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Filtering by: Advisor Hollinger, Geoffrey A. Remove constraint Advisor: Hollinger, Geoffrey A. Decade 2010-2019 Remove constraint Decade: 2010-2019

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  • Augmented Deep Learning Techniques for Robotic State Estimation

    Creator:
    Nicolai, Austin L.
    Abstract:
    While robotic systems may have once been relegated to structured environments and automation style tasks, in recent years these boundaries have begun to erode. As robots begin to operate in largely unstructured environments, it becomes more difficult for them to effectively interpret their surroundings. As sensor technology improves, the amount...
    Resource Type:
    Dissertation
    Full Text:
    Deep Learning Techniques for Robotic State Estimation Abstract approved: Geoffrey A. Hollinger While

  • Modeling human expertise for providing adaptive levels of robot shared autonomy

    Creator:
    Milliken, Lauren
    Abstract:
    In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when...
    Resource Type:
    Masters Thesis
    Full Text:
    Providing Adaptive Levels of Robot Shared Autonomy Abstract approved: Geoffrey A Hollinger In shared

  • Planning Under Uncertainty for Unmanned Aerial Vehicles

    Creator:
    Skeele, Ryan
    Abstract:
    Unmanned aerial vehicle (UAV) technology has grown out of traditional research and military applications and has captivated the commercial and consumer markets, showing the ability to perform a spectrum of autonomous functions. This technology has the capability of saving lives in search and rescue, fighting wildfires in environmental monitoring, and...
    Resource Type:
    Masters Thesis
    Full Text:
    Unmanned Aerial Vehicles Abstract approved: Geoffrey Hollinger Unmanned aerial vehicle (UAV) technology

  • Model Predictive Control for Underwater Robots in Ocean Waves

    Creator:
    Fernández, Daniel C.
    Abstract:
    Underwater robots beneath ocean waves can benefit from feedforward control to reduce position error. This thesis proposes a method using Model Predictive Control (MPC) to predict and counteract future disturbances from an ocean wave field. The MPC state estimator employs a Linear Wave Theory (LWT) solver to approximate the component...
    Resource Type:
    Masters Thesis
    Full Text:
    Waves Abstract approved: Geoffrey A. Hollinger Underwater robots beneath ocean waves can benefit from

  • Deep learning for robotic exploration

    Creator:
    Caley, Jeffrey A.
    Abstract:
    Emerging applications for robotic data collection include ocean monitoring, emergency response and urban search and rescue. At the core of these applications is a robot's ability to make informed decisions on incomplete data. This dissertation addresses this problem by developing novel techniques for modeling and estimating structured environments using deep...
    Resource Type:
    Dissertation
    Full Text:
    Exploration Abstract approved: Geoffrey A Hollinger Emerging applications for robotic data collection

  • Non-Myopic Distributed Coordination and Planning for Robotic Teams

    Creator:
    Smith, Andrew
    Abstract:
    As robotic systems become increasingly prevalent in our lives (e.g., by harvesting food, assisting with disaster response and defense, and transporting persons and our goods around the world) there is a growing need to ensure that they can cooperate to achieve their intended goals. Robotic cooperation in the real-world is...
    Resource Type:
    Dissertation
    Full Text:
    : Non-Myopic Distributed Coordination and Planning for Robotic Teams Abstract approved: Geoffrey A

  • Adaptive Multiagent Traffic Management for Autonomous Robotic Systems

    Creator:
    Rebhuhn, Carrie
    Abstract:
    There is growing commercial interest in the use of unmanned aerial vehicles (UAVs) in urban environments, specifically for package delivery applications. However, the size, complexity and sheer numbers of expected UAVs makes conventional air traffic management that relies on human air traffic controllers infeasible. To enable UAVs to safely and...
    Resource Type:
    Dissertation
    Full Text:
    : Kagan Tumer Geoff Hollinger There is growing commercial interest in the use of unmanned aerial vehicles

  • Multiagent learning for locomotion and coordination in tensegrity robotics

    Creator:
    Iscen, Atil
    Abstract:
    Tensegrity structures are composed of pure compressional elements that are connected via a network of pure tensional elements. The concept of tensegrity promises numerous advantages to the field of robotics. Tensegrity robots are, however, notoriously difficult to control due to their oscillatory nature and nonlinear interaction between the components. Multiagent...
    Resource Type:
    Dissertation
    Full Text:
    : Kagan Tumer Geoffrey Hollinger Tensegrity structures are composed of pure compressional elements that