UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item INDOOR TARGET SEARCH, DETECTION, AND INSPECTION WITH AN AUTONOMOUS DRONE(2024) Ashry, Ahmed; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis investigates the deployment of unmanned aerial vehicles (UAVs) in indoor search and rescue (SAR) operations, focusing on enhancing autonomy through the development and integration of advanced technological solutions. The research addresses challenges related to autonomous navigation and target inspection in indoor environments. A key contribution is the development of an autonomous inspection routine that allows UAVs to navigate to and meticulously inspect targets identified by fiducial markers, replacing manual piloted inspection. To enhance the system’s target recognition, a custom-trained object detection model identifies critical markers on targets, operating in real-time on the UAV’s onboard computer. Additionally, the thesis introduces a comprehensive mission framework that manages transitions between coverage and inspection phases, experimentally validated using a quadrotor equipped with onboard sensing and computing across various scenarios. The research also explores integration and critical analysis of state-of-the-art path planning algorithms, enhancing UAV autonomy in cluttered settings. This is supported by evaluations conducted through software-in-the-loop simulations, setting the stage for future real-world applications.Item A Neuromorphic VLSI Navigation System Inspired By Rodent Neurobiology(2019) Koul, Shashikant; Horiuchi, Timothy K; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Path planning is an essential capability for autonomous mobile robot navigation. Taking inspiration from long-range navigation in animals, a neuromorphic system was designed to implement waypoint path planning on place cells that represent the navigation space as a cognitive graph of places by embedding the place-to-place connectivity in their synaptic interconnections. Hippocampal place cells, along with other spatially modulated neurons of the mammalian brain, like grid cells, head-direction cells and boundary cells are believed to support navigation. Path planning using spike latency of place cells was demonstrated using custom-designed, multi-neuron chips on examples and applied to a robotic arm control problem to show the extension of this system to other application domains. Based on the observation that varying the synaptic current integration in place cells affects the path selection by the planning system, two models of current integration were compared. By considering the overall path execution cost increase in response to an obstruction in the planned path execution, reduced spike latency response of a place cell to simultaneously converging spikes from multiple paths in the network was found to bias the path selection to paths offering more alternatives at various choice points. Application of the planning system to a navigation scenario was completed in software by using a place-cell based map-creation method to generate a map prior to planning and co-opting a grid-cell based path execution system that interacts with the path planning system to enable a simulated agent to do goal-directed navigation.Item Planning for Autonomous Operation of Unmanned Surface Vehicles(2016) Shah, Brual; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. The autonomous operations of the USV over long time horizons requires a path planner to compute paths over long distances in complex marine environments consisting of hundreds of islands of complex shapes. The available free space in marine environment changes over time as a result of tides, environmental restrictions, and weather. Secondly, the maximum velocity and energy consumption of the USV is significantly influenced by the fluid medium flows such as strong currents. Finally, the USV have to operate in an unfamiliar, unstructured marine environment with obstacles of variable dimensions, shapes, and motion dynamics such as other unmanned surface vehicles, civilian boats, shorelines, or docks poses numerous planning challenges. The proposed Ph.D. dissertation explores the above mentioned problems by developing computationally efficient path and trajectory planning algorithms that enables the long term autonomous operation of the USVs. We have developed a lattice-based 5D trajectory planner for the USVs operating in the environment with the congested civilian traffic. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. Secondly, we present a computationally efficient and optimal algorithm for long distance path planning in complex marine environments using A* search on visibility graphs defined over quad trees. Finally, we present an A* based path planning algorithm with newly developed admissible heuristics for computing energy efficient paths in environment with significant fluid flows. The effectiveness of the planning algorithms is demonstrated in the simulation environments by using systems identified dynamics model of the wave amplitude modular vessel (WAM-V) USV14.Item Mission and Scenario Planning for Unmanned Aerial Vehicles (Path Planning and Collision Avoidance Systems)(2016) Shadab, Niloofar; Xu, Huan; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As unmanned autonomous vehicles (UAVs) are being widely utilized in military and civil applications, concerns are growing about mission safety and how to integrate dierent phases of mission design. One important barrier to a coste ective and timely safety certication process for UAVs is the lack of a systematic approach for bridging the gap between understanding high-level commander/pilot intent and implementation of intent through low-level UAV behaviors. In this thesis we demonstrate an entire systems design process for a representative UAV mission, beginning from an operational concept and requirements and ending with a simulation framework for segments of the mission design, such as path planning and decision making in collision avoidance. In this thesis, we divided this complex system into sub-systems; path planning, collision detection and collision avoidance. We then developed software modules for each sub-systemItem RISK-BASED MULTIOBJECTIVE PATH PLANNING AND DESIGN OPTIMIZATION FOR UNMANNED AERIAL VEHICLES(2016) Rudnick-Cohen, Eliot Sylvan; Herrmann, Jeffrey W; Azarm, Shapour; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Safe operation of unmanned aerial vehicles (UAVs) over populated areas requires reducing the risk posed by a UAV if it crashed during its operation. We considered several types of UAV risk-based path planning problems and developed techniques for estimating the risk to third parties on the ground. The path planning problem requires making trade-offs between risk and flight time. Four optimization approaches for solving the problem were tested; a network-based approach that used a greedy algorithm to improve the original solution generated the best solutions with the least computational effort. Additionally, an approach for solving a combined design and path planning problems was developed and tested. This approach was extended to solve robust risk-based path planning problem in which uncertainty about wind conditions would affect the risk posed by a UAV.Item AN ADAPTIVELY SAMPLED PATH PLANNER USING WAYPOINTS: AN ANY-ANGLE VARIANT(2014) Gefen, Yonatan; Martins, Nuno C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis develops a low-cost grid-based path planner that intrinsically supports smooth, curved vehicle dynamics. There are many advantages to grid-based planners, including working natively in the digital space of most sensors, and efficiency in low dimensional space. However, discrete planners create jaggedness in most paths. Further, the dimensionality must be limited for efficiency, usually by limiting vehicle steering angles to a small finite set. The algorithm presented here, Waypoint-A*, extends A* to produce low-cost curved trajectories, taking the dynamics of the vehicle into account explicitly post-planning. Considering the path generated by A* as composed of a set of waypoints, Waypoint-A* calculates the minimum-cost heading on a continuum, to direct the vehicle to the waypoint at the location resulting in the lowest total cost. Smoothness of these curves is invariant to terrain resolution and computation.Item Accurate SLAM With Application For Aerial Path Planning(2013) Friedman, Chen; Chopra, Inderjit; Rand, Omri; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis focuses on operation of Micro Aerial Vehicles (MAVs), in previously unexplored, GPS-denied environments. For this purpose, a refined Simultaneous Localization And Mapping (SLAM) algorithm using a laser range scanner is developed, capable of producing a map of the traversed environment, and estimating the position of the MAV within the evolving map. The algorithm's accuracy is quantitatively assessed using several dedicated metrics, showing significant advantages over current methods. Repeatability and robustness are shown using a set of 12 repeated experiments in a benchmark scenario. The SLAM algorithm is primarily based on an innovative scan matching approach, dubbed Perimeter Based Polar Scan Matching (PB-PSM), which introduces a maximum overlap term to the cost function. This term, along with a tailored cost minimization technique, are found to yield highly accurate solutions for scan matching pairs of range scans. The algorithm is extensively tested on both ground and aerial platforms, in indoor as well as outdoor scenarios, using both in-house and previously published datasets, utilizing several different laser scanners. The SLAM algorithm is then coupled with a global A* path planner, and applied on a single rotor helicopter, performing targeted flight missions using a pilot-in-the- loop implementation. Targeted flight is defined as navigating to a goal position, defined by relative distance from a known initial position. It differs from the more common task of mapping, as it may not rely on loop closure opportunities to smooth out errors and optimize the generated map. Therefore, the importance of position estimates accuracy increases dramatically. The complete algorithm is then used for targeted flight experiments with a pilot in the loop. The algorithm presents the pilot with nothing but heading information. In order to further prevent the pilot from interfering with the obstacle avoidance task, the evolving map and position are not shown to the human pilot. Furthermore, the scenario is introduced with artificial (invisible) obstacles, apparent only to the path planner. The pilot therefore has to adhere to the path planner directions in order to reach the goal while avoiding all obstacles. The resulting paths show the helicopter successfully avoid both real and artificial obstacles, while following the planned path to the goal.