As the drone market expands, the corresponding standardization follows. Drone standardization can vary geographically based on the regulations and requirements of different areas. This study mainly focuses on the European Union Aviation Safety Agency (EASA) regulations and investigates Aerit’s role, as a drone operator in Sweden, within this standardization framework. In particular, Specific Operations and Risk Assessment (SORA) process, developed by EASA, is illustrated. The process covers a comprehensive range of factors related to drone operations to assess and manage risks. In addition to the drone design standardization process, the study looks into drone flight control systems at component-level redundancy and at system-level redundancy with a scientific grounding of dependability. An investigation of what a voting system looks like is then conducted for implementing a redundant flight control architecture. Furthermore, results from Software-In-The-Loop (SITL) implementation in this study show that the performance differs not much for the two flight control architectures (component-level and system-level). Thus, the decision of whether to use one flight controller or redundant flight controllers depends on the specific requirements and priorities of the drone application as well as the level of pre-flight testing. / Eftersom drönarmarknaden växer, följer motsvarande standardisering med. Standardisering av drönar kan variera geografiskt baserat på olika områdens lagar och krav. Denna studie fokuserar främst på Europeiska unionens byrå för luftfartssäkerhet (EASA) och undersöker Aerits roll som drönaroperatör i Sverige inom detta standardiseringsramverk. Särskilt beskrivs processen för Specifika Operationer och Riskbedömning (SORA), utvecklad av EASA. Denna process täcker ett omfattande utbud av faktorer relaterade till drönaroperationer för att bedöma och hantera risker. Utöver standardiseringsprocessen för drönardesign, granskar studien drönarflygkontrollsystem på komponentnivå för redundans och på systemnivå med en vetenskaplig grund för tillförlitlighet. En undersökning av hur ett röstningssystem ser ut genomförs sedan för att implementera en redundant flygkontrollarkitektur. Vidare visar resultaten från mjukvara-i-slingan (SITL) -implementeringen i denna studie att prestandan inte skiljer sig mycket mellan de två flygkontrollarkitekturerna (komponentnivå och systemnivå). Därför beror beslutet om att använda en flygkontroll eller redundanta flygkontroller på de specifika kraven och prioriteterna för drönapplikationen samt nivån av före-flygtestning.

Submitted by Maike Costa (maiksebas@gmail.com) on 2017-07-03T12:25:38Z No. of bitstreams: 1 arquivototal.pdf: 3177649 bytes, checksum: b09cbb264ec1b770f1aa0bb04cc0fb8f (MD5) / Made available in DSpace on 2017-07-03T12:25:39Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 3177649 bytes, checksum: b09cbb264ec1b770f1aa0bb04cc0fb8f (MD5) Previous issue date: 2017-04-04 / Systems using Unmanned Aerial Vehicles (UAV) are typical examples of cyber-physical systems. Designing such systems is not a trivial task because it brings the challenge of dealing with the uncertainty that is inherent to this type of system. Therefore, it is important the usage of appropriate tools for design that can ensure implementation of these systems with a certain level of confiability. Thus, the purpose of this work is to integrate two simulators via HLA in order to simulate and evaluate different flights strategies. For this, it is presented a simulation environment that can execute flight plans in order to evaluate different strategies in uncertain scenarios. The simulator was developed in Ptolemy and integrated with SITL/ArduPilot via HLA. The results show that with the use of the approach presented in this paper it is possible to obtain results closer to reality, thus more efficient flight strategies can be developed and evaluate. / Sistemas que utilizam Veículos Aéreos Não-Tripulados (VANT) são exemplos típicos de sistemas ciber-físicos. Projetar tais sistemas não é uma tarefa trivial porque traz consigo o desafio de lidar com a incerteza, que é algo inerente a este tipo de sistema. Por isso, é importante que o projeto seja feito com ferramentas apropriadas que possam viabilizar a execução desses sistemas com um certo nível de confiança para seus usuários. Deste modo, a proposta deste trabalho é unir dois simuladores, através do HLA, com o objetivo de simular e avaliar estratégias de voos mais próximas do ambiente de voo real. Para isso, foi construído um simulador onde é possível realizar diversos planos de voo com a finalidade de analisar diferentes estratégias em um ambiente provido de incertezas. O simulador foi desenvolvido na ferramenta Ptolemy e integrado, através do HLA, com o simulador SITL/ArduPilot. Os resultados mostram que com a utilização da abordagem defendida neste trabalho é possível obter resultados mais próximos da realidade, assim estratégias mais eficientes de voo podem ser desenvolvidas e avaliadas.

The present study focuses on developing an efficient and stable unmanned aerial system traffic management (UTM) system that utilizes a data-driven target tracking method and a distributed path planning algorithm for multiple Unmanned Aerial Vehicle (UAV) operations with local dynamic networks, which can provide flexible scalability, enabling autonomous operation of a large number of UAVs in dynamically changing environment. Traditional dynamic motion-based target tracking methods often encounter limitations due to their reliance on a finite number of dynamic motion models. To address this, data-driven target tracking methods were developed based on the statistical model of the Gaussian mixture model (GMM) and deep neural networks of long-short term memory (LSTM) model, to estimate instant and future states of UAV for local path planning problems. The estimation accuracy of the data-driven target tracking methods were analyzed and compared with dynamic model-based target tracking methods. A hybrid dynamic path planning algorithm was proposed, which selectively employs grid-free and -based path search methods depending on the spatio-temporal characteristics of the environments. In static environment, the artificial potential field (APF) method was utilized, while the $A^*$ algorithm was applied in the dynamic state environment. Furthermore, the data-driven target tracking method was integrated with the hybrid path planning algorithm to enhance deconfliction. To ensure smooth trajectories, a minimum snap trajectory method was applied to the planned paths, enabling controller tracking that remains dynamically feasible throughout the entire operation of UAVs. The methods were validated in the Software-in-the-loop (SITL) demonstration with the simple PID controller of the UAVs implemented in the software program. / Ph.D. / This dissertation focuses on developing data-driven models for tracking and path planning of Unmanned Aerial Vehicle (UAV) in dynamic environments with multiple operations. The goal is to improve the accuracy and efficiency of Unmanned Aircraft System traffic management (UTM) under such conditions. The data-driven models are based on Gaussian mixture model (GMM) and long-short term memory (LSTM) and are used to estimate the instant and consecutive future states of UAV for local planning problems. These models are compared to traditional target tracking models, which use dynamic motion models like constant velocity or acceleration. A hybrid dynamic path planning approach is also proposed to solve dynamic path planning problems for multiple UAV operations at an efficient computation cost. The algorithm selectively employs a path planning method between grid-free and grid-based methods depending on the characteristics of the environment. In static state conditions, the system uses the artificial potential field method (APF). When the environment is time-variant, local path planning problems are solved by activating the $A^*$ algorithm. Also, the planned paths are refined by minimum snap trajectory to ensure that the path is dynamically feasible throughout a full operation of the UAV along with controller tracking. The methods were validated in the Software-in-the-loop (SITL) demonstration with the simple PID controller of the UAVs implemented in the software program.

Exploration is a fundamental problem in robotics that requires robots to navigate through unknown environments to autonomously gather information about their surroundings while executing collision-free paths. In this project, we propose a method for producing smooth paths during the exploration process in indoor environments using UAVs to improve battery efficiency and enhance the quality of pose estimation. The developed framework is built by merging two approaches that represent the state of the art in the field of autonomous exploration with UAVs. The overall exploration logic is given by GLocal, a paper that introduces a hybrid, i.e. both sampling-based and frontier-based, framework that is able to cope with the issue of odometry drift when exploring indoor environments due to the absence of absolute localization, e.g. through GNSS. The second approach is FUEL, which introduces a frontier-based exploration methodology which computes the ’drones path as an optimized non-uniform B-Spline. The framework described in this thesis borrows the optimized B-Spline trajectory generation from FUEL and implements it in GLocal. To do this, the original cost function defined by GLocal for each exploration viewpoint was modified and the resulting samples were used to select the initial control points of the B-Spline. Furthermore, we extended the underlying state machine governing the entire algorithm and we revisited the original re-planning logic. The presented system is evaluated in various simulated environments, showcasing the advantages and disadvantages of this method. These evaluations demonstrate its improved state estimation performance and absolute observed volume, albeit at the expense of longer traveled trajectories in big and complex environments. / Utforskning är ett grundläggande problem inom robotteknik som kräver att robotar navigerar genom okända miljöer för att autonomt samla in information om sin omgivning samtidigt som de utför kollisionsfria banor. I det här projektet föreslår vi en metod för att producera jämna banor under utforskningsprocessen i inomhusmiljöer med hjälp av UAV:er för att förbättra batterieffektiviteten och förbättra kvaliteten på posestimeringen. Det utvecklade ramverket bygger på en sammanslagning av två metoder som representerar den senaste tekniken inom autonom utforskning med UAV:er. Den övergripande utforskningslogiken ges av GLocal, en artikel som introducerar en hybrid, i.e. både samplingsbaserad och gränsbaserad, ram som kan hantera problemet med odometridrift vid utforskning av inomhusmiljöer på grund av frånvaron av absolut lokalisering, e.g. genom GNSS. Den andra metoden är FUEL, som introducerar en gränsbaserad utforskningsmetod som beräknar drönarens bana som en optimerad icke-uniform B-Spline. Ramverket som beskrivs i denna avhandling lånar den optimerade B-Spline-banegenereringen från FUEL och implementerar den i GLocal. För att göra detta modifierades den ursprungliga kostnadsfunktionen som definierades av GLocal för varje utforskningspunkt och de resulterande samplen användes för att välja de initiala kontrollpunkterna för B-Spline. Dessutom utökade vi den underliggande tillståndsmaskinen som styr hela algoritmen och vi reviderade den ursprungliga logiken för omplanering. Det presenterade systemet utvärderas i olika simulerade miljöer, vilket visar fördelarna och nackdelarna med denna metod. Dessa utvärderingar visar på förbättrad prestanda för tillståndsuppskattning och absolut observerad volym, om än på bekostnad av längre färdvägar i stora och komplexa miljöer.