hervor.The teleoperation of unmanned aerial vehicles (UAVs), especially in cramped, GPS-restricted, environments, poses many challenges. The presence of obstacles in an unfamiliar environment requires reliable state estimation and active algorithms to prevent collisions. In this dissertation, we present a collision-free indoor navigation system for a teleoperated quadrotor UAV. The platform is equipped with an on-board miniature computer and a minimal set of sensors for this task and is self-sufficient with respect to external tracking systems and computation. The platform is capable of highly accurate state-estimation, tracking of the velocity commanded by the user and collision-free navigation. The robot estimates its state in a cascade architecture. The attitude of the platform is calculated with a complementary filter and its linear velocity through a Kalman filter integration of inertial and optical flow measurements. An RGB-D camera serves the purpose of providing visual feedback to the operator and depth measurements to build a probabilistic, robot-centric obstacle state with a bin-occupancy filter. The algorithm tracks the obstacles when they leave the field of view of the sensor by updating their positions with the estimate of the robot's motion. The avoidance part of our navigation system is based on the Model Predictive Control approach. By predicting the possible future obstacles states, the UAV filters the operator commands by altering them to prevent collisions. Experiments in obstacle-rich indoor and outdoor environments validate the efficiency of the proposed setup. Flying robots are highly prone to damage in cases of control errors, as these most likely will cause them to fall to the ground. Therefore, the development of algorithm for UAVs entails considerable amount of time and resources. In this dissertation we present two simulation methods, i.e. software- and hardware-in-the-loop simulations, to facilitate this process. The software-in-the-loop testing was used for the development and tuning of the state estimator for our robot using both the simulated sensors and pre-recorded datasets of sensor measurements, e.g., from real robotic experiments. With hardware-in-the-loop simulations, we are able to command the robot simulated in Gazebo, a popular open source ROS-enabled physical simulator, using computational units that are embedded on our quadrotor UAVs. Hence, we can test in simulation not only the correct execution of algorithms, but also the computational feasibility directly on the robot's hardware. Lastly, we analyze the influence of the robot's motion on the visual feedback provided to the operator. While some UAVs have the capacity to carry mechanically stabilized camera equipment, weight limits or other problems may make mechanical stabilization impractical. With a fixed camera, the video stream is often unsteady due to the multirotor's movement and can impair the operator's situation awareness. There has been significant research on how to stabilize videos using feature tracking to determine camera movement, which in turn is used to manipulate frames and stabilize the camera stream. However, we believe that this process could be greatly simplified by using data from a UAV’s on-board inertial measurement unit to stabilize the camera feed. Our results show that our algorithm successfully stabilizes the camera stream with the added benefit of requiring less computational power. We also propose a novel quadrotor design concept to decouple its orientation from the lateral motion of the quadrotor. In our design the tilt angles of the propellers with respect to the quadrotor body are being simultaneously controlled with two additional actuators by employing the parallelogram principle. After deriving the dynamic model of this design, we propose a controller for this platform based on feedback linearization. Simulation results confirm our theoretical findings, highlighting the improved motion capabilities of this novel design with respect to standard quadrotors

Location prediction and trajectory optimization in multi-UAV application missions

Unmanned aerial vehicles (a.k.a. drones) have a wide range of applications in e.g., aerial surveillance, mapping, imaging, monitoring, maritime operations, parcel delivery, and disaster response management. Their operations require reliable networking environments and location-based services in air-to-air links with cooperative drones, or air-to-ground links in concert with ground control stations. When equipped with high-resolution video cameras or sensors to gain environmental situation awareness through object detection/tracking, precise location predictions of individual or groups of drones at any instant possible is critical for continuous guidance. The location predictions then can be used in trajectory optimization for achieving efficient operations (i.e., through effective resource utilization in terms of energy or network bandwidth consumption) and safe operations (i.e., through avoidance of obstacles or sudden landing) within application missions. In this thesis, we explain a diverse set of techniques involved in drone location prediction, position and velocity estimation and trajectory optimization involving: (i) Kalman Filtering techniques, and (ii) Machine Learning models such as reinforcement learning and deep-reinforcement learning. These techniques facilitate the drones to follow intelligent paths and establish optimal trajectories while carrying out successful application missions under given resource and network constraints. We detail the techniques using two scenarios. The first scenario involves location prediction based intelligent packet transfer between drones in a disaster response scenario using the various Kalman Filtering techniques. The second scenario involves a learning-based trajectory optimization that uses various reinforcement learning models for maintaining high video resolution and effective network performance in a civil application scenario such as aerial monitoring of persons/objects. We conclude with a list of open challenges and future works for intelligent path planning of drones using location prediction and trajectory optimization techniques.Includes bibliographical references

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

Development Of Inertial Navigation System With Applications To Airborne Collision Avoidance

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2016Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2016Ülkemizde ve dünyada insansız hava araçlarının kullanımı gün geçtikçe artmaktadır. Sadece insansız hava aracı kullanımı değil, kullanıldığı alanlar da artış göstermektedir. Bu da insansız hava aracı marketini daha cezbedici kılmaktadır. Bu artış sonucu dünyada bir çok insansız hava aracı şirketi kurulmuş ve bazıları bu araçları seri üretim şeklinde üreterek ihtacat yapabilmektedirler. Dünyadaki bu ekonomik büyümenin bir yansıması olarak dünyadaki insansız hava aracının sayısı da gün geçtikçe artmaktadır. Bu talebin büyüklüğüne bakılarak, 20 yıl sonra meydana gelecek insansız hava aracı çarpışmaları ve trafikleri otoriteleri bu konu ile ilgili çalışmaya sevketmiştir. Bununla beraber uygulama alanlarının artması ve daha da detaylanması nedeniyle belirli özellikleri ve otonom uçuşu gerçekleştirebilen insansız hava araçları artık yetersiz kalmaktadır. Günümüzde genel olarak DJI, Pixhawk, ardupilot gibi markaların araçları veya otopilotları kullanılmaktadır. Bazıları açık kaynak kodlu olsalar bile kod içerisinde değişiklik yapmak veya farklı bir donanım entegre etmek oldukça zor. Bunun haricinde piyasada baskın olup market değeri de en yüksek olan DJI firmasının ürünleri tamamiyle kapalı kutu şekilde satılmaktadır. Otonom uçui, rota takibi, havada asılı kalma ve video çekme, canlı yayın yapma gibi temel isterleri yapabilmelerine ragmen, genişleyen sektörde endüstrinin istekleri, artık insansız hava aracının sadece canlı yayın yapması için değil, harici eklenecek donanımlar ile beraber çalışabilirliği veya başka sistemlerle entegre çalışabilirliği gibi problemleri ortaya çıkarmıştır. Bu nedenle piyasada ciddi bir şekilde müşteri isteğine göre configure edilebilen otopilot sistemleri ihtiyacı doğmuştur. Diğer yandan insansız hava aracı trafiğine bile yol açacak kadar büyüyen bu sektör ve sivil havacılığın da benzer bir şekilde büyüdüğü iki sektör ile karşı karşıyayız. Sivil havacılığın artan trafiği ve çarpışma önleme sistemlerinin yetersiz kalması gibi durumlara çözümler aranmaktadır. Yapılan çalışmalar sonucu [1] insansız hava aracı sahası ile sivil havacılık sahasının birleştirilmesi ve bu birleştirmelerin nasıl yapılması gerektiği konusu ortaya çıkmıştır. Bunun üzerine bir çok üniversite, bu konu üzerine çalışmalar yapmış ve yayınlar ortaya çıkmıştır. Genel olarak problem ise elbette eski teknolojinin hüküm sürdüğü sivil havacılıkta kullanılan ürünlerin, insansız hava araçlarına entegrasyonu imkansızdır. Doğal olarak tüm hava araçlarının kontrolü için tek bir iletişim ağı hepsini kapsayacak şekilde kurulması amaçlanmıştır. Tüm bu hava araçlarının gözlemlenmesi aynı anda yapılabilmeli ki tehlike durumlarında gerekli müdahaleler ve tedbirler önceden veya o an alınabilsin. Bu tezde iki farklı problemin çözümü önerilmiştir. Önerilerin ilki bahsedilen müşteri odaklı insansız hava aracının tasarlanmasıdır. İnsansız hava aracı tasarımındaki en önemli modüllerden biri de INS-AHRS sistemidir. İstanbul Teknik Üniversitesi Kontrol ve Aviyonik Laboratuvarında yapılan bu çalışma öncesinde, otopilot kontrolcü tasarımı çalışmaları yapılmış ve system oturtulmuştur. Yapılan uçuşlarda piyasadaki pahalı sistemler kullanılmaktaydı. Fakat sistemden sisteme farklılıklar göstermesi gereken bu ürünler, platform değişikliklerinde sıkıntılara yol açabiliyordu. Buna örnek vermek gerekirse sabit kanatlı insansız hava aracında sıkıntısız uçabilirken, multi-copter platformunda sapma açısında uçuş anında düzensizlikler ortaya çıkıyordu. Bunun nedeni ise alınan üründe sapma açısı sadee GPS verilerinden elde ediliyor olmasıydı. Hareketli platformun her zaman bir sapma açısı olacağından sabit kanatlı sistemlerde çalışması gayet normaldi. Fakat multi-copter platformunda havada asılı kaldığı zamanlarda sapma açısında bir hız vektörü olmadığından GPS hesaplayamıyor ve bu yüzden salınımlara neden oluyordu. Bu gibi problemlerin çözümü ve tamamiyle yerli, dışarıda çalışabilen, istenilen tüm platformlara tasarım değişiklikleriyle entegre edilebilecek bir INS-AHRS tasarımı yapılmaya çalışılmıştır. Bu tasarım yapılırken literatürde yapılan çalışmalar referans alınmış, ve filtreleme tekniklerinden navigasyon koordinat sistemlerine kadar çalışmalar yapılmıştır. Sensor çıkışlarının gürültülerini bastırmak için alçak geçiren filtrelerden geçirildikten sonra gerekli dönüşümler yapılarak filter seviyesine kadar getirilmiştir. Filtre kısmında iki farklı filter testi yapılmıştır. Biri tamamlayıcı filter ve diğeri kalman filtresidir. Bu filtrelerin her bir INS-AHRS üzerinde testleri yapılmış ve nihai olarak AHRS’de tamamlayıcı filter, INS’de ise kalman filtresinin kullanımı kararlaştırılmıştır. Yapılan çalışmalar İstanbul Teknik Üniversitesi Stadyumunda ve İstanbul Teknik Üniversitesi Havacılık Araştırma Merkezinde test edilmiştir. Yapılan testler 6 aydan fazla sürmesine ragmen nihai sonuca ulaşılabilmiştir. Bu süre zarfında tecrübe edilen en önemli nokta ise gerçek hayatta karşılaşılan problemler ile simulasyon ortamının farklı olmasıdır. Gerçek hayatta en küçük problemde bile aracınız yere çakılabilir ve her çakılmada 200-1000 TL zarar alabilirsiniz. Test yaptığımız süre içerisinde bizden kaynaklı olmayan, fakat üretim hatası olan pervanelerin kopması nedeni ile de kırımlar yaşanmıştır. Bu nedenle sistemin argesinin yapılması pahalıya mal olmuştur. Yapılan test sonuçlarının videoları çekilmiş ve sosyal mecralarda paylaşılmıştır. Bir diğer problem ise insansız hava araçlarının sivil hava sahasına entegrasonudur. Bu entegrasyonun yapılması için gereken teknolojik gelişmeler ve algoritmik çalışmalar gerekmektedir. Önerilen sistemde araç bazlı ve uçuş bazlı haberleşme verileri belirlenip, hangi sistemler üzerinden bu haberleşmenin gereçekleşmesi gerektiği gösterilmiştir. Daha sonra tüm bu sistemler hem hava araçlarında, yer istasyonlarında ve hava trafik kontrolcülerinde olacağından tüm haberleşme ortak bir platform için toplanmış oldu. Bu nedenle uçuş kontrollerinin yapılması daha da kolaylaşacaktır. Bununla beraber çarpışma önleme sistemi için günümüzde kullanılan 2B system değil, zamanın da içine dahil olduğu 4B istem önerilmiştir. Bu algoritmaının adı RRT-Star olup, olasılıksal yaklaşarak çarpışmadan kaçmayı hedefler. Bu kaçışı hedeflerken de en optimal yolu bulmaya çalışır ve o yoldan rotasına devam eder. Olasılıksal yaklaşımların savunduğu argüman sonsuz sayıda örnek sayısında bulunacak yol limitte en optimal yola doğru gider. Bu nedenle olasılıksal çözüm bulma, deterministic yöntemlere göre çok daha hızlı olmaktadır. Fakat algoritmada optimale ne kadar yaklaşmak istenirse o kadar örnekleme sayısını arttırmak gerekmektedir. Bu artış daha çok araştırma yapması ve sistemin uzun zaman boyunca rota üretmesi demektir. Buradaki dengeyi iyi tutturarak hem uygun yolu bulmaya ve en uygun kısa sürede bulmayı amaçlanması istenmektedir. Sistemin testi için donanımla benzetim çalışması gerçekleştirilmiştir. Bu tezde donanım benzetimi öncesi otopilot şeması verilmiş, buna bağlı test düzeneklerinin sistemi gösterilmiştir. Simulasyon olarak XPLANE programı kullanılmış ve programdan gelen sensor verilerine göre donanım sistemi uçurmaya çalışmıştır. Daha sonra çarpışma önleme algoritmasının entegrasyonu ile system testleri gerçekeştirilmiş ve sonuçları paylaşılmıştır. Nihai olarak bu tez, insansız hava aracı sektöründeki günümüzde ve gelecekte meydana gelecek problemleri öngörüp bunlara çözüm bulmak amaçlanmıştır. INS-AHRS tasarımları gerçekleştirilip, gerçek ortamda dışarıda testleri gerçekleştirilmiştir. Çarpışma önleme algoritması üzerine çalışmalar yapılarak da bu sistemin entegrasyonu yapılmış ve donanımsal benzetim ile testleri gerçekleştirilmiştir.Last years, the market growth of UAV is increasing day by day. This market growth is not just for some typical applications, but also application areas are increasing, too. This demand also increases the market value of the UAV. For competition in the market, UAV companies try to develop UAVs more efficient, cost effective and adding different capabilities. However, this growth generates some dangerous situations, moreover, because of the growth in application area, common UAVs are become not enough for applications or missions. In this thesis, I present and demostrate INS-AHRS Design and also Flight Management System with Collision Avoidance for UAV. These algorithms and demonstrations are made by the funding of ITU Control and Avionics Laboratory. In Laboratory, we already have autopilot system for multi-copter platforms and fixed-wing platforms. Before development of this INS-AHRS, we used other products from industry. But these products do not let you manage all system. But with the growth on the UAV applications, in the world also even in our laboratory, many projects required to solve specific problems with UAV. Industry products are designed for just one specific platform which may not be work on another platform. That is the main reason of necessity to develop new INS-AHRS, which can be used for multi-copter platforms. To develop INS-AHRS, filtering techniques and other conversation equations are studied. In this study, it is decided to use one IMU and one GPS. But after encounter with different problems, external magnetometer is added to the system. Then, as datasheet recommended, scaling and also alignment and offset shifting is studied. Before developing the all system, for inner loop, controller all need is attitude and attitude rate feed back. So first, with complimentary filter, gyroscope and accelerometer filtering is developed and tried to test at outside. In simulation, decision of coefficient of complimentary filter is easy to find. But these coefficients do not work at the outside. This shows the most important challenge that simulation platform can never be the same with outside real flight. For INS design, inertial frame to NWU frame conversation is developed. Accelerometers gravity vector and Coriolis vector is removed. Gyroscope outputs are also converted to the NWU frame. At least, all sensor outputs become the type of navigation frame. Whenever all datas gathered are become the type of the same frame, kalman filter is designed for INS. AS a result of INS-AHRS design, after 6 months of testing with other industrial INS, final coefficient of both INS and AHRS is decided. After few more development, test videos are recorded. For the growth of the UAV problem, this thesis presents Flight Management System (FMS) with multi-level autonomy modes that meet the requirements of future flight operations for unmanned aerial systems (UAS). It is envisioned that the future of airspace will become highly heterogeneous and integrate non-standardized aerial systems. In that case, only ground systems will be able to predict future trajectories based on performance models (stored in huge parametric databases). Meanwhile, airborne systems are required to share information. The proposed FMS structure integrates new functionalities such as (1) formal intent and information exchange and collaboration in tactical planning utilizing air-to-air and air-to-ground data links and (2) decentralized, short-term collision detection and avoidance. The air-to-ground data link enables intent sharing and allows field operators (i.e., flight operators or air traffic controllers) to interpret, modify, or re-plan UAS flight intent. The onboard FMS persistently monitors the airspace, tracks potential collisions with the other aircraft and the terrain, and requests re-planning when it detects a possible issue. When an immediate response is needed, the onboard FMS generates a 3D evasive maneuver and executes it autonomously. Flight traffic information is obtained from ADS-B/In transponders and air-to-air data links. ADSB-In/Out implementations make the unmanned systems more visible to the systems in 3D. In addition, the air-to-air data links enable intent sharing between airborne systems and are traceable in four dimensions (i.e., space and time). The experimental FMS was deployed in quadrotor UASs and a ground station and GUI was designed to perform demonstrations and field experiments for the issues introduced in the paper.Yüksek LisansM.Sc

Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail