<div>This dissertation broadly focuses on autonomous condition assessment of civil infrastructures using vision-based methods, which present a plausible alternative to existing manual techniques. A region-based convolutional neural network (Faster R-CNN) is exploited for the detection of various earthquake-induced damages in reinforced concrete buildings. Four different damage categories are considered such as surface crack, spalling, spalling with exposed rebars, and severely buckled rebars. The performance of the model is evaluated on image data collected from buildings damaged under several past earthquakes taking place in different parts of the world. The proposed algorithm can be integrated with inspection drones or mobile robotic platforms for quick assessment of damaged buildings leading to expeditious planning of retrofit operations, minimization of damage cost, and timely restoration of essential services. </div><div><br></div><div> </div><div> Besides, a computer vision-based approach is presented to track the evolution of a damage over time by analysing historical visual inspection data. Once a defect is detected in a recent inspection data set, its spatial correspondences in the data collected during previous rounds of inspection are identified leveraging popular computer vision-based techniques. A single reconstructed view is then generated for each inspection round by synthesizing the candidate corresponding images. The chronology of damage thus established facilitates time-based quantification and lucid visual interpretation. This study is likely to enhance the efficiency structural inspection by introducing the time dimension into the autonomous condition assessment pipeline.</div><div><br></div><div> </div><div> Additionally, this dissertation incorporates depth fusion into a CNN-based semantic segmentation model. A 3D animation and visual effect software is exploited to generate a synthetic database of spatially aligned RGB and depth image pairs representing various damage categories which are commonly observed in reinforced concrete buildings. A number of encoding techniques are explored for representing the depth data. Besides, various schemes for fusion of RGB and depth data are investigated to identify the best fusion strategy. It was observed that depth fusion enhances the performance of deep learning-based damage segmentation algorithms significantly. Furthermore, strategies are proposed to manufacture depth information from corresponding RGB frame, which eliminates the need of depth sensing at the time of deployment without compromising on segmentation performance. Overall, the scientific research presented in this dissertation will be a stepping stone towards realizing a fully autonomous structural condition assessment pipeline.</div>

Formation control in multi-agent systems represents a groundbreaking intersection of various research fields with lots of emerging applications in various technologies. The realm of space exploration also can benefit significantly from formation control, facilitating a wide range of functions from astronomical observations, and climate monitoring to enhancing telecommunications, and on-orbit servicing and assembly. In this thesis, we present a novel 3D formation control scheme for directed graphs in a leader-follower configuration, achieving (almost) global convergence to the desired shape. Specifically, we introduce three controlled variables representing bispherical coordinates that uniquely describe the formation in 3D. Acyclic triangulated directed graphs (a class of minimally acyclic persistent graphs) are used to model the inter-agent sensing topology, while the agents’ dynamics are governed by the single-integrator model and 2nd order nonlinear version representing spacecraft formation flight. The analysis demonstrates that the proposed decentralized robust formation controller using prescribed performance control ensures (almost) global asymptotic stability while avoiding potential shape ambiguities in the final formation. Furthermore, the control laws are implementable in arbitrarily oriented local coordinate frames of follower agents using only low-cost onboard vision sensors, making them suitable for practical applications. Formation maneuvering and collision avoidance among agents are also addressed which play crucial roles in the safety of space operations. Finally, we validate our formation control approach by simulation studies. / Formationskontroll i system med flera agenter representerar en banbrytande skärningspunkt av olika forskningsområden med massor av nya tillämpningar inom olika teknologier. Rymdutforskningens rike kan också dra stor nytta av formationskontroll, underlättar ett brett utbud av funktioner från astronomiska observationer och klimat övervakning för att förbättra telekommunikation och service och montering i omloppsbana. I denna avhandling presenterar vi ett nytt 3D-formationskontrollschema för riktade grafer i en ledare-följare-konfiguration, vilket uppnår (nästan) global konvergens till önskad form. Specifikt introducerar vi tre kontrollerade variabler som representerar bisfäriska koordinater som unikt beskriver formationen i 3D. Acykliska triangulerade riktade grafer (en klass av minimalt acykliska beständiga grafer) används för att modellera avkänningstopologin mellan agenter, medan agenternas dynamik styrs av singelintegratormodellen och 2:a ordningen olinjär version som representerar rymdfarkostbildningsflygning. Analysen visar att den föreslagna decentraliserade robusta formationskontrollanten använder föreskriven prestanda kontroll säkerställer (nästan) global asymptotisk stabilitet samtidigt som potentiell form undviks oklarheter i den slutliga formationen. Dessutom är kontrolllagarna implementerbara i godtyckligt orienterade lokala koordinatramar för efterföljare som endast använder lågkostnad ombord visionsensorer, vilket gör dem lämpliga för praktiska tillämpningar. Formationsmanövrering och undvikande av kollisioner mellan agenter tas också upp som spelar avgörande roller i säkerheten vid rymdoperationer. Slutligen validerar vi vår strategi för formningskontroll genom simuleringsstudier