This thesis addresses the problems of collision avoidance and coalition formation of multiple UAVs in high density traffic environments, proposes simple and efficient algorithms as solutions, and discusses their applications in multiple UAV missions. First, the problem of collision avoidance among UAVs is considered and deconfliction algorithms are proposed. The efficacy of the proposed algorithms is tested using simulations involving random flights in high density traffic. Further, the proposed collision avoidance algorithms are implemented using realistic six degree of freedom UAV models. The studies in this thesis show that implementation of the proposed collision avoidance algorithms leads to a safer and efficient operational airspace occupied by multiple UAVs. Next, coalition formation in a search and prosecute mission involving a large number of UAVs and targets is considered. This problem is shown to be NP-hard and a sub-optimal but polynomial time coalition formation strategy is proposed. Simulations are carried out to show that this coalition formation algorithm works well. The coalition formation algorithm is then extended to handle situations where the UAVs have limited communication ranges. Finally, this thesis considers some multiple UAV missions that require the application of collision avoidance and coalition formation techniques. The problem of multiple UAV rendezvous is tackled by using (i) a consensus among the UAVs to attain rendezvous and (ii) the collision avoidance algorithm previously developed for safety. The thesis also considers a search and prosecute mission where the UAVs also have to avoid collisions among one another. In summary, the main contributions of this thesis include (a) novel collision avoidance algorithms, which are conceptually simple and easy to implement, for resolving path conflicts – both planar and three dimensional – in a high density traffic airspace with UAVs in free flight and (b) efficient coalition formation algorithms for search and prosecute task with large number of UAVs and targets where UAVs have limited communication ranges and targets are maneuvering. Simulations to evaluate the performance of algorithms based on these concepts to carry out realistic tasks by UAV swarms are also given.

Unmanned Aerial Vehicles (UAVs) connected to cellular networks present novel challenges and opportunities in mobility management and localization, distinct from those faced by terrestrial users. This thesis presents an integrated approach, combining two key aspects essential for the integration of UAVs with cellular networks. Firstly, it introduces the mobility management challenges for cellular-connected UAVs, which differ significantly from terrestrial users. While terrestrial mobility management primarily aims to prevent radio link failures near cell boundaries, aerial users experience fragmented and overlapping coverage with line-of-sight conditions involving multiple ground base stations (BSs). Thus, mobility management for UAVs extends beyond link failure avoidance, aiming to minimize unnecessary handovers while ensuring extended service availability, particularly in up-link communication. Line-of-sight conditions from a UAV to multiple BSs increase the likelihood of frequent handovers, resulting in control packet overheads and communication delays. This thesis proposes two approaches to address these challenges: 1) A model-based service availability-aware Mobility Robustness Optimization (MRO) adapting handover parameters to maintain high service availability with minimal handovers, and 2) A model-free approach using Deep Q-networks to decrease unnecessary handovers while preserving high service availability. Simulation results demonstrate that both the proposed algorithms converge promptly and increase the service availability by more than 40 %  while the number of handovers is reduced by more than 50%  as compared to traditional approaches. Secondly, to assess the ability of a network to support the range-based localization for cellular-connected UAVs, an analytical framework is introduced. The metric B-localizability is defined as the probability of successfully receiving localization signals above a specified Signal-to-Interference plus Noise Ratio (SINR) threshold from at least B ground BSs. The framework, accounting for UAV-related parameters in a three-dimensional environment, provides comprehensive insights into factors influencing localizability, such as distance distributions, path loss, interference, and received SINR. Simulation studies explore the correlation between localizability and the number of participating BSs, SINR requirements, air-to-ground channel characteristics, and network coordination. Additionally, an optimization problem is formulated to maximize localizability, investigating the impact of UAV altitude across different scenarios. Our study reveals that in an urban macro environment, the effectiveness of cellular network-based localization increases with altitude, with localizability reaching 100% above 60 meters. This finding indicates that utilizing cellular networks for UAV localization is a viable option. / Unmanned Aerial Vehicles (UAV) anslutna till cellulära nätverk presenterar nya utmaningar och möjligheter inom mobilitetshantering och lokalisering, skilda från dem som markanvändare står inför. Denna avhandling presenterar ett integrerat tillvägagångssätt, som kombinerar två nyckelaspekter som är väsentliga för integrationen av UAV:er med cellulära nätverk. För det första introducerar den mobilitetshanteringsutmaningarna för mobilanslutna UAV:er, som skiljer sig avsevärt från markbundna användare. Medan markbunden mobilitetshantering i första hand syftar till att förhindra radiolänkfel nära cellgränser, upplever antennanvändare fragmenterad och överlappande täckning med siktlinjeförhållanden som involverar flera markbasstationer (BS). Mobilitetshantering för UAV sträcker sig sålunda bortom att undvika länkfel, och syftar till att minimera onödiga överlämningar samtidigt som man säkerställer utökad servicetillgänglighet, särskilt i upplänkskommunikation. Synlinjeförhållanden från en UAV till flera BS:er ökar sannolikheten för frekventa överlämningar, vilket resulterar i kontrollpaketkostnader och kommunikationsförseningar. Denna avhandling föreslår två tillvägagångssätt för att möta dessa utmaningar: 1) En modellbaserad tjänsttillgänglighetsmedveten Mobility Robustness Optimization (MRO) som anpassar parametrar för överlämning för att bibehålla hög servicetillgänglighet med minimal överlämning, och 2) Ett modellfritt tillvägagångssätt med Deep Q- nätverk för att minska onödiga överlämningar samtidigt som hög servicetillgänglighet bibehålls. Simuleringsresultat visar att båda de föreslagna algoritmerna konvergerar snabbt och ökar tjänstens tillgänglighet med mer än 40% medan antalet överlämningar minskas med mer än 50% jämfört med traditionella metoder. För det andra, för att bedöma förmågan hos ett nätverk att stödja den räckviddsbaserade lokaliseringen för de cellulärt anslutna UAV:erna, introduceras ett analytiskt ramverk.Metriska B-lokaliseringsförmågan definieras som sannolikheten för att framgångsrikt ta emot lokaliseringssignaler över en specificerad signal-till-interferens plus brusförhållande (SINR) tröskel från minst B jord BSs.Ramverket, som tar hänsyn till UAV-relaterade parametrar i en tredimensionell miljö, ger omfattande insikter i faktorer som påverkar lokaliserbarhet, såsom avståndsfördelningar, vägförlust, störningar och mottagen SINR. Simuleringsstudier undersöker korrelationen mellan lokaliserbarhet och antalet deltagande BS:er, SINR-krav, luft-till-mark-kanalegenskaper och nätverkskoordination. Dessutom har ett optimeringsproblem formulerats för att maximera lokaliseringsförmågan, undersöka effekten av UAV-höjd över olika scenarier. Vår studie avslöjar att i en urban makromiljö ökar effektiviteten av mobilnätsbaserad lokalisering med höjden, med lokaliserbarhet som når 100% över $60$ meter. Detta fynd indikerar att användning av mobilnät för UAV-lokalisering är ett gångbart alternativ. / <p>QC 20240319</p>

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Two separate imaging payloads were successfully operated using a wireless line-of-sight telemetry system that was developed as part of a recently completed UAV (unmanned aerial vehicle) imaging campaign over the largest coffee plantation in the USA. The objective was to demonstrate the performance of “off-the-shelf” wireless technology in an effort to reduce the cost of line-of-sight telemetry for imaging payloads on UAVs. Pre-deployment tests using a conventional twin-engine piloted aircraft at a flight height of 10k ft demonstrated successful broadband connectivity between a rapidly moving (ca. 280 km hr^(-1)) airborne WLAN (wireless local area network) and a fixed ground station WLAN. This paper details the performance of the wireless telemetry system on a slow-flying (<50 km hr^(-1)) solar-powered UAV at a flight height of 6.4 km.

Thesis submitted in compliance with the requirements for the Master's Degree in Technology: Department of Mechanical Engineering, Durban University of Technology, 2008. / The development of an evolutionary optimisation method and its application to the design of an advanced composite structure is discussed in this study. Composite materials are increasingly being used in various fields, and so optimisation of such structures would be advantageous. From among the various methods available, one particular method, known as Evolutionary Structural Optimisation (ESO), is shown here. ESO is an empirical method, based on the concept of removing and adding material from a structure, in order to create an optimum shape. The objective of the research is to create an ESO method, utilising MSC.Patran/Nastran, to optimise composite structures. The creation of the ESO algorithm is shown, and the results of the development of the ESO algorithm are presented. A tailfin of an aircraft was used as an application example. The aim was to reduce weight and create an optimised design for manufacture. The criterion for the analyses undertaken was stress based. Two models of the tailfin are used to demonstrate the effectiveness of the developed ESO algorithm. The results of this research are presented in the study.

This thesis focuses on the theory and implementation of visual navigation techniques for Autonomous Air Vehicles in outdoor environments. The target of this study is to fuse and cooperatively develop an incremental map for multiple air vehicles under the application of Simultaneous Location and Mapping (SLAM). Without loss of generality, two unmanned air vehicles (UAVs) are investigated for the generation of ground maps from current and a priori data. Each individual UAV is equipped with inertial navigation systems and external sensitive elements which can provide the possible mixture of visible, thermal infrared (IR) image sensors, with a special emphasis on the stereo digital cameras. The corresponding stereopsis is able to provide the crucial three-dimensional (3-D) measurements. Therefore, the visual aerial navigation problems tacked here are interpreted as stereo vision based SLAM (vSLAM) for both single and multiple UAVs applications. To begin with, the investigation is devoted to the methodologies of feature extraction. Potential landmarks are selected from airborne camera images as distinctive points identified in the images are the prerequisite for the rest. Feasible feature extraction algorithms have large influence over feature matching/association in 3-D mapping. To this end, effective variants of scale-invariant feature transform (SIFT) algorithms are employed to conduct comprehensive experiments on feature extraction for both visible and infrared aerial images. As the UAV is quite often in an uncertain location within complex and cluttered environments, dense and blurred images are practically inevitable. Thus, it becomes a challenge to find feature correspondences, which involves feature matching between 1st and 2nd image in the same frame, and data association of mapped landmarks and camera measurements. A number of tests with different techniques are conducted by incorporating the idea of graph theory and graph matching. The novel approaches, which could be tagged as classification and hypergraph transformation (HGTM) based respectively, have been proposed to solve the data association in stereo vision based navigation. These strategies are then utilised and investigated for UAV application within SLAM so as to achieve robust matching/association in highly cluttered environments. The unknown nonlinearities in the system model, including noise would introduce undesirable INS drift and errors. Therefore, appropriate appraisals on the pros and cons of various potential data filtering algorithms to resolve this issue are undertaken in order to meet the specific requirements of the applications. These filters within visual SLAM were put under investigation for data filtering and fusion of both single and cooperative navigation. Hence updated information required for construction and maintenance of a globally consistent map can be provided by using a suitable algorithm with the compromise between computational accuracy and intensity imposed by the increasing map size. The research provides an overview of the feasible filters, such as extended Kalman Filter, extended Information Filter, unscented Kalman Filter and unscented H Infinity Filter. As visual intuition always plays an important role for humans to recognise objects, research on 3-D mapping in textures is conducted in order to fulfil the purpose of both statistical and visual analysis for aerial navigation. Various techniques are proposed to smooth texture and minimise mosaicing errors during the reconstruction of 3-D textured maps with vSLAM for UAVs. Finally, with covariance intersection (CI) techniques adopted on multiple sensors, various cooperative and data fusion strategies are introduced for the distributed and decentralised UAVs for Cooperative vSLAM (C-vSLAM). Together with the complex structure of high nonlinear system models that reside in cooperative platforms, the robustness and accuracy of the estimations in collaborative mapping and location are achieved through HGTM association and communication strategies. Data fusion among UAVs and estimation for visual navigation via SLAM were impressively verified and validated in conditions of both simulation and real data sets.

The applications for Unmanned Aerial Vehicles are numerous and cover a range of areas from military applications, scientific projects to commercial activities, but many of these applications require substantial human involvement. This work focuses on the problems and limitations in cooperative Unmanned Aircraft Systems to provide increasing realism for cooperative algorithms. The Consensus Based Bundle Algorithm is extended to remove single agent limits on the task allocation and consensus algorithm. Without this limitation the Consensus Based Grouping Algorithm is proposed that allows the allocation and consensus of multiple agents onto a single task. Solving these problems further increases the usability of cooperative Unmanned Aerial Vehicles groups and reduces the need for human involvement. Additional requirements are taken into consideration including equipment requirements of tasks and creating a specific order for task completion. The Consensus Based Grouping Algorithm provides a conflict free feasible solution to the multi-agent task assignment problem that provides a reasonable assignment without the limitations of previous algorithms. Further to this the new algorithm reduces the amount of communication required for consensus and provides a robust and dynamic data structure for a realistic application. Finally this thesis provides a biologically inspired improvement to the Consensus Based Grouping Algorithm that improves the algorithms performance and solves some of the difficulties it encountered with larger cooperative requirements.

The reliance of unmanned aerial vehicles (UAVs) on GPS and other external navigation aids has become a limiting factor for many missions. UAVs are now physically able to fly in many enclosed or obstructed environments, due to the shrinking size and weight of electronics and other systems. These environments, such as urban canyons or enclosed areas, often degrade or deny external signals. Furthermore, many of the most valuable potential missions for UAVs are in hostile or disaster areas, where navigation infrastructure could be damaged, denied, or actively used against the vehicle. It is clear that developing alternative, independent, navigation techniques will increase the operating envelope of UAVs and make them more useful. This thesis presents work in the development of reliable monocular vision-aided inertial navigation for UAVs. The work focuses on developing a stable and accurate navigation solution in a variety of realistic conditions. First, a vision-aided inertial navigation algorithm is developed which assumes uncorrelated feature and vehicle states. Flight test results on a 80 kg UAV are presented, which demonstrate that it is possible to bound the horizontal drift with vision aiding. Additionally, a novel implementation method is developed for integration with a variety of navigation systems. Finally, a vision-aided navigation algorithm is derived within a Bierman-Thornton factored extended Kalman Filter (BTEKF) framework, using fully correlated vehicle and feature states. This algorithm shows improved consistency and accuracy by 2 to 3 orders of magnitude over the previous implementation, both in simulation and flight testing. Flight test results of the BTEKF on large (80 kg) and small (600 g) vehicles show accurate navigation over numerous tests.

The use of Unmanned Aerial Vehicles (UAV) has exploded over the last decade with the constant need to reduce costs while maintaining capability. Despite the relentless development of electronics and battery technology there is a sustained need to reduce the size and weight of the on-board systems to free-up payload capacity. One method of reducing the energy storage requirement of UAVs is to utilise naturally occurring sources of energy found in the atmosphere. This thesis explores the use of static and semi-dynamic soaring to extract energy from naturally occurring shallow layer cumulus convection to improve range, endurance and average speed. A simulation model of an X-Models XCalibur electric motor-glider is used in combination with a refined 4D parametric atmospheric model to simulate soaring flight. The parametric atmospheric model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation. For micro to small UAVs to be able to track the desired trajectories a highly accurate Attitude Heading Reference System (AHRS) is needed. Detailed analysis of the practical implementation of advanced attitude determination is used to enable optimal execution of the trajectories generated. The new attitude determination methods are compared to existing Kalman and complimentary type filters. Analysis shows the methods developed are capable of providing accurate attitude determination with extremely low computational requirements, even during extreme manoeuvring. The new AHRS techniques reduce the need for powerful on-board microprocessors. This new AHRS technique is used as a foundation to develop a robust navigation filter capable of providing improved drift performance, over traditional filters, in the temporary absence of global navigation satellite information. All these algorithms have been verified by flight tests using a mixture of manned and unmanned aerial vehicles and avionics developed specifically for this thesis.

Unmanned aerial vehicles (UAVs), when paired with an onboard camera, have proven to be useful tools in many applications, including aerial photography, precision agriculture, and search and rescue operations. Likewise, UAVs capable of physically interacting with the environment have shown great potential to help people perform dangerous, or time-consuming tasks more safely and efficiently than they could on their own. However, due to onboard computation and battery life limitations and complex flight dynamics, using UAVs to physically interact with the environment is still a developing area of research. Considering these limitations, the primary goals of this work are to (1) develop a new UAV platform for aerial manipulation, (2) develop modular hardware and software for the platform to enable specific tasks to be performed autonomously, and (3) develop a visual target tracking method to enable robust performance of autonomous aerial manipulation tasks in unstructured, real-world environments. To that end, the design of the Interacting-BoomCopter UAV (I-BC) is presented here as a new platform for aerial manipulation. With a simple tricopter frame, a single additional actuator for generating horizontal forces, and lightweight, modular end-effectors, the I-BC aims to balance efficiency and functionality in performing aerial manipulation tasks, and is able to perform various tasks such as mounting sensors in hard-to-reach places, and opening small doors or panels. An onboard camera, force and distance sensors, and a powerful single board computer (SBC) enable the I-BC to operate autonomously in unstructured environments, with potential applications in areas such as large-scale infrastructure inspection, industrial inspection and maintenance, and nuclear decontamination efforts.

"Micro aerial vehicles (MAV) are small remotely piloted or autonomous aircraft. Wingspans of MAVs can be as small as six inches to allow MAV’s to avoid detection during reconnaissance missions. Improving the aerodynamic efficiency of MAV’s by increasing the lift to drag ratio could lead to increased MAV range and endurance or future decreases in aircraft size. In this project, biologically inspired flight is used as a framework to improve MAV performance since MAV’s operate in a similar flight regime to birds. A novel wind tunnel apparatus was constructed that allows the planform shape of a MAV wing to be easily altered. The scale-model wing mimics a bird wing by using variable feather lengths to vary the wing planform shape. Genetic algorithms that use natural selection as an optimization process were applied to establish successive populations of candidate wing shapes. These wing shapes were tested in the wind tunnel where wings with higher fitness values were allowed to ‘breed’ and create a next generation of wings. After numerous generations were tested an acceptably strong solution was found that yielded a lift to drag ratio of 3.28. This planform was a non conventional planform that further emphasized the ability of a genetic algorithm to find a novel solution to a complex problem. Performance of the best planform was compared to previously published data for conventional MAV planform shapes. Results of this comparison show that while the highest lift to drag ratio found from the genetic algorithm is lower than published data, inabilities of the test wing to accurately represent a flat plate Zimmerman planform and limitations of the test setup can account for these discrepancies."