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Filtering by: Advisor Hollinger, Geoffrey A. Remove constraint Advisor: Hollinger, Geoffrey A. Academic Affiliation Mechanical, Industrial, and Manufacturing Engineering Remove constraint Academic Affiliation: Mechanical, Industrial, and Manufacturing Engineering Degree Level Master's Remove constraint Degree Level: Master's

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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

  • 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