Leaf area index (LAI) is a versatile indicator of crop growth that is used to estimate evapotranspiration (ET), monitor nitrogen status, and estimate crop yield. Traditional methods for measuring LAI can be improved using high resolution remote sensing. The aim of this study was to compare approaches for estimating LAI from UAV-derived visible vegetation indices. Coincident ground-based and remotely sensed data were obtained from two irrigated wheat fields and were sampled at a total of 5 events in 2019 and 2020. Ground-based LAI was measured with a ceptometer and remotely sensed images were collected using a consumer-grade UAV. Mosaiced orthophotos were resampled from native (0.06m) spatial resolution to increasingly coarser spatial resolutions up to 3 m by either a direct or ladder resampling method. Visible band color information (RGB) was extracted from the orthophotos at the points that LAI was collected within field and 12 different visible vegetation indices (VVIs) were calculated. Linear regression was performed to evaluate the relationships between wheat LAI and each calculated VVI for all spatial resolutions and resampling methods. Three VVIs, visible atmospherically resistant index (VARI), normalized green-red difference index (NGRDI), and modified green-red vegetation index (MGRVI), estimated LAI equally well (R2= 0.66, 0.66,0.66; RMSE=0.74,0.73,0.73; MAE=0.57,0.56,0.56) when resampled to 3 m spatial resolution with the ladder resampling method. These results demonstrate the potential to remotely estimate LAI using only RGB cameras and consumer grade drones. An additional aim of this study was to evaluate use of a remotely sensed visible vegetation index to characterize the spatial variability of LAI within irrigated wheat fields. Variation of LAI was measured with a ceptometer on random nested grids at two sites with pre-determined management zones in 2019 and 2020. Coincident digital imagery was collected using a consumer-grade unmanned aerial vehicle (UAV). A visible atmospherically resistant index (VARI) LAI estimation model was applied to red, green, blue (RGB) UAV imagery using a ladder resampling approach from 0.06 m to 3 m spatial resolution. There was significant within-field spatial and temporal variation of mean LAI. For example, in May at the Grace, ID location measured LAI ranged from 0.21 to 2.58 and in June from 1.68 to 4.15. The relationship of measured and estimated LAI among management zones was strong (R2=0.84), validating the remote sensing approach to characterize LAI differences among management zones. There were statistically significant differences in estimated LAI among zones for all sampling dates (P=0.05). We assumed a minimum difference of 15% between zone LAI and the field mean for justifying variable rate irrigation among zones, a threshold that corresponds with approximately a 10% difference in evapotranspiration rate. Three of the five sampling dates had LAI differences that exceeded the threshold for at least one zone, with all three having mean LAI of less than 2.5. The VARI model for estimating LAI remotely is more effective at identifying LAI differences among management zones at lower LAI. Application of this approach has potential for applications such as estimating evapotranspiration of irrigated fields and delineation of zones for variable rate irrigation.

The implementation of Unmanned Aerial Vehicles (UAVs) and Structure‐from‐Motion (SfM) photogrammetry in assessing forest structures for forest inventory and biomass estimations has shown great promise in reducing costs and labour intensity while providing relative accuracy. Tree Height (TH) and Diameter at Breast Height (DBH) are two major variables in biomass assessment. UAV-based TH estimations depend on reliable Digital Terrain Models (DTMs), while UAV-based DBH estimations depend on reliable dense photogrammetric point cloud. The main aim of this study was to evaluate the performance of multirotor UAV photogrammetric point cloud in estimating homogeneous and heterogeneous forest structures, and their comparison to more accurate LiDAR data obtained from Aerial Laser Scanners (ALS), Terrestrial Laser Scanners (TLS), and more conventional means like manual field measurements. TH was assessed using UAVSfM and LiDAR point cloud derived DTMs, while DBH was assessed by comparing UAVSfM photogrammetric point cloud to LiDAR point cloud, as well as to manual measurements. The results obtained in the study indicated that there was a high correlation between UAVSfM TH and ALSLiDAR TH (R2 = 0.9258) for homogeneous forest structures, while a lower correlation between UAVSfM TH and TLSLiDAR TH (R2 = 0.8614) and UAVSfM TH and ALSLiDAR TH (R2 = 0.8850) was achieved for heterogeneous forest structures. A moderate correlation was obtained between UAVSfM DBH and field measurements (R2 = 0.5955) for homogenous forest structures, as well as between UAVSfM DBH and TLSLiDAR DBH (R2 = 0.5237), but a low correlation between UAVSfM DBH and UAVLiDAR DBH (R2 = 0.1114). This research has demonstrated that UAVSfM can be adequately used as a cheaper alternative in forestry management compared to more highcost and accurate LiDAR, as well as traditional technologies, depending on accuracy requirements.

This thesis addresses the subject of drone deliveries within healthcare to examine whether unmanned aerial vehicles can contribute with increased accessibility to medical supply. An overview of the advancements made in the field since its introduction in health care, is first presented in the report. Through a literary review on the subject, dimensions have been identified as significant for determining the possibly increased accessibility to medical equipment that the unmanned aerial system provides, and thus the utility of the system. This thesis further evaluates the system out of an accessibility perspective, which aims to meet theoretical criteria established by previous researchers in the accessibility field, and thereby adopt a holistic approach to the subject. It is concluded that the dimensions are dependent on characteristics of the geographical location and should be considered when discussing drones in medicine. However, knowledge and evaluations are lacking from regions that have implemented such a system, which contributes to uncertainties for how drone deliveries work in practice.

This compilation thesis presents an overarching framework on the utilization of nonlinear model predictive control(NMPC) for various applications in the context of Unmanned Aerial Vehicle (UAV) path planning and collision avoidance. Fast and novel optimization algorithms allow for NMPC formulations with high runtime requirement, as those posed by controlling UAVs, to also have sufficiently large prediction horizons as to in an efficient manner integrate collision avoidance in the form of set-exclusion constraints that constrain the available position-space of the robot. This allows for an elegant merging of set-point reference tracking with the collision avoidance problem, all integrated in the control layer of the UAV. The works included in this thesis presents the UAV modeling, cost functions, constraint definitions, as well as the utilized optimization framework. Additional contributions include the use case on multi-agent systems, how to classify and predict trajectories of moving (dynamic) obstacles, as well as obstacle prioritization when an aerial agent is in the precense of more obstacles, or other aerial agents, than can reasonably be defined in the NMPC formulation. For the cases of dynamic obstacles and for multi-agent distributed collision avoidance this thesis offers extensive experimental validation of the overall NMPC framework. These works push the limits of the State-of-the-Art regarding real-time real-life implementations of NMPC-based collision avoidance. The works also include a novel RRT-based exploration framework that combines path planning with exploration behavior. Here, a multi-path RRT * planner plans paths to multiple pseudo-random goals based on a sensor model and evaluates them based on the potential information gain, distance travelled, and the optimimal actuation along the paths.The actuation is solved for as as the solutions to a NMPC problem, implying that the nonlinear actuator-based and dynamically constrained UAV model is considered as part of the combined exploration plus path planning problem. To the authors best knowledge, this is the first time the optimal actuation has been considered in such a planning problem. For all of these applications, the utilized optimization framework is the Optimization Engine: a code-generation framework that generates a custom Rust-based solver from a specified model, cost function, and constraints. The Optimization Engine solves general nonlinear and nonconvex optimization problems, and in this thesis we offer extensive experimental validation of the utilized Proximal-Averaged Newton-type method for Optimal Control (PANOC) algorithm as well as both the integrated Penalty Method and Augmented Lagrangian Method for handling the nonlinear nonconvex constraints that result from collision avoidance problems.

No description available.

No description available.

Due to the changing climate and inherent atypically occurring meteorological events in the Arctic regions, more accurate snow quality predictions are needed in order to support the Sámi reindeer herding communities in northern Sweden that struggle to adapt to the rapidly changing Arctic climate. Spatial snow depth distribution is a crucial parameter not only to assess snow quality but also for multiple environmental research and social land use purposes. This contrasts with the current availability of affordable and efficient snow monitoring methods to estimate such an extremely variable parameter in both space and time. In this thesis, a novel approach to determine spatial snow depth distribution in challenging alpine terrain is presented and tested during a field campaign performed in Tarfala, Sweden in April 2019. A multispectral camera capturing five spectral bands in wavelengths between 470 and 860 nanometers on board of a small Unmanned Aerial Vehicle is deployed to derive 3D snow surface models via photogrammetric image processing techniques. The main advantage over conventional photogrammetric surveys is the utilization of accurate RTK positioning technology that enables direct georeferencing of the images, and thus eliminates the need for ground control points and dangerous and time-consuming fieldwork. The continuous snow depth distribution is retrieved by differencing two digital surface models corresponding to the snow-free and snow-covered study areas. An extensive error assessment based on ground measurements is performed including an analysis of the impact of multispectral imagery. Uncertainties and non-transparencies due to a black-box environment in the photogrammetric processing are, however, present, but accounted for during the error source analysis. The results of this project demonstrate that the proposed methodology is capable of producing high-resolution 3D snow-covered surface models (< 7 cm/pixel) of alpine areas up to 8 hectares in a fast, reliable and cost-efficient way. The overall RMSE of the snow depth estimates is 7.5 cm for data acquired in ideal survey conditions. The proposed method furthermore assists in closing the scale gap between discrete point measurements and regional-scale remote sensing, and in complementing large-scale remote sensing data by providing an adequate validation source. As part of the Swedish cooperation project ’Snow4all’, the findings of this project are used to support and validate large-scale snow models for improved snow quality prediction in northern Sweden. / På grund av klimatförändringar och naturliga meteorologiska händelser i arktis behövs mer exakta snökvalitetsprognoser för att stödja samernas rensköttsamhällen i norra Sverige som har problem med att anpassa sig till det snabbt föränderliga arktiska klimatet. Rumslig snödjupsfördelning är en avgörande parameter för att inte bara bedöma snökvaliteten utan även för flera miljöforskning och sociala markanvändningsändamål. Detta står i motsats till den nuvarande tillgången till överkomliga och effektiva metoder för snöövervakning för att uppskatta sådan extremt varierande parameter i tid och rum. I detta arbete presenteras och testas en ny metod för att bestämma rumslig snödjupssdistribution i utmanande alpin terräng under en fältstudie som genomfördes i Tarfala i norra Sverige i april 2019. Via fotogrammetrisk bildbehandlingsteknik hämtades snöytemodeller i 3D med hjälp av en multispektral kamera monterad på en liten obemannad drönare. En viktig fördel, i jämförelse med konventionella fotogrammetriska undersökningar, är användningen av exakt RTK-positioneringsteknik som möjliggör direkt georeferencing och eliminerar behovet av markkontrollpunkter. Den kontinuerliga snödjupfördelningen hämtas genom att ytmodellerna delas upp i snöfria respektive snötäckta undersökningsområden. En omfattande felsökning som baseras på markmätningar utförs, inklusive en analys av effekten av multispektrala bilder. Resultaten från denna studie visar att den famtagna metoden kan producera högupplösta snötäckta höjdmodeller i 3D (< 7 cm/pixel) av alpina områden på upp till 8 hektar på ett snabbt, pålitligt och kostnadseffektivt sätt. Den övergripande RMSE för det beräknade snödjupet är 7,5 cm för data som förvärvats under idealiska undersökningsförhållanden. Som ett led i det svenska projektet “Snow4all” används resultaten från projektet för att förbättra och validera storskaliga snömodeller för att bättre förutse snökvaliteten i norra Sverige.

The aviation industry is under increasing pressure to reduce cost and manage the increased number of passengers. One area under pressure is the Air Traffic Control. The Air Traffic Control will in a foreseeable future manage the introduction of drones also known as Unmanned Aerial Vehicles by integrating them into civil airspace with manned aircraft. Drones are lacking consensus from authorities with regards to standards due to their rapid expansion. Given their size, shape and speed, they can also pose threats to manned aircrafts and there is a need to address them in an Air Traffic Management system interoperating with manned aircrafts. The purpose in this study is to identify what considerations to make when automating complex system elements with respect to safety. Safety involves all the different stakeholders in the air transportation system, which is a Safety critical System. Furthermore, the aim is also to identify areas in which European Operational Concept Validation Methodology (E-OCVM) can be complemented with. Standard E-OCVM is missing specific assessment criteria with regards to safety and how it can interact with other standards. The approach is thereby to use various standards with focus on Systems Engineering to complement E-OCVM since it is lacking with regards to how it is used to validate Air Traffic Control systems. To capture the complexity of automating elements of an industry involving many stakeholders, a qualitative analysis was conducted in this project, using a System Engineering approach with four standards A-SLP, A-RLP, A-DAS and A-SAS. A-SLP and A-RLP are two general standards while A-DAS and A-SAS are focusing on the contexts of aircrafts and software development. Empirical data was gathered by semi-structured interviews of seven experts within the relevant areas in the field. From the review of the four standards, it was found that they can for instance complement E-OCVM in how software errors can lead to a failure condition among other ways. The main identified considerations faced with an integration of drones into civil airspace, is to manage the human interaction with the introduced Air Traffic Management systems. More specifically, the human element must be involved from the training phase in the development of systems in a Safety Critical System to minimize risk. Furthermore, redundancies that are built into the system has to, not only be able to put the system into a safe state, but also be carefully analyzed in how they interact with other systems to avoid misjudgement for the Air Traffic Controllers. Lastly, to obtain specific details on how interoperability could occur using standards, the standards used in this study refer to usage of other documents and standards. Standards specifically tailored for the operational context of drones would facilitate further testing and implementation of their integration into civil airspace. Given that different standards were used to complement the EOCVM standard, a set of unified standards are required that are proportional with the type of drones, the type of operations and in the environment that they are operating in. This will be needed to fulfill the European vision of safe integration of drones and needs thereby to be carried out in a global manner, thus also share experience with other actors to advance the new technology adaptation.

Developing regions are often characterized by large areas that are poorly reachable or explored. The mapping and census of roaming populations in these areas are often difficult and sporadic. A recent spark in the development of small aerial vehicles has made them the perfect tool to efficiently and accurately monitor these areas. This paper presents an approach to aid area surveying through the use of Unmanned Aerial Vehicles. The two main components of this approach are an efficient on-device deep learning object identification component to capture and infer images with acceptable performance (latency andaccuracy), and a dynamic path planning approach, informed by the object identification component. In particular, this thesis illustrates the development of the path planning component, which exploits potential field methods to dynamically adapt the path based on inputs from the vision system. It also describes the integration work that was performed to implement the approach on a prototype platform, with the aim to achieve autonomous flight with onboard computation. The path planning component was developed with the purpose of gaining information about the populations detected by the object identification component, while considering the limited resources of energy and computational power onboard a UAV. The developed algorithm was compared to navigation using a predefined path, where the UAV does not react to the environment. Results from the comparison show that the algorithm provide more information about the objects of interest, with a very small change in flight time. The integration of the object identification and the path planning components on the prototype platform was evaluated in terms of end-to-end latency, power consumption and resource utilization. Results show that the proposed approach is feasible for area surveying in developing regions. Parts of this work has been published in the workshop of DroNet, collocated with MobiSys, with the title Surveying Areas in Developing Regions Through Context Aware Drone Mobility. Thework was carried out in collaboration with Alessandro Montanari, Alice Valentini, Cecilia Mascoloand Amanda Prorok. / Utvecklingsländer är ofta karaktäriserade av vidsträcka områden som är svåråtkomliga och outforskade. Kartläggning och folkräkning av populationen i dessa områden är svåra uppgifter som sker sporadiskt. Nya framsteg i utvecklingen av små, luftburna fordon har gjort dem till perfekta verktyg för att effektivt och noggrant bevaka dessa områden. Den här rapporten presenterar en strategi för att underlätta utforskning av dessa områden med hjälp av drönare. De två huvudkomponenterna i denna strategi är en effektiv maskininlärningskomponent för objektidentifiering med acceptabel prestanda i avseende av latens och noggrannhet, samt en dynamisk navigeringskomponent som informeras av objektidentifieringskomponenten. I synnerhet illustrerar denna avhandling utvecklingen av navigeringskomponenten, som utnyttjar potentialfält för att dynamiskt anpassa vägen baserat på information från objektidentifieringssystemet. Dessutom beskrivs det integrationsarbete som utförts för att implementera strategin på en prototypplattform, med målet att uppnå autonom flygning med all beräkning utförd ombord. Navigeringskomponenten utvecklades i syfte att maximera informationen om de populationer som upptäckts av objektidentifieringskomponenten, med hänsyn till de begränsade resurserna av energi och beräkningskraft ombord på en drönare. Den utvecklade algoritmen jämfördes med navigering med en fördefinierad väg, där drönaren inte reagerar på omgivningen. Resultat från jämförelsen visar att algoritmen ger mer information om objekten av intresse, med en mycket liten förändring i flygtiden. Integreringen av objektidentifieringskomponenten och navigeringskomponenten på prototypplattformen utvärderades med avseende på latens, strömförbrukning och resursutnyttjande. Resultaten visar att den föreslagna strategin är genomförbar för kartläggning och utforskning av utvecklingsregioner. Delar av detta arbete har publicerats i DroNets workshop, samlokaliserad med MobiSys, med titeln Surveying Areas in Developing Regions Through Context Aware Drone Mobility. Arbetet utfördes i samarbete med Alessandro Montanari, Alice Valentini, Cecilia Mascolo och Amanda Prorok.

Yes / Unmanned Aerial Vehicles (UAVs) are considered, nowadays, as a futuristic and robust paradigm for 5G wireless networks, in terms of providing Internet connectivity services onto infrastructure cellular networks. In this paper, the interference regime caused by multiple downlink aerial wireless transmission beams has been highlighted. This has been introduced by estimating the UAVs coverage area that is analytically derived in a tractable closed-form expression. The rationale of the analysed coverage approach relies on observing and adapting the joint aerial distance between the aerial base stations. This can minimize the intra-overlapped coverage and ultimately maximize the overall coverage performance for a better quality of service demands. The novelty of our approach brings useful design insights for UAVs system-level performance that technically helps in aerial coverage computations without the need of performing an aerial deployment setup. To the end, the performance effectiveness of our methodology has been tested under an urban propagation environment conditions, in which the original probabilistic channel model approximation has been taken into account. Moreover, this paper identifies the interference issue of such an aerial network as a shrinkage or distortion phenomenon.