This thesis presents the design of an autopilot capable of flying small unmanned aerial vehicles with wingspans less then 21 inches. The autopilot is extremely small and lightweight allowing it to fit in aircraft of this size. The autopilot features an advanced, highly autonomous flight control system with auto-launch and auto-landing algorithms. These features allow the autopilot to be operated by a wide spectrum of skilled and unskilled users. Innovative control techniques implemented in software, coupled with light weight, robust, and inexpensive hardware components were used in the design of the autopilot.

Though still in relative infancy, the field of flapping flight has potential to have a far-reaching impact on human life. Nature presents a myriad of examples of successful uses of this locomotion. Human efforts in flapping flight have seen substantial improvement in recent times. Wing kinematics are a key aspect of this study. This study summarizes previous wing trajectory generators and presents a new trajectory generation method built upon previous methods. This includes a novel means of commanding unequal half-stroke durations subject to robotic trajectory continuity requirements. Additionally, previous optimization methods are improved upon. Experimental optimization is performed using the new trajectory generation method and a more traditional means. Methods for quantifying and compensating for sensor time-dependence are also discussed. Results show that the Polar Fourier Series trajectory generator advanced rapidly through the optimization process, especially during the initial phase of experimentation. The Modified Berman and Wang trajectory generator moved through the design space more slowly due to the increased number of kinematic parameters. When optimizing lift only, the trajectory generators produced similar results and kinematic forms. The findings suggest that the objective statement should be modified to reward efficiency while maintaining a certain amount of lift. It is expected that the difference between the capabilities of the two trajectory generators will become more apparent under such conditions.

Coalitions are collections of agents that join together to solve a common problem that either cannot be solved individually or can be solved more efficiently as a group. Each individual agent has capabilities that can benefit the group when working together as a coalition. Typically, individual capabilities are joined together in an additive way when forming a coalition. This work will introduce a new operator that is used when combining capabilities, and suggest that the behavior of the operator is contextual, depending on the nature of the capability itself. This work considers six different capabilities of Unmanned Air Vehicles (UAV) and determines the nature of the new operator in the context of each capability as coalitions (squadrons) of UAVs are formed. Coalitions are formed using three different search algorithms, both with and without heuristics: Depth-First, Depth-First Iterative Deepening, and Genetic Algorithm (GA). The effectiveness of each algorithm is evaluated. Multi agent-based UAV simulation software was developed and used to test the ideas presented. In addition to coalition formation, the software aims to address additional multi-agent issues such as agent identity, mutability, and communication as applied to UAV systems, in a realistic simulated environment. Social potential fields provide a means of modeling a clustering attractive force at the same time as a collision-avoiding repulsive force, and are used by the simulation to maintain aircraft position relative to other UAVs.

Unmanned Aerial Vehicles (UAVs) provides a platform for many automated tasks and with an ever increasing advances in computing, these tasks can be more complex. The use of UAVs is expanded in this thesis with the goal of Search and Rescue (SAR), where a UAV can assist fast responders to search for a lost person and relay possible search areas back to SAR teams. To identify a person from an aerial perspective, low-level Histogram of Oriented Gradients (HOG) feature descriptors are used over a segmented region, provided from thermal data, to increase classification speed. This thesis also introduces a dataset to support a Bird’s-Eye-View (BEV) perspective and tests the viability of low level HOG feature descriptors on this dataset. The low-level feature descriptors are known as Boosted Histogram of Oriented Gradients (BHOG) features, which discretizes gradients over varying sized cells and blocks that are trained with a Cascaded Gentle AdaBoost Classifier using our compiled BEV dataset. The classification is supported by multiple sensing modes with color and thermal videos to increase classification speed. The thermal video is segmented to indicate any Region of Interest (ROI) that are mapped to the color video where classification occurs. The ROI decreases classification time needed for the aerial platform by eliminating a per-frame sliding window. Testing reveals that with the use of only color data iv and a classifier trained for a profile of a person, there is an average recall of 78%, while the thermal detection results with an average recall of 76%. However, there is a speed up of 2 with a video of 240x320 resolution. The BEV testing reveals that higher resolutions are favored with a recall rate of 71% using BHOG features, and 92% using Haar-Features. In the lower resolution BEV testing, the recall rates are 42% and 55%, for BHOG and Haar-Features, respectively.

In wireless communications, employing pilot symbols enables to estimate the state of the wireless channel at the expense of decreasing the number of symbols available for transmitting data. Addressing this trade-off is particularly challenging when the channel changes rapidly over time, since channel estimates become obsolete over short transmission periods. This master thesis proposes an analytical model to characterize this trade-off and develops an algorithm to find the near-optimal pilot spacing in terms of the achieved over spectral efficiency. This algorithm is simulated in a cellular system that serves uncrewed aerial vehicles. Numerical results indicate that the altitude of the uncrewed aerial vehicle, the Rician factor, the Doppler frequency, and the number of receive antennas influence the overall spectral efficiency and consequently, pilot spacing should take into account these system parameters. / I trådlös kommunikation möjliggör användning av pilotsymboler att uppskatta tillståndet för den trådlösa kanalen på bekostnad av att minska antalet tillgängliga symboler för att överföra data. Att ta itu med denna avvägning är särskilt utmanande när kanalen ändras snabbt över tiden, eftersom kanaluppskattningar blir föråldrade under korta överföringsperioder. Denna masteruppsats föreslår en analytisk modell för att karakterisera denna avvägning och utvecklar en algoritm för att hitta det närmast optimala pilotavståndet i termer av uppnådd över spektral effektivitet. Denna algoritm simuleras i ett cellulärt system som betjänar obemannade flygfarkoster. Numeriska resultat indikerar att höjden för det obemannade luftfartyget, Rician-faktorn, Dopplerfrekvensen, antalet mottagarantenner påverkar den totala spektrala effektiviteten och följaktligen bör pilotavståndet ta hänsyn till dessa systemparametrar.

No description available.

This thesis aims to analyze how object detectors perform under winter weather conditions, specifically in areas with varying degrees of snow cover. The investigation will evaluate the effectiveness of commonly used object detection methods in identifying vehicles in snowy environments, including YOLO v8, Yolo v5, and Faster R-CNN. Additionally, the study explores the method of labeling vehicle objects within a set of image frames for the purpose of high-quality annotations in terms of correctness, details, and consistency. Training data is the cornerstone upon which the development of machine learning is built. Inaccurate or inconsistent annotations can mislead the model, causing it to learn incorrect patterns and features. Data augmentation techniques like rotation, scaling, or color alteration have been applied to enhance some robustness to recognize objects under different alterations. The study aims to contribute to the field of deep learning by providing valuable insights into the challenges of detecting vehicles in snowy conditions and offering suggestions for improving the accuracy and reliability of object detection systems. Furthermore, the investigation will examine edge devices' real-time tracking and detection capabilities when applied to aerial images under these weather conditions. What drives this research is the need to delve deeper into the research gap concerning vehicle detection using drones, especially in adverse weather conditions. It highlights the scarcity of substantial datasets before Mokayed et al. published the Nordic Vehicle Dataset. Using unmanned aerial vehicles(UAVs) or drones to capture real images in different settings and under various snow cover conditions in the Nordic region contributes to expanding the existing dataset, which has previously been restricted to non-snowy weather conditions. In recent years, the leverage of drones to capture real-time data to optimize intelligent transport systems has seen a surge. The potential of drones in providing an aerial perspective efficiently collecting data over large areas to precisely and timely monitor vehicular movement is an area that is imperative to address. To a greater extent, snowy weather conditions can create an environment of limited visibility, significantly complicating data interpretation and object detection. The emphasis is set on edge devices' real-time tracking and detection capabilities, which in this study introduces the integration of edge computing in drone technologies to explore the speed and efficiency of data processing in such systems.

The use of unmanned aerial vehicles (UAVs) is well established in the military sector with great advantages in modern warfare. The concept of using UAV swarms has been discussed over two decades, but is now seeing its first real system used by the Israel defence forces. There is no exact definition what a swarm is, but it is proposed that it should satisfy the following three requirements. A swarm should have limited human control, the number of agents in a swarm should be at least three and the agents in the swarm should cooperate to perform common tasks. The complexity of controlling multiple autonomous UAVs gives way to the problem of how to take advantage of the operators cognitive and tactical abilities to control a swarm to effectively conduct military reconnaissance missions. The method of using behaviour trees as a control structure was derived from previous work in swarm systems. A behaviour tree is a structured way of organising and prioritising actions of autonomous systems. Behaviour trees are similar to finite state machines (FSMs) with the advantages of being modular, reactive, and with better readability. Three different behaviour trees with increasing complexity was created and simulated in the game engine Unity. A fourth more real life behaviour tree was created and used as a basis for discussing the strength and weaknesses of using behaviour trees against previous work. Using behaviour tree as a unifying structure for creating a swarm that integrates the tactical abilities of an operator with the strength of an autonomous swarm seems promising. The proposed method of using behaviour trees i suggested to be used as a platform for discussing the swarm desired functions and to create a common vision for both operators and engineers how a swarm should function. / Användning av drönare är väletablerad inom det militära och ger stora fördelar i dagens moderna krigsföring. Idén om att använda en svärm av drönare har diskuterats under de senaste två decennierna, men först nu sett sin första riktiga system som använts av Israels försvarsmakt. Det finns ingen exakt definition av vad en svärm är, men det har föreslagits att en svärm ska uppfylla de följande tre kraven. En svärm ska ha begränsad mänsklig interaktion, antalet agenter ska vara minst tre och svärmen ska samarbeta för att lösa gemensamma uppgifter. Svårigheterna med att styra en autonom svärm ger upphov till hur man ska utnyttja en operatörs kognitiva och taktiska förmåga för att styra en autonom drönarsvärm för att effektivt utföra militära spaningsuppdrag. Utifrån tidigare arbete inom styrning av svärmar verkade beteende träd som en lovande metod. Beteendeträd är ett strukturerat sätt att organisera och prioritera beteenden för ett autonomt system. Beteendeträd har många likheter med ändliga tillståndsmaskiner, men fördelarna att vara modulära, responsiva och mer lättläsliga. Tre olika beteendeträd med ökande komplexitet skapades och deras funktionalitet simulerades i Unity. Ett fjärde mer verklighetstroget beteendeträd skapades och användes som underlag för att diskutera beteendeträds styrkor och svagheter i jämförelse med tidigare arbeten. Användningen av beteendeträd för att förena den mänskliga operatören med det autonoma systemet verkar lovande. Den föreslagna metoden att använda beteendeträd för att styra en svärm är tänkt att användas som ett gemensamt underlag för att möjliggöra att operatörer och ingenjörer kan ha en gemensam bild hur en svärm ska fungera.

A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems as well as novel AoI-aware scheduling policies accounting for the energy constraints at the transmitter nodes (for several settings of communication networks) in the process of decision-making using tools from optimization theory and reinforcement learning. The first part of this dissertation develops a stochastic hybrid system (SHS)-based general framework to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. First, we study a general setting of status updating systems, where a set of source nodes provide status updates about some physical process(es) to a set of monitors. For this setting, the continuous state of the system is formed by the AoI/age processes at different monitors, the discrete state of the system is modeled using a finite-state continuous-time Markov chain, and the coupled evolution of the continuous and discrete states of the system is described by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we derive a system of linear equations for the characterization of the joint moment generating function (MGF) of an arbitrary set of age processes in the network. Afterwards, we study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. For this setup, we develop SHS-based methods that allow the characterization of higher-order marginal/joint moments of the age processes in the network. Finally, our SHS-based framework is applied to derive the stationary marginal and joint MGFs for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which radio frequency (RF)-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables. / Doctor of Philosophy / A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation first develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems. Afterwards, using tools from optimization theory and reinforcement learning, novel AoI-aware scheduling policies are developed while accounting for the energy constraints at the transmitter nodes for several settings of communication networks, including unmanned aerial vehicles (UAVs)-assisted and radio frequency (RF)-powered communication networks, in the process of decision-making. In the first part of this dissertation, a stochastic hybrid system (SHS)-based general framework is first developed to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. Afterwards, this framework is applied to derive the stationary marginal and joint moment generating functions (MGFs) for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of a UAV as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which RF-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables.

The maneuverability and freedom provided by unmanned aerial vehicles (UAVs) make these an interesting choice for transporting objects in settings such as search and rescue operations, construction, and smart factories. A commonly proposed method of transport is by using cables attached between each UAV and the payload. However, the geometrical constraints posed by these attachments typically result in a system with highly complex dynamics. Although not an issue for conventional PID control schemes, these complex dynamics make the direct application of model predictive controllers (MPCs) infeasible for real-time usage. For this reason, much of the previous work has focused on treating the payload as a disturbance, thereby losing the ability to predict its effect on the UAVs. Contrary to this, this thesis presents an MPC that both captures the dynamics of the payload, and is capable of real-time usage. This is made possible by a parametrized linearization of the original system, and results in greatly improved performance compared to the disturbance model approach. The controller is derived for a system with two UAVs that transport a bar-like payload and verified both in simulations and physical experiments. The resulting control system is able track a multitude of setpoints, including rotations of both payload and UAVs, as well as lateral translations. Furthermore, it is able to attenuate external disturbances well, and dampens and prevents oscillations more efficiently when compared to the disturbance based approach. The resulting MPC solving time is on the order of milliseconds. Additionally, an initial attempt to decentralize the system is made, and the resulting controller experimentally tested on the UAV–bar system, resulting in a lower MPC solving time (2:5 times faster on average), but worsened performance in terms of position tracking of the bar. / Den manövrerbarhet och frihet som möjliggörs av användandet utav obemannade luftfarkoster (drönare) gör dessa till tämligen intressanta kandidater för lasttransport inom områden såsom sök- och räddningsuppdrag, byggnadskonstruktion och s.k. smarta fabriker. En vanligen förespråkad transportmetod består utav att förse systemet med kablar som fästs mellan last och drönare. De geometriska restriktioner som denna lastkoppling innebär resulterar emellertid ofta i system med väldigt komplicerad dynamik och interaktionskrafter. Även om detta inte innebär något problem för konventionella PID reglersystem så omöjliggör detta det direkta applicerandet utav modellprediktiv reglering (MPC) för realtidsbruk. Av denna anledning har tidigare verk fokuserat på att behandla lasten och dess inverkan på drönarna som en störning, men med detta därmed förlorat möjligheten att förutspå dess effekt på drönarna. I kontrast till detta, kommer det i detta verk att presenteras en MPC som både fångar lastens dynamik och är snabb nog för realtidsanvändning. Detta görs möjligt utav en parametriserad linjärisering utav originalsystemet och ger märkbart bättre resultat än den störningbaserade modellen. Reglersystemet appliceras på ett system bestående utav två drönare och en stång-liknande last och resultatet verifieras både i form av numeriska simuleringar och fysiska experiment. Det resulterande systemet klarar av både rotationer utav last och drönare samt translationer i alla riktningar. Dessutom är systemet kapabelt att hantera externa störningar och både dämpar och förhindrar oscillationer bättre i jämförelse med reglersystem baserat på störningsmodeller. Lösningstiden för MPC-regulatorn är i storleksordningen millisekunder. Utöver detta görs ett initialt försök i att decentralisera tidigare nämnda MPC och det resulterande reglersystemet utvärderas experimentellt på samma drönarsystem som tidigare. Detta resulterar i en lägre lösningstid (2.5 ggr snabbare i genomsnitt), men även i försämrad prestanda med avseende på reglering av stångens position.