Unmanned aircraft systems (UAS) represent the future of modern aviation. Over the past 10 years their use abroad by the military has become commonplace for surveillance and combat. Unfortunately, their use at home has been far more restrictive. Due to safety and regulatory concerns, UAS are prohibited from flying in the National Airspace System without special authorization from the FAA. One main reason for this is the lack of an on-board pilot to "see and avoid" other air traffic and thereby maintain the safety of the skies. Development of a comparable capability, known as "Sense and Avoid" (SAA), has therefore become a major area of focus. This research focuses on the SAA problem as it applies specifically to small UAS. Given the size, weight, and power constraints on these aircraft, current approaches fail to provide a viable option. To aid in the development of a SAA system for small UAS, various simulation and hardware tools are discussed. The modifications to the MAGICC Lab's simulation environment to provide support for multiple agents is outlined. The use of C-MEX s-Functions to improve simulation performance and code portability is also presented. For hardware tests, two RC airframes were constructed and retrofitted with autopilots to allow autonomous flight. The development of a program to interface with the ground control software and run the collision avoidance algorithms is discussed as well. Intruder sensing is accomplished using a low-power, low-resolution radar for detection and an Extended Kalman Filter (EKF) for tracking. The radar provides good measurements for range and closing speed, but bearing measurements are poor due to the low-resolution. A novel method for improving the bearing approximation using the raw radar returns is developed and tested. A four-state EKF used to track the intruder's position and trajectory is derived and used to provide estimates to the collision avoidance planner. Simulation results and results from flight tests using a simulated radar are both presented. To effectively plan collision avoidance paths a tree-branching path planner is developed. Techniques for predicting the intruder position and creating safe, collision-free paths using the estimates provided by the EKF are presented. A method for calculating the cost of flying each path is developed to allow the selection of the best candidate path. As multiple duplicate paths can be created using the branching planner, a strategy to remove these paths and greatly increase computation speed is discussed. Both simulation and hardware results are presented for validation.

Proving the correctness of autonomous systems is challenged by the use of non-deterministic artificial intelligence algorithms and ever-increasing lines of code. While correctness is conventionally determined through analysis and testing, it is impossible to train and test the system for all possible scenarios or formally analyze millions of lines of code. This thesis describes an alternative method that monitors system behavior during runtime and executes a recovery action if any formally specified property is violated. Multiple parallel safety monitors synthesized from linear temporal logic (LTL) formulas capturing the correctness and liveness properties are implemented in isolated configurable hardware to avoid negative impacts on the system performance. Model checking applied to the final implementation establishes the correctness of the last line of defense against malicious attacks and software bugs. The first part of this thesis illustrates the monitor synthesis flow with rules defining a three-dimensional cage for a commercial-off-the-shelf drone and demonstrates the effectiveness of the monitoring system in enforcing strict behaviors. The second part of this work defines safety monitors to provide assurances for a virtual autonomous flight beyond visual line of sight. Distinct sets of monitors are called into action during different flight phases to monitor flight plan conformance, stability, and airborne collision avoidance. A wireless interface supported by the proposed architecture enables the configuration of monitors, thereby eliminating the need to reprogram the FPGA for every flight. Overall, the goal is to increase trust in autonomous systems as demonstrated with two common drone operations. / Master of Science / Software code in autonomous systems, like cars, drones, and robots, keeps growing not just in length, but also in complexity. The use of machine learning and artificial intelligence algorithms to make decisions could result in unexpected behaviors when encountering completely new situations. Traditional methods of verifying software encounter difficulties while establishing the absolute correctness of autonomous systems. An alternative to proving correctness is to enforce correct behaviors during execution. The system's inputs and outputs are monitored to ensure adherence to formally stated rules. These monitors, automatically generated from rules specified as mathematical formulas, are isolated from the rest of the system and do not affect the system performance. The first part of this work demonstrates the feasibility of the approach by adding monitors to impose a virtual cage on a commercially available drone. The second phase of this work extends the idea to a simulated autonomous flight with a predefined set of points that the drone must pass through. These points along with the necessary parameters for the monitors can be uploaded over Bluetooth. The position, speed, and distance to nearby obstacles are independently monitored and a recovery action is executed if any rule is violated. Since the monitors do not assume anything about the source of the violations, they are effective against malicious attacks, software bugs, and sensor failures. Overall, the goal is to increase confidence in autonomous systems operations.

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UAS has been become a very popular tool in surveying and evaluation of the systems measurement uncertainties are necessary. The most common method for georeferencing UAS data is to use ground control points (GCP) in order to use them in block adjustment. In recent years’ new techniques for direct georeferencing with UAS have been presented, which in theory means that the position of the UAS can be determined accurately enough and therefore GCP’s can be excluded. This study evaluates  uncertainties of the UAS Freya from SmartPlanes that don’t need GCP’s for georeferencing. The technique applied in the evaluation is based on Post Processed Kinematic (PPK) for coordinate determination of the UAS, which means that the collected GNSS data can be post processed using a reference station.   The test area was a 280 x 320 m block in the north end of Gävle airport, Sweden. Each flight is conducted in two orthogonal blocks and evaluated in three different ways against the 16 GCP. The altitude was about 90 m for all flights. The uncertainty of the PPK-technique is tested and evaluated with three different methods to ensure both accuracy and potential use. In total five flights were assessed and evaluated with Agisoft PhotoScan against 16 GCP spread over the area. The position of each GCP’s was determined with four independent network RTK measurements.  The results show that the georeferencing with the PPK-technique and block adjustment has potential to meet the uncertainties in level with indirect georeferencing using GCP. The results show very similar planimetric uncertainties, around 0,020 m in RMS, for all evaluations with the PPK-technique. The results of the uncertainty in height is more scattered where the two lowest results in a RMS under 0,015 m and the highest over 0,100 m for the difference against the 16 GCP.  It is possible to achieve low uncertainties with the method without the use of GCP. For areas where establishment of GCP is not possible, using UAS equipped with PPKtechnology provides a very suitable alternative to use. The results show relatively large differences between the evaluations and in order to determine the exact cause of them, further studies are required. / Den starka teknikutvecklingen för UAS resulterar i flera nya produkter på marknaden och för att utvärdera deras mätosäkerheter krävs det kontroller av systemen. Den vanligaste metoden vid georeferering med UAS är att använda koordinatbestämda flygsignaler på marken. På senare år har metoder för direkt georeferering presenterats, vilket i teorin innebär att positionen för UAS kan bestämmas så pass noggrant att flygsignaler kan uteslutas. I denna studie utvärderas mätosäkerheter för Freya, ett UAS från SmartPlanes som med hjälp av bra positionering och blockutjämning ska kunna användas för georeferering utan flygsignalering. Systemet från Smartplanes bygger på Post Processed Kinematic (PPK) för koordinatbestämning, vilket innebär att insamlat GNSS data kan efterberäknas med korrektioner från en referensstation. Mätosäkerheten för PPK-tekniken testas och utvärderas med 3 olika metoder för att se både på mätosäkerhet samt möjlig användning. Totalt fem flygningar har utförts på ett testområde som var cirka 280x320 m och beläget i den norra delen av Gävle flygplats. Flyghöjden var kring 90 m för alla flygningar som vidare har bearbetats och utvärderats i programvaran PhotoScan från Agisoft. Kontrollen av mätosäkerheten har gjorts mot 16 spridda kontrollpunkter på marken som har positionsbestämts med fyra oberoende nätverks-RTK mätningar vardera. Varje flygning är utförd i två ortogonala block och utvärderades med fyra olika konfigurationer mot de 16 kontrollpunkterna.  Resultaten visar att georeferering med hjälp av blockutjämning och PPK-tekniken har potential för att uppnå mätosäkerheter i nivå med indirekt georeferering med hjälp av stödpunkter på marken. I plan visar resultaten på väldigt jämna mätosäkerheter, kring 0,020 m i RMS, för alla utvärderingar med PPK-tekniken. Resultaten i höjd är mer spridda där de lägsta visar mätosäkerheter under 0,015 m RMS och de högsta över 0,100 m i RMS för avvikelsen mot kontrollpunkterna. Det är fullt möjligt att uppnå låga mätosäkerheter med metoden utan användning av stödpunkter. För användningsområden där stödpunkter inte kan etableras är detta UAS med PPK-tekniken ett mycket lämpligt alternativ att använda. Resultaten visar på relativt stora skillnader mellan de olika testade metoderna och för att avgöra den exakta orsaken till dem skulle vidare studier behövas.

The focus of this dissertation is on developing novel methods and extending existing ones to improve the security and reliability of fixed-wing unmanned aircraft systems (UAS). Specifically, we focus on three strands of work: i) designing UAS controllers with performance guarantees using the robust control framework, ii) developing tools for detection and mitigation of physical-layer security threats in UAS, and iii) extending tools from compositional verification to design and verify complex systems such as UAS. Under the first category, we use the robust H-infinity control approach to design a linear parameter-varying (LPV) path-following controller for a fixed-wing UAS that enables the aircraft to follow any arbitrary planar curvature-bounded path under significant environmental disturbances. Three other typical path-following controllers, namely, a linear time-invariant H-infinity controller, a nonlinear rate-tracking controller, and a PID controller, are also designed. We study the relative merits and limitations of each approach and demonstrate through extensive simulations and flight tests that the LPV controller has the most consistent position tracking performance for a wide array of geometric paths. Next, convex synthesis conditions are developed for control of distributed systems with uncertain initial conditions, whereby independent norm constraints are placed on the disturbance input and the uncertain initial state. Using this approach, we design a distributed controller for a network of three fixed-wing UAS and demonstrate the improvement in the transient response of the network when switching between different trajectories. Pertaining to the second strand of this dissertation, we develop tools for detection and mitigation of security threats to the sensors and actuators of UAS. First, a probabilistic framework that employs tools from statistical analysis to detect sensor attacks on UAS is proposed. By incorporating knowledge about the physical system and using a Bayesian network, the proposed approach minimizes the false alarm rates, which is a major challenge for UAS that operate in dynamic and uncertain environments. Next, the security vulnerabilities of existing UAS actuators are identified and three different methods of differing complexity and effectiveness are proposed to detect and mitigate the security threats. While two of these methods involve developing algorithms and do not require any hardware modification, the third method entails hardware modifications to the actuators to make them resilient to malicious attacks. The three methods are compared in terms of different attributes such as computational demand and detection latency. As for the third strand of this dissertation, tools from formal methods such as compositional verification are used to design an unmanned multi-aircraft system that is deployed in a geofencing application, where the design objective is to guarantee a critical global system property. Verifying such a property for the multi-aircraft system using monolithic (system-level) verification techniques is a challenging task due to the complexity of the components and the interactions among them. To overcome these challenges, we design the components of the multi-aircraft system to have a modular architecture, thereby enabling the use of component-based reasoning to simplify the task of verifying the global system property. For component properties that can be formally verified, we employ results from Euclidean geometry and formal methods to prove those properties. For properties that are difficult to be formally verified, we rely on Monte Carlo simulations. We demonstrate how compositional reasoning is effective in reducing the use of simulations/tests needed in the verification process, thereby increasing the reliability of the unmanned multi-aircraft system. / Doctor of Philosophy / Given the safety-critical nature of many unmanned aircraft systems (UAS), it is crucial for stake holders to ensure that UAS when deployed behave as intended despite atmospheric disturbances, system uncertainties, and malicious adversaries. To this end, this dissertation deals with developing novel methods and extending existing ones to improve the security and reliability of fixed-wing UAS. Specifically, we focus on three key areas: i) designing UAS controllers with performance guarantees, ii) developing tools for detection and mitigation of security threats to sensors and actuators of UAS, and iii) extending tools from compositional verification to design and verify complex systems such as UAS. Pertaining to the first area, we design controllers for UAS that would enable the aircraft to follow any arbitrary planar curvature-bounded path under significant atmospheric disturbances. Four different controllers of differing complexity and effectiveness are designed, and their relative merits and limitations are demonstrated through extensive simulations and flight tests. Next, we develop control design tools to improve the transient response of multi-mission UAS networks. Using these tools, we design a controller for a network of three fixed-wing UAS and demonstrate the improvement in the transient response of the network when switching between different trajectories. As for the contributions in the second area, we develop tools for detection and mitigation of security threats to the sensors and actuators of UAS. First, we propose a framework for detecting sensor attacks on UAS. By judiciously using knowledge about the physical system and techniques from statistical analysis, the framework minimizes the false alarm rates, which is a major challenge in designing attack detection systems for UAS. Then, we focus on another important attack surface of the UAS, namely, the actuators. Here, we identify the security vulnerabilities of existing UAS actuators and propose three different methods to detect and mitigate the security threats. The three methods are compared in terms of different attributes such as computational demand, detection latency, need for hardware modifications, etc. In regard to the contributions in the third area, tools from compositional verification are used to design an unmanned multi-aircraft system that is tasked to track and compromise an aerial encroacher, wherein the multi-aircraft system is required to satisfy a global system property pertaining to collision avoidance and close tracking. A common approach to verifying global properties of systems is monolithic verification where the whole system is analyzed. However, such an approach becomes intractable for complex systems like the multi-aircraft system considered in this work. We overcome this difficulty by employing the compositional verification approach, whereby the problem of verifying the global system property is reduced to a problem of reasoning about the system’s components. That being said, even formally verifying some component properties can be a formidable task; in such cases, one has to rely on Monte Carlo simulations. By suitably designing the components of the multi-aircraft system to have a modular architecture, we show how one can perform focused component-level simulations rather than conduct simulations on the whole system, thereby limiting the use of simulations during the verification process and, as a result, increasing the reliability of the system.

The modernization of the National Air Traffic Control System is on the horizon, and with it, the possible introduction of autonomous air vehicles into the national airspace. Per the FAA Aerospace Forecast (FAA, 2013), U.S. carrier passenger traffic is expected to average 2.2 percent growth per year over the next 20 years with government statistics indicating that the average domestic load factor for airlines in 2014 was approximately 84.4 percent (US Department of Transportation, 2015). Adding to that demand, the potential introduction of unmanned and autonomous air vehicles motivates reconsideration of control schemes. One of the proposed solutions (Eby, 1994) would involve a decentralized control protocol. Equipping each aircraft with the information necessary to navigate safely through integrated airspace becomes an information sharing problem: how much information about other aircraft is required for a pilot to safely fly the gamut of a heavily populated airspace and what paradigm shifts may be necessary to safely and efficiently utilize available airspace? This thesis describes the development of a tool for testing alternative traffic management systems, centralized or decentralized, in the presence of uncertainty. Applying a computational fluid dynamics-inspired approach to the problem creates a simulation tool to model both the movement of traffic within the airspace and also allows study of the effects of interactions between vehicles. By incorporating a Smoothed Particle Hydrodynamics (SPH) based model, discrete particle aircraft each carry a set of unique deterministic and stochastic properties. With this model, aircraft interaction can be studied to better understand how variations in the nondeterministic properties of the system affect its overall efficiency and safety. The tool is structured to be sufficiently flexible as to allow incorporation of different collision detection and avoidance rules for aircraft traffic management. / Master of Science

<p dir="ltr">This study introduces a methodology wherein daily values are linearly interpolated to achieve uniform temporal resolution across various data sets, including spectral and environmental information. This approach facilitates further analysis using machine learning techniques to estimate biomass. The proposed Best Friend Frame (B.F.F.) data set integrates Unmanned Aerial Systems (UAS) data, weather data, weather indices, soil hydrological group classifications, and topographic information. Two different biomass estimations were created to enhance versatility: one averaged per management practice and another averaged per physical experimental plot size. Additionally, SuperDove satellite data were combined with identical environmental data as that of the UAS.</p><p dir="ltr">UAS flights were conducted at the ACRE field in 2022 and 2023. The UAS data were captured at a height of 30 meters, yielding a ground sample distance of 2 cm/pixel per flight. Satellite data were sourced from the Planet SuperDove product. The images were processed using Crop Image Extraction (CIE) and calibrated with Vegetation Index Derivation (VID). Spatial resolution was defined as the experimental plot size per year per crop type (soybean or corn). Topographic data were derived from Indiana LiDAR data, and soil information was obtained from the USDA SSURGO dataset.</p><p dir="ltr">The B.F.F. framework can be utilized with various models to identify which environmental inputs influence biomass accumulation throughout the growing season.</p>

<p>Civil engineering structures must provide an adequate and safe performance during their time of service, and the owners of these structures must have a reliable inspection strategy to ensure time-dependent damage does not become excessive. Visual inspection is the first step in every structural inspection; however, many elements in the majority of structures are difficult to access and require specialized personal and equipment. In an attempt to reduce the risk of the inspector and the cost of additional equipment, the use of Unmanned Aircraft Systems (UAS) has been increasing in the last years. The absence of standards and regulations regarding the use of UAS in inspection of structures has allowed the market to widely advertise Unmanned Aerial Vehicles (UAV) without protocols or qualifications that prove their effectiveness, leaving the owners of the structures to solely rely on claims of the vendors before deciding which technology suits their particular inspection needs. Focusing primarily on bridge inspection, this research aimed to address the lack of performance-based evaluation and standards for UAS, developing a validation criterion to evaluate a given UAS based on a repeatable test that resembles typical conditions in a structure. </p><p><br></p><p>Current applications of UAS in inspection of structures along with its advantages and limitations were studied to determine the current status of UAS technologies. A maximum typical rotor-tip-to-rotor-tip distance of an UAV was determined based on typical UAVs used in bridge inspection, and two main parameters were found to be relevant when flying close to structures: proximity effects in the UAV and availability of visual line of sight. Distances where proximity effects are relevant were determined based on several field inspections and flights close to structures. In addition, the use of supplementary technologies such as Global Positioning System (GPS) and Inertial Measurement Units (IMU) was studied to understand their effect during inspection. </p><p><br></p><p>Following the analysis, the author introduces the idea of a series of obstacles and elements inside an enclosed space that resemble components of bridge structures to be inspected using UAVs, allowing repeatability of the test by controlling outside parameters such as lighting condition, wind, precipitation, temperature, and GPS signal. Using distances based on proximity effects, maximum typical rotor-tip-to-rotor-tip distance, and a gallery of bridges and situations when flying close to bridge structures, a final arrangement of elements is presented as the evaluation chamber. Components inside the evaluation chamber include both “real” steel and concrete specimens as well as those intended to simulate various geometric configurations on which other features are mounted. Pictures of damages of steel and concrete elements have been placed in the internal faces of the obstacles that can be assessed either in real-time flight or in post-processing work. A detailed comparison between the objectives of this research project and the results obtained by the evaluation chamber was performed using visual evaluation and resolution charts for the images obtained, the availability of visual line of sight during the test, and the absence of GPS signal.</p><p><br></p><p>From the comparison and analysis conducted and based on satisfactory flight results as images obtained during flights, the evaluation chamber is concluded to be a repeatable and reliable tool to apply to any UAS prior to inspect bridges and other structures, and the author recommends to refrain from conducting an inspection if the UAS does not comply with the minimum requirements presented in this research work. Additionally, this research provided a clearer understanding of the general phenomenon presented when UAVs approach structures and attempts to fill the gap of knowledge regarding minimum requirements and criterion for the use of UAS technologies in inspection of structures.</p>

Over the last years there has been a rapid development in the UAS-technology (Unmanned Aircraft Systems) and today there are several UAS systems on the market. The fast development has led to differences in both price and capability of taking high-quality images between the systems. The purpose of this study was firstly to investigate how two UAS systems differ in the uncertainty of measurement while making digital terrain models, secondly, to investigate how different UAS systems cope with the laws and requirements that exist for producing digital terrain models for detail projection, SIS-TS 21144:2016 Table 6 level 1-3. A comparative study on two software’s creation of point clouds from picture data was also conducted. In this study, three digital models were made from one specific area. They were created with two different UAS-systems and laser scanning from an airplane. The models were compared and analysed using the RUFRIS method. The UASsystems used were a fixed wings Smartplanes S1C and a rotary wings Dji Phantom 4 PRO. The Smartplanes flew 174 m above the ground and the Dji Phantom 4 flew 80 m above the ground. The results from the study show that laser scanning from the airplane created the model with the lowest measurement uncertainty and met all the requirements for each separate type (asphalt, natural soil, grass and gravel) for detail projection according to SIS.TS 201144:2016 table 6 level 1-3. Additionally, the results show that the terrain model produced by the Dji Phantom 4 only met the requirements for asphalt where the mean deviation was 0,001 m. The results produced with “Smartplanes” met the requirements for asphalt and gravel where the mean deviations were -0,007 m and 0,017 m. The softwares PhotoScan and UASMaster were compared while creating point clouds from pictures taken by the Smartplanes. The results show that PhotoScan had the lowest uncertainty for asphalt, grass and gravel surfaces while UASMaster produced lower uncertainty for natural soil. The results indicate that airborne laser scanning should be the preferred method for collection of topographic data since it created lower measurement uncertainties than the other methods in this study. It is also possible to create digital terrain models with UAS for detail projection for asphalt and gravel surface in accordance with 21144:2016. Finally, it was concluded that the used software programs are showing differences in creating point clouds. / De senaste åren har tekniken för Unmanned Aircraft System (UAS) utvecklats snabbt och idag finns flera system på marknaden. Ett resultat av den snabba utvecklingen är att de olika systemen skiljer sig åt, dels i pris men även i kapacitet. Syftet med studien var att undersöka hur olika UAS-system skiljer sig åt i mätosäkerhet vid framställning av digitala terrängmodeller, men även hur olika UAS-system står sig mot det regelverk som finns för framställning av digitala terrängmodeller vid detaljprojektering enligt SIS-TS 21144:2016 Tabell 6 klass 1-3. Ytterligare ett syfte med studien var att undersöka hur olika programvaror skiljer sig åt vid framställning av punktmoln från bilddata. I studien kontrollerades och jämfördes tre digitala terrängmodeller genererade över samma område med två olika UAS-system samt laserskanning från ett flygplan. Terrängmodellerna jämfördes mot kontrollprofiler framställda med RUFRIS-metoden. De olika UAS-systemen var en dyrare variant, Smartplanes S1C (fastavingar), och en billigare variant, Dji Phantom 4 PRO (roterande vingar). De tillämpade flyghöjderna för flygningarna var 174 m för Smartplanes och 80 m för Dji Phantom. Resultatet från studien visar att laserskanning från flygplanet uppnådde lägst mätosäkerhet och klarade samtliga krav för varje separat marktyp för detaljprojektering enligt SIS-TS 201144:2016 Tabell 6 klass 1-3. Marktyper som undersöktes var: asfalt, naturmark, gräs och grus. Vidare klarade terrängmodellen producerad med Dji Phantom endast kravet för asfaltsytor, där medelavvikelsen fastställdes till 0,001 m. Terrängmodellen producerad med Smartplanes klarade endast kraven för marktyperna asfalt och grus där medelavvikelsen fastställdes till -0,007 m respektive 0,017 m. Som en del i studien jämfördes programvarorna PhotoScan och UASMaster för framställning av punktmoln för bilder insamlade med Smartplanes S1C. Resultatet visar att PhotoScan uppnådde lägst mätosäkerhet för asfalt, gräs och grus medan UASMaster uppnådde lägst mätosäkerhet för naturmark. Studien visar att flygburen laserskanning borde vara en fortsatt föredragen metod för insamling av topografisk data då metoden resulterade i lägst mätosäkerheter i denna studie. Vidare visar studien att det är möjligt att framställa digitala terrängmodeller med UAS för detaljprojektering enligt SISTS 21144:2016 för asfalt- och grusytor. Dessutom konstateras att olika bearbetningsprogram skiljer sig vid framställning av punktmoln.