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Filtering by: Creator Attardo, Geoffrey M. Remove constraint Creator: Attardo, Geoffrey M. Advisor Hollinger, Geoffrey A. Remove constraint Advisor: Hollinger, Geoffrey A. Degree Field Robotics Remove constraint Degree Field: Robotics

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  • Efficiently Learning Human Preferences for Robot Autonomy

    Creator:
    Somers, Thane N.
    Abstract:
    Human-robot teams are invaluable for mapping unknown environments, exploring difficult-to-reach areas, and manipulating inaccessible equipment. However, guiding autonomous robots requires dealing with these dynamic domains while synthesizing a significant amount of data and balancing competing objectives. Current mission planning methods often involve manually specifying low-level parameters of the mission, such...
    Resource Type:
    Masters Thesis
    Full Text:
    Human Preferences for Robot Autonomy Abstract approved: Geoffrey A. Hollinger Human-robot teams are

  • 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

  • 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

  • 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

  • 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

  • Topologically-Guided Robotic Information Gathering

    Creator:
    McCammon, Seth
    Abstract:
    Information gathering tasks, such as terrestrial search and rescue, aerial inspection, and marine monitoring, require robotic unmanned systems to make decisions on how to travel within an environment to maximize or minimize a path-dependent information objective function. The distribution of information throughout the environment is the result of various processes,...
    Resource Type:
    Dissertation
    Full Text:
    -Guided Robotic Information Gathering Abstract approved: Geoffrey A. Hollinger Information gathering

  • Learning-based Techniques for Fast and Robust Motion Planning

    Creator:
    Chow, Scott
    Abstract:
    Motion planning is a cornerstone of autonomous robots, enabling robots to safely and efficiently perform tasks such as package delivery, infrastructure inspection, and manipulation. However, as the field of robotics matures, robotic systems are being developed that (1) are challenging to analytically model, (2) require computationally expensive model-based controllers, and...
    Resource Type:
    Dissertation
    Full Text:
    Techniques for Fast and Robust Motion Planning Abstract approved: Geoffrey A. Hollinger Motion planning is

  • Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

    Creator:
    Senewiratne, Yomal K.
    Abstract:
    We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
    Resource Type:
    Masters Thesis
    Full Text:
    approved: Geoffrey A. Hollinger We present a method for decentralized, multi-robot exploration in adverse