This dissertation addresses improvements to a consensus based task allocation algorithms for improving the Quality of Service in multi-task and multi-agent environments. Research in the past has led to many centralized task allocation algorithms where a central computation unit is calculating the global optimum task allocation solution. The centralized algorithms are plagued by creating a single point of failure and the bandwidth needed for creating consistent and accurate situational awareness off all agents. This work will extend upon a widely researched decentralized task assignment algorithm based on the consensus principle. Although many extensions have led to improvements of the original algorithm, there is still much opportunity for improvement in providing sufficient and reliable task assignments in real-world dynamic conditions and changing environments. This research addresses practical changes made to the consensus based task allocation algorithms for improving the Quality of Service in multi-task and multi-agent environments.

Unmanned Aerial Vehicles (UAVs) är obemannade flygfarkoster som främst använts och utvecklats inom det militära. Under senare år har användandet även tagit fart inom den civila sektorn, däribland mätningsbranschen. För att samla in geodata används Unmanned Aircraft Systems (UAS), vilka är system som består av fler komponenter än endast luftfarkosten t.ex. även kamera och kontrollstation. UAS är ett bra alternativ till traditionell flygfotografering då högupplösta bilder kan genereras till en låg kostnad. Eftersom UAS är en relativt ny metod måste osäkerheten utvärderas. Syftet med detta examensarbete är att utvärdera hur stödpunkter påverkar osäkerheten vid georeferering av UAS-bilder. Data erhölls från en flygning utförd av Sweco i november 2012. För att kunna utvärdera stödpunkternas inverkan översignalerades det 5 ha stora området med 35 stödpunkter. Nio olika konfigurationer av stödpunkter georefererades i programvaran Agisoft PhotoScan 0.9.0 och resultatet analyserades i Microsoft Excel, Geo Professional och Surfer 10L. Resultaten visar att osäkerheten för georefereringen minskar när antalet stödpunkter ökar, förutsatt att en jämn placering tillämpas. Bra georeferering uppnåddes när fyra stödpunkter användes. Vi rekommenderar ändå att minst fem stödpunkter används, fem stycken ger bra möjligheter till en god geometri – en i varje hörn och en i mitten. Det lägsta RMS-värdet i 3D (72 mm) erhölls med 17 stödpunkter jämnt fördelade över området. Det högsta RMS-värdet i 3D (190 mm) fick konfigurationen med sex stödpunkter placerade i ett av områdets hörn, något som tydligt visar hur stödpunkters placering (geometrin) påverkar osäkerheten av georefereringen. Även om fyra stödpunkter (en i varsitt hörn) bara får marginellt större RMS-värde än om en extra stödpunkt placeras i mitten, rekommenderas den sistnämnda för den bättre geometrin. För att kontrollera georefereringen rekommenderas några extra inmätta kontrollpunkter i området. / The main use and development of Unmanned Aerial Vehicles (UAVs) havethrough history been driven for military purposes, but in recent years the usehas increased also in the civilian sector, including the surveying industry. Inorder to collect geodata Unmanned Aircraft Systems (UAS) are used. UAS aresystems that consist not only of the unmanned vehicle, but also of componentslike a camera and a control station. UAS is a good alternative to traditionalaerial survey due to the high resolution images and the low operational cost.The uncertainty of UAS must be evaluated further since it is a relatively newsurveying method. The purpose of this study is to analyze the number of groundcontrol point’s (GCP’s) impact on the uncertainty of georeferencing UAS images.Data was collected from a flight conducted by Sweco in November 2012. The areawhich was flown (5 ha) was “over-signalized” by 35 GCPs in order to evaluate theirimpact on the georeferencing uncertainty. Nine different configurations of GCPswere georeferenced in the software Agisoft PhotoScan 0.9.0 and the result wasanalyzed in Microsoft Excel, Geo Professional and Surfer 10L. The result showsthat the uncertainty of the georeferencing decreases when the number of GCPsincreases, provided their distribution is even in the area. A goodgeoreferencing was obtained when four GCPs were used. Regardless, we recommendthe use of five, five provide a good geometry – one in each corner and one inthe middle. The least RMS value in 3D (72 mm) was found with 17 GCPs evenlydistributed in the area. The highest RMS value in 3D (190 mm) was found whenall six GCPs were placed in one of the corners of the area. This shows that thedistribution of GCPs has a great impact on the uncertainty of thegeoreferencing. Even if four GCPs (one in each corner) just get a little higherRMS value than if one extra GCP is placed in the middle, the latter isrecommended because of the favourable geometry. To be able to control thegeoreferencing it is recommended to survey some extra GCPs in the area.

Emerging health concern for gaseous nitrogen oxides (NOx) emitted during surface mine blasting has prompted mining authorities in the United States to pursue new regulations. NOx is comprised of various binary compounds of nitrogen and oxygen. Nitric oxide (NO) and nitrogen dioxide (NO2) are the most prominent. Modern explosive formulations are not designed to produce NOx during properly-sustained detonations, and researchers have identified several causes through laboratory experiments; however, direct sampling of NOx following full-scale surface mine blasting has not been accomplished. The purpose of this thesis was to demonstrate a safe, innovative method of directly quantifying NOx concentrations in a full-scale surface mining environment. A small unmanned aircraft system was used with a continuous gas monitor to sample concentrated fumes. Three flights were completed – two in the Powder River Basin. Results from a moderate NOx emission showed peak NO and NO2 concentrations of 257 ppm and 67.2 ppm, respectively. The estimated NO2 presence following a severe NOx emission was 137.3 ppm. Dispersion of the gases occurred over short distances, and novel geometric models were developed to describe emission characteristics. Overall, the direct sampling method was successful, and the data collected are new to the body of scientific knowledge.

The ability to quantify soil moisture spatial variability and its temporal dynamics over entire fields through direct soil observations using remote sensing will improve early detection of water stress before crop physiological or economic damage has occurred, and it will contribute to the identification of zones within a field in which soil water is depleted faster than in other zones of a field. The overarching objective of this research is to develop tools and methods for remotely estimating soil moisture variability in agricultural crop production. Index-based and machine learning methods were deployed for processing hyperspectral data collected from moisture-controlled samples. In the first of five studies described in this dissertation, the feasibility of using “low-cost” index-based multispectral reflectance sensing for remotely delineating soil moisture content from direct soil and crop residue measurements using down-sampled spectral data were determined. The relative reflectance from soil and wheat stalk residue were measured using visible and near-infrared spectrometers. The optimal pair of wavelengths was chosen using a script to create an index for estimating soil and wheat stalk residue moisture levels. Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples; however, this index worked poorly on estimating stalk residue moisture. In the second of five studies, 20 machine learning algorithms were applied to full spectral datasets for moisture prediction and comparing them to the index-based method from the previous objective. Cubic support vector machine (SVM) and ensemble bagged trees methods produced the highest composite prediction accuracies of 96% and 93% for silt-loam soil samples, and 86% and 93% for wheat stalk residue samples, respectively. Prediction accuracy using the index-based method was 86% for silt-loam soil and 30% for wheat stalk residue. In the third study, a spectral measurement platform capable of being deployed on a UAS was developed for future use in quantifying and delineating moisture zones within agricultural landscapes. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using a Raspberry Pi embedded computer that was programmed to interface with the UAS autopilot for autonomous reflectance data acquisition. A similar ground-based system was developed to keep track of ambient light during reflectance target measurement. The systems were tested under varying ambient light conditions during the 2017 Great American Eclipse. In the fourth study, the data acquisition system from the third study was deployed for recognizing different targets in the grayscale range using machine learning methods and under ambient light conditions. In this study, a dynamic method was applied to update integration time on spectrometers to optimize sensitivity of the instruments. It was found that by adjusting the integration time on each spectrometer such that a maximum intensity across all wavelengths was reached, the targets could be recognized simply based on the reflectance measurements with no need of a separate ambient light measurement. Finally, in the fifth study, the same data acquisition system and variable integration time method were used for estimating soil moisture under ambient light condition. Among 22 machine learning algorithms, linear and quadratic discriminant analysis achieved the maximum prediction accuracy. A UAS-deployable hyperspectral data acquisition system containing three portable spectrometers and an embedded computer was developed to classify moisture content from spectral data. Partial least squares regression and machine learning algorithms were shown to be effective to generate predictive models for classifying soil moisture.

Unmanned Aircraft Systems (UAS) represent a significant potential for growth in the aerospace industry. Their use in military operations has increased exponentially in the last decade alone, requiring a corresponding increase in training airspace in the United States. In addition to military usage, UAS have the potential to fulfill a myriad of roles for both the public and private sectors. However, the use of UAS has been limited in the National Airspace System (NAS) to military and public applications and only under fairly restrictive Certificates of Authorization or Waiver (COA). The only way to truly realize the potential of UAS is to fully integrate them into the NAS. The desire to integrate UAS was recently codified into law with the 2012 FAA Modernization Act, mandating integration by specific, fairly short timelines. There are several challenges currently preventing the full integration of UAS that range from technological to procedural areas. However, the one common theme in all of these challenges is Safety. Across the literature on this topic there is no consensus on how safe UAS need to be to achieve integration, whether UAS can currently meet specified safety targets, and if not, what is the best way to achieve the safety goals. The purpose of this effort was to demonstrate a comprehensive framework for analyzing UAS integration efforts using a Target Level of Safety (TLS) approach. Using reliability tools, aircraft encounter models, and data from a wide variety of sources ranging from manned aircraft safety, explosives, falling debris and earthquake damage, the primary outcome of the effort was a better understanding of the risk to second and third party persons as a result of UAS operations in the NAS. This framework and associated models are validated using reliability and casualty data from manned aircraft operations. The framework is then applied to several relevant and specific cases to demonstrate the impact of policy decisions on UAS reliability and allowed operational areas. The supporting research and analysis can serve as a baseline for future integration analysis and decision-making efforts, and was designed to allow stakeholders and decision makers in this field to assess UAS safety, and set minimum system reliability requirements and mitigation system effectiveness standards.

As problems in the world become increasingly complex, designers in multiple disciplines have begun to propose swarms as a solution. The espoused benefits include flexibility, resilience, and potential for decentralized control, yet there lacks consensus on what a swarm is, what characteristics they possess, and what applications they are able to address. This study addresses these questions by creating a unified approach for understanding and analyzing swarms, called the Swarm Analysis Framework. The framework pursues three goals: 1) provide extensive analysis on the many characteristics and applications that define a swarm, 2) remain flexible enough to facilitate design, testing, analysis, and other problems in understanding swarms, and 3) outline swarm applications specific to aircraft and spacecraft based swarms. Afterwards, the Swarm Analysis Framework is used to guide a case study in which the application is a swarm was developed to study one of these aerospace applications. Ultimately, the Swarm Analysis Framework, along with its extensions improvements, should be able to act as a guide or roadmap in understanding how swarms behave across multiple disciplines.<br>

No description available.

Radar-based remote sensing is consistently growing, and new technologies and subsequent techniques for characterization are changing the feasibility of understanding the environment. The emergence of easily accessible unmanned aircraft system (UAS) has broadened the scope of possibilities for efficiently surveying the world. The continued development of low-cost sensing systems has greatly increased the accessibility to characterize physical phenomena. In this thesis, we explore the viability and implementation of using UAS as a means of radar-based remote sensing for ground penetrating radar (GPR) and polarimetric scatterometry. Additionally, in this thesis, we investigate the capabilities and implementations of low-cost microwave technologies for applications in radar-based remote sensing compared to higher fidelity and more expensive technologies of similar scope.

Unmanned aircraft systems (UAS) often encounter turbulent fields that perturb the aircraft from its desired target trajectory, or in a manner that increases the load factor. The aircraft's fixed dihedral angle, providing passive roll-stiffness, is often selected based on lateral-directional stability requirements for the vehicle. A study to predict the effect of an active dihedral system on lateral-directional stability and vertical gust rejection capability was conducted to assess the performance and feasibility of the system. Traditionally, the dihedral location begins at the root to maintain wing structural requirements, however, the active dihedral system was also evaluated for dynamic stability and gust rejection performance at alternative dihedral breakpoint locations. Simulations were completed using linear parameter-varying (LPV) models, derived from traditional Newtonian aircraft dynamics and associated kinematic equations, to improve the modeling of the nonlinear active dihedral system. The stability of the LPV system was evaluated using Lyapunov stability theory applied to switched linear systems, assessing bounds of operation for the dihedral angle and flapping rate. An ideal feedback controller was developed using a linear–quadratic regulator (LQR) for both a discrete gust model and a continuous gust model, and a gain scheduled LQR controller was implemented to show the benefits of gain scheduling with a parameter varying state and input model. Finally, a cost analysis was conducted to investigate the real-world benefit of altering the dihedral breakpoint location. The effects of the active dihedral system on battery capacity and consumption efficiency were observed and compared with the gust rejection authority. / Master of Science / Unmanned aircraft systems (UAS) often encounter wind disturbances that perturb the aircraft from its desired target trajectory, or in a manner that increases the force encountered on the vehicle. The aircraft's fixed dihedral angle, providing stiffness to roll rotations, is often selected based on stability and control requirements for the vehicle. A study to predict the effect of a flapping wing (active dihedral) system on the stability, control, and wind gust rejection capability is completed to assess the performance and feasibility of such a system. Traditionally, the dihedral location begins at the root to maintain wing structural requirements, however, the active dihedral system was also evaluated for stability and wind gust rejection performance at alternative locations along the wing where the dihedral could begin, with intention of finding the best location. Simulations were completed using a varying set of simplified models, obtained from traditional aircraft mechanics, to improve the modeling of the true complex active dihedral system. The stability of the system was evaluated using various theories applied to the linear systems in attempt to define a bounded operating region for the dihedral angle and flapping motion. An ideal controller for the system was developed using ideas from well documented linear control theory for both a single wind gust and a continuous wind gust model. A controller that varies with vehicle flapping motion was implemented to show the benefits of scheduling the controller with a parameter varying state and input model. Finally, a cost analysis was conducted to investigate the real-world benefit of altering the dihedral starting location. The effects of the active dihedral system on battery capacity and consumption efficiency were observed and compared with the total gust rejection capability.

Over the last years there has been a rapid development in the UAS-technology (Unmanned Aircraft Systems) and today there are several UAS systems on the market. The fast development has led to differences in both price and capability of taking high-quality images between the systems. The purpose of this study was firstly to investigate how two UAS systems differ in the uncertainty of measurement while making digital terrain models, secondly, to investigate how different UAS systems cope with the laws and requirements that exist for producing digital terrain models for detail projection, SIS-TS 21144:2016 Table 6 level 1-3. A comparative study on two software’s creation of point clouds from picture data was also conducted. In this study, three digital models were made from one specific area. They were created with two different UAS-systems and laser scanning from an airplane. The models were compared and analysed using the RUFRIS method. The UASsystems used were a fixed wings Smartplanes S1C and a rotary wings Dji Phantom 4 PRO. The Smartplanes flew 174 m above the ground and the Dji Phantom 4 flew 80 m above the ground. The results from the study show that laser scanning from the airplane created the model with the lowest measurement uncertainty and met all the requirements for each separate type (asphalt, natural soil, grass and gravel) for detail projection according to SIS.TS 201144:2016 table 6 level 1-3. Additionally, the results show that the terrain model produced by the Dji Phantom 4 only met the requirements for asphalt where the mean deviation was 0,001 m. The results produced with “Smartplanes” met the requirements for asphalt and gravel where the mean deviations were -0,007 m and 0,017 m. The softwares PhotoScan and UASMaster were compared while creating point clouds from pictures taken by the Smartplanes. The results show that PhotoScan had the lowest uncertainty for asphalt, grass and gravel surfaces while UASMaster produced lower uncertainty for natural soil. The results indicate that airborne laser scanning should be the preferred method for collection of topographic data since it created lower measurement uncertainties than the other methods in this study. It is also possible to create digital terrain models with UAS for detail projection for asphalt and gravel surface in accordance with 21144:2016. Finally, it was concluded that the used software programs are showing differences in creating point clouds. / De senaste åren har tekniken för Unmanned Aircraft System (UAS) utvecklats snabbt och idag finns flera system på marknaden. Ett resultat av den snabba utvecklingen är att de olika systemen skiljer sig åt, dels i pris men även i kapacitet. Syftet med studien var att undersöka hur olika UAS-system skiljer sig åt i mätosäkerhet vid framställning av digitala terrängmodeller, men även hur olika UAS-system står sig mot det regelverk som finns för framställning av digitala terrängmodeller vid detaljprojektering enligt SIS-TS 21144:2016 Tabell 6 klass 1-3. Ytterligare ett syfte med studien var att undersöka hur olika programvaror skiljer sig åt vid framställning av punktmoln från bilddata. I studien kontrollerades och jämfördes tre digitala terrängmodeller genererade över samma område med två olika UAS-system samt laserskanning från ett flygplan. Terrängmodellerna jämfördes mot kontrollprofiler framställda med RUFRIS-metoden. De olika UAS-systemen var en dyrare variant, Smartplanes S1C (fastavingar), och en billigare variant, Dji Phantom 4 PRO (roterande vingar). De tillämpade flyghöjderna för flygningarna var 174 m för Smartplanes och 80 m för Dji Phantom. Resultatet från studien visar att laserskanning från flygplanet uppnådde lägst mätosäkerhet och klarade samtliga krav för varje separat marktyp för detaljprojektering enligt SIS-TS 201144:2016 Tabell 6 klass 1-3. Marktyper som undersöktes var: asfalt, naturmark, gräs och grus. Vidare klarade terrängmodellen producerad med Dji Phantom endast kravet för asfaltsytor, där medelavvikelsen fastställdes till 0,001 m. Terrängmodellen producerad med Smartplanes klarade endast kraven för marktyperna asfalt och grus där medelavvikelsen fastställdes till -0,007 m respektive 0,017 m. Som en del i studien jämfördes programvarorna PhotoScan och UASMaster för framställning av punktmoln för bilder insamlade med Smartplanes S1C. Resultatet visar att PhotoScan uppnådde lägst mätosäkerhet för asfalt, gräs och grus medan UASMaster uppnådde lägst mätosäkerhet för naturmark. Studien visar att flygburen laserskanning borde vara en fortsatt föredragen metod för insamling av topografisk data då metoden resulterade i lägst mätosäkerheter i denna studie. Vidare visar studien att det är möjligt att framställa digitala terrängmodeller med UAS för detaljprojektering enligt SISTS 21144:2016 för asfalt- och grusytor. Dessutom konstateras att olika bearbetningsprogram skiljer sig vid framställning av punktmoln.