There is an ever growing need for environmentally sustainable alternatives in today's society due to the looming threat of greenhouse gasses. One field where the need for new environmentally friendly solutions is needed is the aviation industry. The problem the industry is facing is due to the weight and space constraints that exist in aerial vehicles. In this bachelor project a solution for unmanned drones is proposed where it is powered by a hybrid solution consisting of batteries working together with fuel cells. The batteries compliment each other where the fuel cell is a lightweight energy source while the battery is used to combat the changing power demand. This project was done in collaboration with the Green Raven project to evaluate the optimal setup to power the energy system for an hour. The work was done theoretically in Matlab and Simulink to find the optimal system. From these simulations, data was collected to calculate the optimal configuration between batteries and amount of hydrogen stored in the Hydrogen tank. It was concluded that the best option to store the hydrogen was in a 2 liter tank at 300 bar together with 2 additional batteries with the capacity of 4000 mAh. This setup was concluded as the best option as it used up all hydrogen and landed with less charge in the battery than at the start point. / I takt med den globala uppvärmningen så växer behovet av klimatmedvetna hållbara lösningar. Ett område i stort behov av innovation är flygindustrin som länge varit en av de största klimatbovarna. Flygindustrin stora problem är att dess fordon både har begränsad volym och vikt. I detta kandidatexamensarbete kommer vi diskutera en hybridlösning där obemannade drönare drivs av en hybridlösning där batterier tillsammans med bränsleceller driver drönaren. Batterierna och bränslecellerna komplimenterar varandra då bränslecellerna är är lättviktiga och tillför en stabil produktion av ström till drönaren medan batterierna agerar komplement och hjälper till när det behövs extra kraft. Projektet som i samarbete med The Green Raven project utfördes för att utvärdera det optimala systemet för att förse drönaren nog med kraft i en timme. Projektet har utförts teoretiskt i Matlab och Simulink för att hitta den optimala balansen mellan batterier och bränsleceller. Från dessa simuleringar samlades data in för att optimera konfigurationen mellan bränslecellerna och batterierna. Från resultaten drogs slutsatsen att 2 batterier med en kapacitet på 4000 mAh som tillsammans med vätgas som förvarades i en 2 liter tank med ett tryck på 300 bar var den bästa konfigurationen. Denna lösning ansågs som den bästa då all vätgas förbrukades under simulation och att batteriet vid stopp hade en lägre laddning än vid flygstart.

<p>This thesis explores the usage of machine learning (ML) algorithms and software-defined radio (SDR) hardware for the detection of signal jamming and message injection cyberattacks against unmanned aerial vehicle (UAV) wireless communications. In the first work presented in this thesis, a real-time ML solution for classifying four types of jamming attacks is proposed for implementation with a UAV using an onboard Raspberry Pi computer and HackRF One SDR. Also presented in this thesis is a multioutput multiclass convolutional neural network (CNN) model implemented for the purpose of identifying the direction in which a jamming sample is received from, in addition to detecting and classifying the jamming type. Such jamming types studied herein are barrage, single-tone, successive-pulse, and protocol-aware jamming. The findings of this chapter forms the basis of a reinforcement learning (RL) approach for UAV flightpath modification as the next stage of this research. The final work included in this thesis presents a ML solution for the binary classification of three different message injection attacks against ADS-B communication systems, namely path modification, velocity drift and ghost aircraft injection attacks. The collective results of these individual works demonstrate the viability of artificial-intelligence (AI) based solutions for cybersecurity applications with respect to UAV communications.</p>

Target tracking is an important part of video surveillance from a UAV. Tracking a target in an urban environment can be difficult because of the number of occlusions present in the environment. If multiple UAVs are used to track a target and the target behavior is learned autonomously by the UAV then the task may become easier. This thesis explores the hypothesis that an existing cooperative control algorithm can be enhanced by a language modeling algorithm to improve over time the target tracking performance of one or more ground targets in a dense urban environment. Observations of target behavior are reported to the Sequence Memoizer which uses the observations to create a belief model of future target positions. This belief model is combined with a kinematic belief model and then used in a cooperative auction algorithm for UAV path planning. The results for tracking a single target using the combined belief model outperform other belief models and improve over the duration of the mission. Results from tracking multiple targets indicate that algorithmic enhancements may be needed to find equivalent success. Future target tracking algorithms should involve machine learning to enhance tracking performance.

<p>In this work, machine learning (ML) modeling is proposed for the detection and classification of global positioning system (GPS) spoofing in unmanned aerial vehicles (UAVs). Three testing scenarios are implemented in an outdoor yet controlled setup to investigate static and dynamic attacks. In these scenarios, authentic sets of GPS signal features are collected, followed by other sets obtained while the UAV is under spoofing attacks launched with a software-defined radio (SDR) transceiver module. All sets are standardized, analyzed for correlation, and reduced according to feature importance prior to their exploitation in training, validating, and testing different multiclass ML classifiers. Two schemes for the dataset are proposed, location-dependent and location-independent datasets. The location-dependent dataset keeps the location specific features which are latitude, longitude, and altitude. On the other hand, the location-independent dataset excludes these features. The resulting performance evaluation of these classifiers shows a detection rate (DR), misdetection rate (MDR), and false alarm rate (FAR) better than 92%, 13%, and 4%, respectively, together with a sub-millisecond detection time. Hence, the proposed modeling facilitates accurate real-time GPS spoofing detection and classification for UAV applications.</p> <p><br></p> <p>Then, a three-class ML model is implemented on a UAV with a Raspberry Pi processor for classifying the two GPS spoofing attacks (i.e., static, dynamic) in real-time. First, several models are developed and tested utilizing the prepared dataset. Models evaluation is carried out using the DR, F-score, FAR, and MDR, which all showed an acceptable performance. Then, the optimum model is loaded to the onboard processor and tested for real-time detection and classification. Location-dependent applications, such as fixed-route public transportation, are expected to benefit from the methodology presented herein as the longitude, latitude, and altitude features are characterized in the implemented model.</p>

Unmanned Aerial Vehicles (UAVs) can navigate in indoor environments and through environments that are hazardous or hard to reach for humans. This makes them suitable for use in search and rescue missions and by emergency response and law enforcement to increase situational awareness. However, even for an experienced UAV tele-operator controlling the UAV in these situations without colliding into obstacles is a demanding and difficult task. This thesis presents a human-UAV interface along with a collision avoidance method, both optimized for a human tele-operator. The objective is to simplify the task of navigating a UAV in indoor environments. Evaluation of the system is done by testing it against a number of use cases and a user study. The results of this thesis is a collision avoidance method that is successful in protecting the UAV from obstacles while at the same time acknowledges the operator’s intentions. / Obemannad luftfarkoster (UAV:er) kan navigera i inomhusmiljöer och genom miljöer som är farliga eller svåra att nå för människor. Detta gör dem lämpliga för användning i sök- och räddningsuppdrag och av akutmottagning och rättsväsende genom ökad situationsmedvetenhet. Dock är det även för en erfaren UAV-teleoperatör krävande och svårt att kontrollera en UAV i dessa situationer utan att kollidera med hinder. Denna avhandling presenterar ett människa-UAV-gränssnitt tillsammans med en kollisionsundvikande metod, båda optimerade för en mänsklig teleoperatör. Målet är att förenkla uppgiften att navigera en UAV i inomhusmiljöer. Utvärdering av systemet görs genom att testa det mot ett antal användningsfall och en användarstudie. Resultatet av denna avhandling är en kollisionsundvikande metod som lyckas skydda UAV från hinder och samtidigt tar hänsyn till operatörens avsikter.

Obstacle avoidance is a well researched area, however most of the works only consider a 2D environment. Drones can move in three dimensions. It is therefore of interest to develop a system that ensures safe flight in these three dimensions. Obstacle avoidance is of highest importance for drones if they are intended to work autonomously and around humans, since drones are often fragile and have fast moving propellers that can hurt humans. This project is based on the obstacle restriction algorithm in 3D, and uses OctoMap to conveniently use the sensor data from multiple sensors simultaneously and to deal with their limited field of view. The results show that the system is able to avoid obstacles in 3D. / Hinderundvikande är ett utforskat område, dock för det mesta har forskningen fokuserat på 2D-miljöer. Eftersom drönare kan röra sig i tre dimensioner är det intressant att utveckla ett system som garanterar säker rörelse i 3D. Hinderundvikande är viktigt för drönare om de ska arbeta autonomt runt människor, eftersom drönare ofta är ömtåliga och har snabba propellrar som kan skada människor. Det här projektet är baserat på Hinderrestriktionsmetoden (ORM), och använder OctoMap för att använda information från många sensorer samtidigt och för att hantera deras begränsade synfält. Resultatet visar att systemet kan undvika hinder i 3D.

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 (UAV), often referred to as drones, have seen increased use in search and rescue (SAR) missions. Traditionally, these missions involve manual control of each drone for aerial surveillance. As UAV autonomy progresses, the next phase in drone technology consists of a shift to autonomous collaborative multi-drone operations, where drones function collectively in swarms. A significant challenge lies in designing user interfaces that can effectively support UAV pilots in their mission without an overload of information from each drone and of their surroundings. This thesis evaluates important human factors, such as situational awareness (SA) and cognitive workload, within complex search and rescue scenarios, with the goal of increasing trust in multi-drone systems through the design and testing of various components. Conducting these user studies aims to generate insights for the future design of multi-drone systems. Two prototypes were developed with a multi-drone user interface, and simulated a stressful search and rescue mission with high cognitive workload. In the second prototype, a heatmap guided UAV pilots based on the lost person model. The prototypes were tested in a conducted user study with experienced UAV pilots in different SAR organizations across Sweden. The results showed variability in SA while monitoring drone swarms, depending on user interface components and SA levels. The prototypes caused significant cognitive workload, slightly reduced in the heatmap-equipped prototype. Furthermore, there was a marginal increase in trust observed in the prototype with the heatmap. Notably, a lack of manual control raised challenges for the majority of participants and many desired features were suggested by participants. These early expert insights can serve as a starting point for future development of multi-drone systems. / The HERD project, supported by the Innovation Fund Denmark for the DIREC project (9142-00001B)

This thesis focuses on visual multiple-target tracking (MTT) from a UAV. Typical state-of-the-art multiple-target trackers rely on an object detector as the primary detection source. However, object detectors usually require a GPU to process images in real-time, which may not be feasible to carry on-board a UAV. Additionally, they often do not produce consistent detections for small objects typical of UAV imagery.In our method, we instead detect motion to identify objects of interest in the scene. We detect motion at corners in the image using optical flow. We also track points long-term to continue tracking stopped objects. Since our motion detection algorithm generates multiple detections at each time-step, we use a hybrid probabilistic data association filter combined with a single iteration of expectation maximization to improve tracking accuracy.We also present a motion detection algorithm that accounts for parallax in non-planar UAV imagery. We use the essential matrix to distinguish between true object motion and apparent object motion due to parallax. Instead of calculating the essential matrix directly, which can be time-consuming, we design a new algorithm that optimizes the rotation and translation between frames. This new algorithm requires only 4 ms instead of 47 ms per frame of the video sequence.We demonstrate the performance of these algorithms on video data. These algorithms are shown to improve tracking accuracy, reliability, and speed. All these contributions are capable of running in real-time without a GPU.

Drones flying in squad formation while interconnected in an ad-hoc fashion are called Flying Ad hoc Networks (FANETs). These FANETs are gathering special interests in the networking community in their deployment for different vital missions. Such missions include rescue missions in case of disasters, monitoring and border control, animal monitoring, crowd monitoring and management, etc. The main problems researched with FANETs are typically inherited from what has been done for mobile ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) earlier. One of the major problems is routing and forwarding gathered data towards the member(i.e., the drone) closest to the sink or the member that gateways to the Internet to reach the sink. Clustering the FANET nodes (i.e., the drones) is found to be a good solution for this problem. The preeminent contributions of this thesis include a novel grinding technique of the geolocation where FANET is deployed to perform certain tasks, a grid-based mobility model for UAVs, and extending the EMASS algorithm so that it can adapt to our proposed grid-based system. The result proves our mobility model’s superiority over one of the most used mobility models, Random walk.