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The aim of this thesis is to advance the control and estimation schemes for multirotors, and more specifically the Aerial Robotic Worker, in order to progress towards the necessary control and estimation performance for robust control, cooperation and collaboration. Towards this envisioned aim, this Licentiate thesis will present the following main research contributions: a) a singularity-free attitude controller for the attitude problem has been established, that does not have the inherent drawbacks of Euler angle or Direction Cosine Matrix based approaches, b) a generalized estimation scheme for attitude, position and parameter estimation will be presented that has the merit of low computational footprint, while it is robust towards magnetic disturbances and able to identify key parameters in the model of an Aerial Robotic Worker, c) an method for estimating the induced vibration frequencies on the multirotor’s frame, and the respective amplitudes, that relies on notch filtering for attenuating the induced vibrations, and d) a theoretical establishment, as well as an experimental development and evaluation of a variable pitch propeller model to add additional degrees of freedom and increase the robustness of an Aerial Robotic Worker. In the first part of this thesis the main contributions of the previous research approaches will be highlighted, while in the second part of the thesis the corresponding and in full detail articles will be presented.

Uncrewed aerial vehicles (UAVs), commonly known as drones, have become increasingly prevalent in various applications. However, the localization and payload detection of drones is crucial for ensuring safety and security. This thesis proposes a vision-based solution using deep learning techniques to address these challenges. Existing solutions like radars and acoustic sensors have limitations, including high costs, limited accuracy, and the need for prior knowledge of the drone's model. Normal radars lack angle estimation accuracy and rely on known micro-Doppler features for payload detection, while acoustic sensors are restricted to close ranges for payload analysis. In contrast, cameras offer a cost-effective alternative as they have become widely available and can capture visual data. In addition, due to resource constraints, mounting multiple sensors on the UAV along with the camera is impractical, making reliance on cameras alone essential for addressing the mentioned problems. Recent advancements in deep learning algorithms enable regression and classification tasks, making vision data a promising choice for solving drone localization and payload detection problems. The proposed solution leverages convolutional neural networks (CNNs) for regression tasks, estimating the distance of a drone from the captured image. The CNN takes a cropped image within the drone's bounding box as input and outputs the estimated distance. Additionally, the drone's azimuth and elevation angles have been estimated based on its position in the captured image using a simple pinhole model for the camera. Also, the ResNet and EfficientNet classifiers could accurately classify drones as loaded or unloaded, even without prior knowledge of their shape. Due to a scarcity of publicly available datasets, this study developed the first air-to-air simulated dataset specifically for the classification of loaded versus unloaded drones. To evaluate the performance of the proposed solution, both simulated and experimental tests were conducted. The results showcased promising accuracy, with a root mean square error (RMSE) of less than 10 meters for distance estimation and an RMSE of less than 3 degrees for angle estimation. Furthermore, the payload detection problem was effectively addressed, achieving a classification accuracy of over 85\% for distinguishing between loaded and unloaded drones using the trained network based on the simulated dataset. The numerical highlights demonstrate the effectiveness of using camera sensors for 3D localization, with accurate distance and angle estimations. The high accuracy achieved in payload classification showcases the potential of the proposed solution for detecting drone payloads at distances up to 100 meters. These results pave the way for enhanced safety and security in drone environments.

This research study focuses on solving the deployment planning problem for UAV-BSs using Multi-Objective Evolutionary Algorithms (MOEAs). The main research objectives encompass gridbased modelling of the target area, investigating evolution parameters, and evaluating algorithm performance in diverse deployment scenarios. Cost, coverage, and interference are considered as objectives along with specific constraints to generate optimal deployment plans. The solution incorporates objective decision support for selecting the best solution among the Pareto front. The research also accounts for parameter initialization and UAV network heterogeneity. Through comprehensive evaluations, the proposed solution demonstrates computational efficiency and the ability to generate satisfactory deployment plans. The study recommends using NonDominated Sorting Genetic Algorithm-II (NSGA-II) for optimal performance. The research also incorporates a fitness approximation technique to reduce computational time while maintaining solution quality. The findings provide valuable insights and recommendations for efficient and balanced deployment planning. However, the research acknowledges limitations and suggests future enhancements. Overall, this research contributes to the field by establishing a foundation for robust and practical deployment plans, guiding future advancements. Future research should focus on addressing identified limitations to enhance applicability and effectiveness in real-world deployment scenarios.

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This thesis aimed to develop a rendezvous and proximity operation simulation to be tested with physical sensors and hardware, in order to assess the fidelity and performance of low-cost off-the-shelf systems for a hardware-in-the-loop testbed. With the push towards complex autonomous rendezvous missions, a low barrier to entry spacecraft simulator platform allows researchers to test and validate robotics systems, sensors, and algorithms for space applications, without investing in multimillion dollar equipment. This thesis conducted drone flights that followed a representative rendezvous trajectory while collecting lidar data of a target spacecraft model with a lidar sensor affixed to the drone. A relative orbital motion simulation tool was developed to create trajectories of varying orbits and initial conditions, and a representative trajectory was selected for use in drone flights. Two 3D tracking systems, OptiTrack and Vive, were assessed during these flights. OptiTrack is a high-cost state-of-the-art motion capture system that performs pose estimation by tracking reflective markers on a target in the tracking area. Vive is a lower-cost tracking system whose base stations emit lasers for its tracker to detect. Data collection by two lidar types was also assessed during these flights: real lidar data from a physical sensor, and virtual lidar data from a virtual sensor in a virtual environment. Drone flights were therefore performed in these four configurations of tracking system and lidar type, to directly compare the performance of higher-cost configurations with lower-cost configurations. The errors between the tracked drone position time history and the target position time history were analyzed, and the low-cost Vive and real lidar configuration was demonstrated to provide comparable error to the OptiTrack and real lidar configuration because of the dominance of the drone controller error over the tracking system error. In addition, lidar data of a target satellite model was collected by real and virtual lidar sensors during these flights, and point clouds were successfully generated. The resulting point clouds were compared by visualizing the data and noting the characteristics of real lidar data and its error, and how it compared to idealized virtual lidar data of a virtual target satellite model. The resulting real-world data characteristics were found to be modellable which can then be used for more robust simulation development within virtual reality. These results demonstrated that low-cost and open-source hardware and software provide satisfactory results for simulating this kind of spacecraft mission and capturing useful and usable data. / Master of Science / As space missions become more complex, there is a need for lower-cost, more accessible spacecraft simulation platforms that can test and validate hardware and software on the ground for a space-based mission. In this thesis, two position tracking systems and two lidar data collection types were assessed to see if the performance of a low-cost tracking system was comparable to a high-cost tracking system for a space-based simulation. The tracking systems tested were the high-cost state-of-the-art OptiTrack system and the low-cost Vive system. The two types of lidar data collected were real lidar from a physical sensor and virtual lidar from a virtual sensor. These assessments were performed in four configurations, to test each configuration of tracking system and lidar type. First, a simulation tool was developed to simulate the orbital dynamics of a spacecraft that operates in proximity to another spacecraft. After choosing an orbit and initial conditions that represent one such potential mission, the resulting trajectory was uploaded to a drone which acted as a surrogate for a spacecraft, and it flew the uploaded route around a model satellite, collecting lidar data in the process with a lidar sensor affixed to the drone. The tracking systems provided the drone with its position data, and the lidar sensor on the drone collected lidar data of a model satellite as it flew. The data revealed that the low-cost tracking system performance was comparable to the high-cost tracking system because the drone's controller error dominated over the tracking system errors. Additionally, the low-cost drone and physical lidar sensor generated high quality point cloud data that captured the geometry of the target satellite and illustrated the characteristics of real-world lidar data and its errors. These results demonstrated that low-cost and open-source hardware and software provide satisfactory results for simulating this kind of spacecraft mission and capturing useful and usable data.

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The current focus on the Internet of Things (IoT) has led to the emergence of many network scenarios with unlimited use cases, including smart homes, smart cities, smart agriculture, and more. Unmanned aerial vehicles (UAVs), also known as drones, have become increasingly popular due to their versatility and ability to collect and transmit data through various sensors and cameras. With real-time data transmission, autonomy, and cost-effectiveness, UAVs have become valuable tools for different applications, including disaster management, agriculture monitoring, and remote area control. Low-power wide-area network (LPWAN) technology plays a crucial role in enabling IoT, and LoRaWAN is one of the specific LPWAN communication technologies that can provide low power consumption and coverage over a wide range. During a catastrophe, wireless communication is critical for analyzing damaged regions, coordinating rescue and relief team actions, saving lives, and reducing economic losses. UAVs can partially replace damaged or overloaded wireless networks as an alternative wireless network provider. This thesis aimed to simulate a LoRa network and investigate the relationship between the UAV coverage radius and elevation angle, as well as the effect of multipath distortion and signal attenuation on UAV and user distance. By calculating signal-to-noise ratio (SNR) and bit error rate (BER) for LoRa in a line-of-sight (LoS) and non-line-of-sight (NLoS) environment, we provided a comprehensive analysis of LoRaWAN performance in real-life environments for long distances. The results indicate that LoRaWAN communication is reliable in various environments, making it a promising technology for emergency and medical communications. / Det nuvarande fokuset på Internet of Things (IoT) har lett till uppkomsten av många nätverksscenarier med obegränsade användningsfall, inklusive smarta hem, smarta städer, smart jordbruk och mer. Obemannade flygfarkoster (UAV), även kända som drönare, har blivit allt populärare på grund av deras mångsidighet och förmåga att samla in och överföra data genom olika sensorer och kameror. Med realtidsdataöverföring, autonomi och kostnadseffektivitet har UAVs blivit värdefulla verktyg för olika applikationer, inklusive katastrofhantering, jordbruksövervakning och fjärrkontroll av områden. Low-power wide-area network (LPWAN)-teknik spelar en avgörande roll för att möjliggöra IoT, och LoRaWAN är en av de specifika LPWAN-kommunikationsteknikerna som kan ge låg strömförbrukning och täckning över ett brett spektrum. Under en katastrof är trådlös kommunikation avgörande för att analysera skadade regioner, koordinera räddnings- och hjälpteams åtgärder, rädda liv och minska ekonomiska förluster. UAV:er kan delvis ersätta skadade eller överbelastade trådlösa nätverk som en alternativ leverantör av trådlöst nätverk. Detta examensarbete syftade till att simulera ett LoRa-nätverk och undersöka sambandet mellan UAV-täckningsradien och höjdvinkeln, såväl som effekten av flervägsdistorsion och signaldämpning på UAV och användaravstånd. Genom att beräkna signal-brusförhållande (SNR) och bitfelsfrekvens (BER) för LoRa i en siktlinje (LoS) och icke-siktlinje (NLoS) miljö, gav vi en omfattande analys av LoRaWAN prestanda i verkliga miljöer för långa avstånd. Resultaten indikerar att LoRaWAN-kommunikation är tillförlitlig i olika miljöer, vilket gör den till en lovande teknik för akut- och medicinsk kommunikation.

<p>The growing popularity and increased accessibility of unmanned aerial vehicles (UAVs) have raised concerns about potential threats they may pose. In response, researchers have devoted significant efforts to developing UAV detection and classification systems, utilizing diverse methodologies such as computer vision, radar, radio frequency, and audio-based approaches. However, the availability of publicly accessible UAV audio datasets remains limited. Consequently, this research endeavor was undertaken to address this gap by undertaking the collection of a comprehensive UAV audio dataset, alongside the development of a precise and efficient audio-based UAV classification system.</p> <p>This research project is structured into three distinct phases, each serving a unique purpose in data collection and training the proposed UAV classifier. These phases encompass data collection, dataset evaluation, the implementation of a proposed convolutional neural network, training procedures, as well as an in-depth analysis and evaluation of the obtained results. To assess the effectiveness of the model, several evaluation metrics are employed, including training accuracy, loss rate, the confusion matrix, and ROC curves.</p> <p>The findings from this study conclusively demonstrate that the proposed CNN classi- fier exhibits nearly flawless performance in accurately classifying UAVs across 22 distinct categories.</p>

The details of an experimental investigation focusing on obtaining the static and vibration characteristics of a full-scale carbon composite wing and fuselage structural assemblies of an ultralight unmanned aerial vehicle (UAV) are presented. The UAV has a total empty weight of 155-lb and an overall length of approximately 20.6t. A three-tier whiffletree system and the tail fixture were designed and used to load the wing and the fuselage in a manner consistent with a high-g flight condition. A shaker-table approach was used for the wing vibration testing, whereas the modal characteristics of the fuselage structure were determined for a freeree configuration. The static responses of the both structures under simulated loading conditions as well as their dynamic properties such as the natural frequency, damping coefficient and associated mode shapes were obtained. The design and implementation of the static and vibration tests along with the experimental results are presented in this thesis.