A new path planning approach based on artificial electric potential energy

Path planning is one of the most fundamental desired autonomous navigation capabilities for aircrafts. A sensible environment modeling method plays a significant role in improving the path planning algorithm, and the electric potential principle has a unique advantage in this regard. Due to the random node generation of the sampling-based algorithm, it is difficult to generate an optimum path. In the integration of electric potential cost function and probability function, the calculation has approved that there is a negative correlation between the path cost value and probability value, that is, the lower the cost value, the higher the probability that the path nodes is to be selected. Meanwhile, the electric potential value of the entire path is also used to evaluate the safety of an entire route. The simulation results depict that, compared with other traditional methods, the algorithm proposed in this article has distinctive superiority in raising and enhancing computational efficiency and path safety

Implementation of a local path planning algorithm for unmanned aerial vehicles

As the presence of Unmanned Aircraft Systems (UASs) become more prominent today and in the future. They are used in a variety of ways to solve solutions for a variety of tasks. UASs that are battery-powered typically have a flight time of no more than 30 minutes. Some tasks make take the drone beyond visual line of sight (BVLOS). The approach taken within this paper is allocating a secondary flight computer onboard the UAS to calculate paths while the primary computer controls the aircraft and follows the path being generated. With a proper map of the environment and use of a path planning algorithm the safety of the aircraft can be increased in missions that are BVLOS. This thesis will cover the concepts of path planning algorithms and the development of a modified version of a popular path planning algorithm. Show simulations of comparison with other variations of path planning algorithms and software in the loop (SITL) simulations on a fixed-wing aircraft. It will also show this algorithm's results when implemented in flight tests onboard a fixed-wing and multi-rotor UAS

Cooperation of unmanned systems for agricultural applications: A theoretical framework

Agriculture 4.0 comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management with the objective of optimising production by accounting for variabilities and uncertainties within agricultural systems. Autonomous ground and aerial vehicles can lead to favourable improvements in management by performing in-field tasks in a time-effective way. In particular, greater benefits can be achieved by allowing cooperation and collaborative action among unmanned vehicles, both aerial and ground, to perform in-field operations in precise and time-effective ways. In this work, the preliminary and crucial step of analysing and understanding the technical and methodological challenges concerning the main problems involved is performed. An overview of the agricultural scenarios that can benefit from using collaborative machines and the corresponding cooperative schemes typically adopted in this framework are presented. A collection of kinematic and dynamic models for different categories of autonomous aerial and ground vehicles is provided, which represents a crucial step in understanding the vehicles behaviour when full autonomy is desired. Last, a collection of the state-of-the-art technologies for the autonomous guidance of drones is provided, summarising their peculiar characteristics, and highlighting their advantages and shortcomings with a specific focus on the Agriculture 4.0 framework. A companion paper reports the application of some of these techniques in a complete case study in sloped vineyards, applying the proposed multi-phase collaborative scheme introduced here

Gaussian Processes for Machine Learning in Robotics

Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as perception, control, planning, and decision making. Machine learning involves learning, reasoning, and acting based on the data. This is achieved by constructing computer programs that process the data, extract useful information or features, make predictions to infer unknown properties, and suggest actions to take or decisions to make. This computer program corresponds to a mathematical model of the data that describes the relationship between the variables that represent the observed data and properties of interest. The aforementioned model is learned based on the available training data, which is accomplished using a learning algorithm capable of automatically adjusting the parameters of the model to agree with the data. Therefore, the architecture of the model needs to be selected accordingly, which is not a trivial task and usually depends on the machine-learning engineer’s insights and past experience. The number of parameters to be tuned varies significantly with the selected machine learning model, ranging from two or three parameters for Gaussian processes (GP) to hundreds of thousands for artificial neural networks. However, as more complex and novel robotic applications emerge, data complexity increases and prior experience may be insufficient to define adequate mathematical models. In addition, traditional machine learning methods are prone to problems such as overfitting, which can lead to inaccurate predictions and catastrophic failures in critical applications. These methods provide probabilistic distributions as model outputs, allowing for estimating the uncertainty associated with predictions and making more informed decisions. That is, they provide a mean and variance for the model responses. This thesis focuses on the application of machine learning solutions based on Gaussian processes to various problems in robotics, with the aim of improving current methods and providing a new perspective. Key areas such as trajectory planning for unmanned aerial vehicles (UAVs), motion planning for robotic manipulators and model identification of nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that allow for more efficient planning and energy savings in exploration missions have been developed. These algorithms are compared with traditional analytical approaches, demonstrating their superiority in terms of efficiency when using machine learning. Area coverage and linear coverage algorithms with UAV formations are presented, as well as a sea surface search algorithm. Finally, these algorithms are compared with a new method that uses Gaussian processes to perform probabilistic predictions and optimise trajectory planning, resulting in improved performance and reduced energy consumption. Regarding motion planning for robotic manipulators, an approach based on Gaussian process models that provides a significant reduction in computational times is proposed. A Gaussian process model is used to approximate the configuration space of a robot, which provides valuable information to avoid collisions and improve safety in dynamic environments. This approach is compared to conventional collision checking methods and its effectiveness in terms of computational time and accuracy is demonstrated. In this application, the variance provides information about dangerous zones for the manipulator. In terms of creating models of non-linear systems, Gaussian processes also offer significant advantages. This approach is applied to a soft robotic arm system and UAV energy consumption models, where experimental data is used to train Gaussian process models that capture the relationships between system inputs and outputs. The results show accurate identification of system parameters and the ability to make reliable future predictions. In summary, this thesis presents a variety of applications of Gaussian processes in robotics, from trajectory and motion planning to model identification. These machine learning-based solutions provide probabilistic predictions and improve the ability of robots to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan las relaciones entre las variables que representan los datos observados y las propiedades que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización que ajustan automáticamente los parámetros para lograr un rendimiento óptimo. Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas, la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas como salidas del modelo, lo que permite estimar la incertidumbre asociada a las predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una varianza para las respuestas del modelo. Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación de movimientos para manipuladores robóticos y la identificación de modelos de sistemas no lineales. En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción del consumo de energía. En cuanto a la planificación de movimientos para manipuladores robóticos, se propone un enfoque basado en modelos gaussianos que permite una reducción significativa en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar el espacio de configuraciones de un robot, lo que proporciona información valiosa para evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara con los métodos convencionales de planificación de movimientos y se demuestra su eficacia en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación, la varianza proporciona información sobre zonas peligrosas para el manipulador. En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones entre las entradas y las salidas del sistema. Los resultados muestran una identificación precisa de los parámetros del sistema y la capacidad de realizar predicciones futuras confiables. En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos en robótica, desde la planificación de trayectorias y movimientos hasta la identificación de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

[EN] Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.The authors would like to acknowledge the Spanish Ministerio de Ciencia, Innovacion y Universidades for providing funding through the project RTI2018-096904-B-I00 and the local administration Generalitat Valenciana through projects GV/2017/029 and AICO/2019/055. 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A Framework for Offline Risk-aware Planning of Low-altitude Aerial Flights during Urban Disaster Response

Disaster response missions are dynamic and dangerous events for first responders. Active situational awareness is critical for effective decision-making, and unmanned aerial assets have successfully extended the range and output of sensors. Aerial assets have demonstrated their capability in disaster response missions via decentralized operations. However, literature and industry lack a systematic investigation of the algorithms, datasets, and tools for aerial system trajectory planning in urban disasters that optimizes mission performance and guarantee mission success. This work seeks to develop a framework and software environment to investigate the requirements for offline planning algorithms and flight risk models when applied to aerial assets exploring urban disaster zones. This is addressed through the creation of rapid urban maps, efficient flight planning algorithms, and formal risk metrics that are demonstrated in scenario-driven experiments using Monte Carlo simulation. First, rapid urban mapping strategies are independently compared for efficient processing and storage through obstacle and terrain layers. Open-source data is used when available and is supplemented with an urban feature prediction model trained on satellite imagery using deep learning. Second, sampling-based planners are evaluated for efficient and effective trajectory planning of nonlinear aerial dynamic systems. The algorithm can find collision-free, kinodynamic feasible trajectories using random open-loop control targets. Alternative open-loop control commands are formed to improve the planning algorithm’s speed and convergence. Third, a risk-aware implementation of the planning algorithm is developed that considers the uncertainty of energy, collisions, and onboard viewpoint data and maps them to a single measure of the likelihood of mission failure. The three modules are combined in a framework where the rapid urban maps and risk-aware planner are evaluated against benchmarks for mission success, performance, and speed while creating a unique set of benchmarks from open-source data and software. One, the rapid urban map module generates a 3D structure and terrain map within 20 meters of data and in less than 5 minutes. The Gaussian Process terrain model performs better than B-spline and NURBS models in small-scale, mountainous environments at 10-meter squared resolution. Supplementary data for structures and other urban landcover features is predicted using the Pix2Pix Generative Adversarial Network with a 3-channel encoding for nine labels. Structures, greenspaces, water, and roads are predicted with high accuracy according to the F1, OIU, and pixel accuracy metrics. Two, the sampling-based planning algorithm is selected for forming collision-free, 3D offline flight paths with a black-box dynamics model of a quadcopter. Sampling-based planners prove successful for efficient and optimal flight paths through randomly generated and rapid urban maps, even under wind and noise uncertainty. The Stable-Sparse-RRT, SST, algorithm is shown to improve trajectories for minimum Euclidean distance more consistently and efficiently than the RRT algorithm, with a 50% improvement in finite-time path convergence for large-scale urban maps. The forward propagation dynamics of the black-box model are replaced with 5-15 times more computationally efficient motion primitives that are generated using an inverse lower-order dynamics model and the Differential Dynamic Programming, DDP, algorithm. Third, the risk-aware planning algorithm is developed that generates optimal paths based on three risk metrics of energy, collision, and viewpoint risk and quantifies the likelihood of worst-case events using the Conditional-Value-at-Risk, CVaR, metric. The sampling-based planning algorithm is improved with informative paths, and three versions of the algorithm are compared for the best performance in different scenarios. Energy risk in the planning algorithm results in 5-35% energy reduction and 20-30% more consistency in finite-time convergence for flight paths in large-scale urban maps. All three risk metrics in the planning algorithm generally result in more energy use than the planner with only energy risk, but reduce the mean flight path risk by 10-50% depending on the environment, energy available, and viewpoint landmarks. A final experiment in an Atlanta flooding scenario demonstrates the framework’s full capability with the rapid urban map displaying essential features and the trajectory planner reporting flight time, energy consumption, and total risk. Furthermore, the simulation environment provides insight into offline planning limitations through Monte Carlo simulations with environment wind and system dynamics noise. The framework and software environment are made available to use as benchmarks in the field to serve as a foundation for increasing the effectiveness of first responders’ safety in the challenging task of urban disaster response.Ph.D

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