This dissertation investigates how to plan paths for Unmanned Aerial Vehicles (UAV) for the task of covering an environment. Three increasingly complex coverage problems based on the environment that needs to be covered are studied. The dissertation starts with a 2D point coverage problem where the UAV needs to visit a set of sites on the ground plane by flying on a fixed altitude plane parallel to the ground. The UAV has limited battery capacity which may make it infeasible to visit all the points. A novel symbiotic UAV and Unmanned Ground Vehicle (UGV) system where the UGV acts as a mobile recharging station is proposed. A practical, efficient algorithm for solving this problem using Generalized Traveling Salesperson Problem (GTSP) solver is presented. Then the algorithm is extended to a coverage problem that covers 2D regions on the ground with a UAV that can operate in fixed-wing or multirotor mode. The algorithm is demonstrated through proof-of-concept experiments. Then this algorithm is applied to covering 2D regions, not all of which lie on the same plane. This is motivated by bridge inspection application, where the UAV is tasked with visually inspecting planar regions on the bridge. Finally, a general version of the problem where the UAV is allowed to fly in complete 3D space and the environment to be covered is in 3D as well is presented. An algorithm that clusters viewpoints on the surface of a 3D structure and has an UAV autonomously plan online paths to visit all viewpoints is presented. These online paths are re-planned in real time as the UAV obtains new information on the structure and strives to obtain an optimal 3D coverage path. / Doctor of Philosophy / This dissertation investigates how to plan paths for Unmanned Aerial Vehicles (UAV). Three increasingly complex coverage problems based on the environment that needs to be covered are studied. The dissertation starts with a 2D point coverage problem where the UAV needs to visit a set of sites on the ground by flying at a fixed altitude. The UAV has limited battery capacity which may make it impossible to visit all the points. A novel symbiotic UAV and Unmanned Ground Vehicle (UGV) system where the UGV acts as a mobile recharging station is proposed. A practical, efficient algorithm for solving this problem using Generalized Traveling Salesperson Problem (GTSP) solver is presented. Then the algorithm is extended to coverage of 2D regions on the ground with a hybrid UAV. The algorithm is demonstrated through proof-of-concept experiments. Then this algorithm is applied to covering 2D regions on 3D structures. This is motivated by bridge inspection application, where the UAV is tasked with visually inspecting regions on the bridge. Finally, a general version of the problem where the UAV is allowed to fly in 3D space and the environment to be covered is in 3D as well is presented. An algorithm that clusters points on the surface of a 3D structure and has an UAV autonomously plan online paths to visit all viewpoints is presented. These online paths are re-planned in real time as the UAV obtains new information on the structure and strives to obtain an optimal 3D coverage path.

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation's resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges' cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures' surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application.

Att underhålla infrastruktur med hög standard är viktigt för alla länder, och att misslyckas med detta innebär allvarliga logistiska och ekonomiska konsekvenser. Kraftledningsinspektion är en betydelsefull del i detta. Denna uppsats har sökt svar på vad för- och nackdelarna är med att använda satellitövervakning av kraftledningar, samt svar på ifall teknik för satellitövervakning av kraftledningar också kan tillämpas på volymberäkningar av skog. Metoden har utgått från intervjuer med experter och relevanta företag samt litteratur som underlag. Att undersöka vilka för- och nackdelar som finns med satellitövervakning av kraftledningar var viktigt eftersom satellitövervakning är ett snabbt växande fält, men inte särskilt väl undersökt. Att undersöka huruvida teknik för kraftledningsövervakning med satelliter är tillräcklig för att estimera skogsvolym bedömdes vara värdefullt eftersom skogsvolym idag estimeras med luftburen LiDAR, medan luftburen LiDAR påstods vara signifikant mycket dyrare överlag än satellitmätningar. Det fanns alltså en eventuell ekonomisk fördel med att estimera skogsvolym med satelliter istället för dagens luftburna mätningar. Det förväntade resultatet var att tekniken för satellitövervakning av kraftledningar är tillräcklig för att estimera skogsvolym. De största nackdelarna med satellitövervakning av kraftledningar berör problemen med att nå tillräckligt hög noggrannhet i processerna för trädidentifiering, samt att utveckla effektiva tillvägagångssätt för att utvärdera detta då underlaget gällande föreslagna och utvärderade metoder är glest. En annan nackdel visade sig vara att satellitmetoderna är svåra att göra konkurrenskraftiga i jämförelse med de etablerade luftburna LiDAR-metoderna i fråga om kostnader. Anledningen är att de högupplösta satellitbilder som ofta krävts för att nå hög noggrannhet fortfarande är dyra, även om en fördel som också identifierades var att ny och billigare satellitteknik just nu utvecklas i hög takt. Gällande denna fråga visade sig den största fördelen vara den snabba utvecklingen av nya satelliter med högre upplösning, som öppnar upp möjligheten för att komma ikapp de konventionella metoderna. Det förväntade resultatet kring huruvida satellitövervakning har ekonomiska fördelar jämfört med luftburen övervakning motsägs alltså av resultatet i denna rapport, med avseende på kraftledningsövervakning. Däremot indikerar resultatet att tekniken för satellitövervakning av kraftledningar är tillräcklig för att estimera skogsvolym, vilket överensstämmer med det förväntade resultatet. / Maintaining infrastructure of high standard is important for all countries. Failing this means severe logistical and economic consequences. Power line inspection is an important part of this. This thesis has searched for an answer to what the advantages and disadvantages are of inspecting power lines by using satellites, as well as an answer to if the technology of satellite surveillance of power lines is sufficient to estimate forest volume. The methodology of the thesis has been to turn to companies and experts in the field and to use relevant literature. Examining what the advantages and disadvantages of satellite inspection of power lines are was important since satellite surveillance is a growing field, but not very well researched. To analyze whether technology of satellite surveillance of power lines is enough to estimate forest volume was thought to be valuable since forest volume today is estimated by airborne LiDAR, while airborne LiDAR was claimed to be significantly more expensive in general than 3 satellite measurements. Thus, there was a potential economic advantage to estimate forest volume with satellites instead of airborne measurements. The expected result was that the technology of satellite surveillance of power lines is sufficient to estimate forest volume. The biggest disadvantages of satellite surveillance of power lines involve the problems of achieving high enough accuracy in the processes of tree identification, as well as developing effective formulas to evaluate this when the research material of proposed methods is sparse. Another disadvantage turned out to be that the satellite methods are hard to compete with, in comparison to the established airborne LiDAR methods and in regard to cost. The reason is that the high-resolution satellite images that often are demanded still are expensive, even though an advantage that also was identified in this thesis is that new and cheaper satellite technology is being developed at a quick rate. The biggest advantage of satellite surveillance of power lines turned out to be the quick development of new satellites with higher resolution, which enables the possibility to catch up with the conventional methods. The expected result in regard to whether satellite surveillance has economic advantages compared to airborne surveillance is contradicted in the result of this thesis, in regard to power line inspection. However, the result indicates that the technology of satellite surveillance of power lines is sufficient to estimate forest volume, which concurs with the expected result.