<p>Multirotor unmanned aerial vehicles (UAVs) added with the ability to physically interact with the environment has opened endless possibilities for aerial mobile manipulation tasks. With the unlimited reachable workspace and physical interaction capabilities, such robots can enhance human ability to perform dangerous and hard-to-reach tasks. However, realizing aerial mobile manipulation in real-world scenarios is challenging with respect to the diversity in aerial platforms, control fidelity and susceptibility to variations in the environment. Therefore, the first part of the dissertation provides tools to  classify and evaluate different multirotor designs. A measure of responsiveness of a multirotor platform in exerting generalized forces and rejecting disturbances is discussed through the control bandwidth analysis. Superiority in control bandwidth for fully-actuated multirotors is established in a comparison with equivalent under-actuated multirotors. To further classify and distinguish multirotor platforms, a new mobility measure is proposed and compared by surveying all aerial platforms employed for aerial mobile manipulation. In compliance to the control bandwidth analysis, the mobility measure for fully-actuated multirotors is relatively higher making them better suited for manipulation tasks. </p> <p><br></p> <p><br></p> <p>Aerial physical interaction, as a part of aerial mobile manipulation, with partially unknown environments is challenging due to the uncertainties imposed while dexterously exerting force signatures. A hybrid physical interaction (HyPhI) controller is proposed to enable constrained force contact with a steady transition from unconstrained motion, by squelching excess energy during initial impact. However, uncertainties posed by the partially unknown environment requires to understand the surrounding environment and their current physical states, that can enhance interaction performance. The limited resources and flight time of the multirotors requires to simultaneously understand the environment and perform aerial physical interactions. Inspection-on-the-fly is an uncanny ability of humans to intuitively infer states during manipulation while reducing the necessity to conduct inspection and manipulation separately. In this dissertation, the inspection-on-the-fly method based HyPhI controller is proposed to engage in a steady contact with partially unknown environments, while simultaneously estimating the physical states of the surfaces. The proposed method is evaluated in a mockup of real-world facility, to understand the surface properties while engaging in steady interactions. Further, such inspection of surfaces and estimation of various states enables a deeper understanding of the environment while enhancing the ability to physically interact. </p>

<p dir="ltr">Unmanned surface vehicles (USVs) offer a versatile platform for various maritime applications, including research, surveillance, and search-and-rescue operations. A critical capability for USVs is maintaining position (station keeping) in dynamic environments and coordinating movement with other USVs (formation control) for collaborative missions. This thesis investigates control strategies for USVs operating in challenging conditions. </p><p dir="ltr">The initial focus is on evaluating traditional control methods like Backstepping and Sliding Mode controllers for station keeping in simulated environments with disturbances. The results from these tests pointed towards the need for a more robust control technique, like deep-learning based control for enhanced performance. </p><p dir="ltr">The thesis then explores formation control, a crucial aspect of cooperative USV missions. A vision-based passive control strategy utilizing a virtual leader concept is proposed. This approach leverages onboard cameras to detect markers on other USVs, eliminating the need for direct communication and potentially improving scalability and resilience. </p><p dir="ltr">Then the thesis presents vision-based formation control architecture and the station keeping controller evaluations. Simulation results are presented, analyzed, and used to draw conclusions about the effectiveness of the proposed approaches. Finally, the thesis discusses the implications of the findings and proposes potential future research directions</p>

<p>The vehicle industry has an endless appetite to get better. Often, this appetite is justified by the need of the hour. In the agricultural space, this translates to improving agricultural productivity in the face of population growth, reduced arable land and shortage of skilled farm labor. As for torsional vibrations, which have been around ever since the wheel was invented, the problem gets redefined with new regulations demanding new powertrains with improved fuel efficiency and reduced emissions.</p><p>A solution to the agriculture problem, involves efficiently automating the harvesting process.The first section of this thesis covers the ‘Auto-Unload’ where the goal of automation is achieved. This was done by building a simulation framework that was used to develop and synthesize the ‘AutoUnload’ controller. This controller was later deployed on a combine and a successful unloading on-the-go was demonstrated with a combine, tractor, and tractor-driven grain cart.</p><p>The solution to the second problem about drivetrain vibrations involved deriving a mathematical model for simulating the powertrain of a medium-duty truck. This was done to confirm resonance seen during testing done on a chassis dynamometer. The consequent control strategy to mitigate undesired vibration was to move the torque excitation away from the natural frequency of the system. This was achieved by a ‘gear-shifting’ algorithm. Comparison between on-road tests with and without the ‘gear-shifting’ algorithm showed that such a control strategy can effectively eliminate resonance. The solution methodology developed in this work is robust and transferable to higher engine torques and harvest speeds.</p>

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The geometrical structure of a tree can consist of thousands, even millions, of branches, twigs and leaves in complex arrangements. The structure contains a lot of useful information and can be used for example to assess a tree's health or calculate parameters such as total wood volume or branch size distribution. Because of the complexity, capturing the structure of an entire tree used to be nearly impossible, but the increased availability and quality of particularly digital cameras and Light Detection and Ranging (LIDAR) instruments is making it increasingly possible. A set of digital images of a tree, or a point cloud of a tree from a LIDAR scan, contains a lot of data, but the information about the tree structure has to be extracted from this data through analysis. This work presents a method of reconstructing 3D models of trees from point clouds. The model is constructed from cylindrical segments which are added one by one. Bayesian inference is used to determine how to optimize the parameters of model segment candidates and whether or not to accept them as part of the model. A Hough transform for finding cylinders in point clouds is presented, and used as a heuristic to guide the proposals of model segment candidates. Previous related works have mainly focused on high density point clouds of sparse trees, whereas the objective of this work was to analyze low resolution point clouds of dense almond trees. The method is evaluated on artificial and real datasets and works rather well on high quality data, but performs poorly on low resolution data with gaps and occlusions.

<p>This thesis presents solutions to some of the problems facing Autonomous Surface Vehicle (ASV) deployments in inland waterways through the development of navigational and control systems. Fluvial systems are one of the hardest inland waterways to navigate and are thus used as a use-case for system development. The systems are built to reduce the reliance on a-prioris during ASV operation. This is crucial for exceptionally dynamic environments such as fluvial bodies of water that have poorly defined routes and edges, can change course in short time spans, carry away and deposit obstacles, and expose or cover shoals and man-made structures as their water level changes. While navigation of fluvial systems is exceptionally difficult potential autonomous data collection can aid in important scientific missions in under studied environments.</p> <p><br></p> <p>The work has four contributions targeting solutions to four fundamental problems present in fluvial system navigation and control. To sense the course of fluvial systems for navigable path determination a fluvial segmentation study is done and a novel dataset detailed. To enable rapid path computations and augmentations in a fast moving environment a Dubins path generator and augmentation algorithm is presented ans is used in conjunction with an Integral Line-Of-Sight (ILOS) path following method. To rapidly avoid unseen/undetected obstacles present in fluvial environments a Deep Reinforcement Learning (DRL) agent is built and tested across domains to create dynamic local paths that can be rapidly affixed to for collision avoidance. Finally, a custom low-cost and deployable ASV, BREAM (Boat for Robotic Engineering and Applied Machine-Learning), capable of operating in fluvial environments is presented along with an autonomy package used in providing base level sensing and autonomy processing capability to varying platforms.</p> <p><br></p> <p>Each of these contributions form a part of a larger documented Fluvial Navigation Control Architecture (FNCA) that is proposed as a way to aid in a-priori free navigation of fluvial waterways. The architecture relates the navigational structures into high, mid, and low-level controller Guidance and Navigational Control (GNC) layers that are designed to increase cross vehicle and domain deployments. Each component of the architecture is documented, tested, and its application to the control architecture as a whole is reported.</p>

<p dir="ltr">The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.</p><p dir="ltr">The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.</p><p dir="ltr">Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p>