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Filtering by: Creator Attardo, Geoffrey M. Remove constraint Creator: Attardo, Geoffrey M. Degree Field Robotics Remove constraint Degree Field: Robotics Resource Type Masters Thesis Remove constraint Resource Type: Masters Thesis

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  • Communication Planning for Cooperative Terrain-Based Underwater Localization

    Creator:
    Anderson, Jacob
    Abstract:
    This thesis presents a decentralized communication planning algorithm for cooperative terrain-based navigation (dec-TBN) with autonomous underwater vehicles. The proposed algorithm uses forward simulation to approximate the value of communicating at each time step. The simulations are used to build a directed acyclic graph that can be searched to provide a...
    Resource Type:
    Masters Thesis

  • Deployment Planning for Multiple Marsupial Robots

    Creator:
    Lee, Chris (Yu Hsuan)
    Abstract:
    Multi-robot systems are versatile and extremely capable of exploration tasks in complex environments. Increasingly sophisticated planners, which incorporate new features of a multi-robot system, are necessary for the operation of the systems. Marsupial robots are multi-robot systems consisting of a carrier robot (e.g., a ground vehicle), which is highly capable...
    Resource Type:
    Masters Thesis
    Full Text:
    Multiple Marsupial Robots Abstract approved: Geoffrey Hollinger Multi-robot systems are versatile and

  • 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

  • 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

  • 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

  • Robots Against Infectious Diseases : A Technologically Grounded, Human-Centered Exploration

    Creator:
    Kraft, Kory
    Abstract:
    Robots have the potential to protect health care workers, provide patient care, and ultimately save lives in infectious disease outbreaks. Nevertheless, infectious disease outbreak scenarios present unique technological and social challenges for robotics. This work explores what robots can and should do in the fight against infectious diseases. We present...
    Resource Type:
    Masters Thesis

  • Terrain-Aware Probabilistic Search Planning for Unmanned Aerial Vehicles

    Creator:
    Schomer, Nathan L.
    Abstract:
    Mountain search and rescue is a vital form of emergency response to assist people in austere environments (e.g., extreme terrain, poor weather). Volunteer mountain search and rescue teams in the United States have begun adopting consumer- grade unmanned aerial vehicles to assist a variety of tasks (e.g., search, resource delivery);...
    Resource Type:
    Masters Thesis

  • Multiagent Learning via Dynamic Skill Selection

    Creator:
    Sachdeva, Enna
    Abstract:
    Multiagent coordination has many real-world applications such as self-driving cars, inventory management, search and rescue, package delivery, traffic management, warehouse management, and transportation. These tasks are generally character-ized by a global team objective that is often temporally sparse - realized only upon completing an episode. The sparsity of the shared...
    Resource Type:
    Masters Thesis