O’Neill Brendan

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O’Neill
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Brendan
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  • Thesis
    Signal Absorption-Based Range Estimator for Undersea Swarms
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2020-09) O’Neill, Brendan
    Robotic swarms are increasingly complex above the waterline due to reliable communication links. However, the limited propagation of similar signals in the ocean has impacted advances in undersea robotics. Underwater vehicles often rely on acoustics for navigation solutions; however, this presents challenges for robotic swarms. Many localization methods rely on precision time synchronization or two-way communication to estimate ranges. The cost of Chip-scale Atomic Clocks (CSACs) and acoustic modems is limiting for large-scale swarms due to the cost-per-vehicle and communications structure. We propose a single vehicle with reliable navigation as a "leader" for a scalable swarm of lower-cost vehicles that receive signals via a single hydrophone. This thesis outlines range estimation methods for sources with known signal content, including frequency and power at its origin. Transmission loss is calculated based on sound absorption in seawater and geometric spreading loss to estimate range through the Signal Absorption-Based Range Estimator (SABRE). SABRE's objective is to address techniques that support low-cost undersea swarming. This thesis's contributions include a novel method for range estimation onboard underwater vehicles that supports relative navigation through Doppler-shift methods for target bearing. This thesis develops the theory, algorithms, and analytical tools for real-world data range estimation.
  • Thesis
    Adaptive AUV-assisted diver navigation for loosely-coupled teaming in undersea operations
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2023-09) O’Neill, Brendan ; Leonard, John J.
    Human divers face immense challenges in the undersea domain due to constraints on life support, sensory input, and mobility. Due to these challenges, even simple tasks are difficult, and navigation between points of interest is key among them. However, humans have progressively utilized creativity, innovation, and research to explore the Earth’s oceans at greater depths and with increased spatial and temporal detail. Autonomous underwater vehicles often lack the tools, dexterity, or flexibility to manage specific tasks or unforeseen circumstances. However, advances in inertial navigation, computation, and acoustic communication enable autonomous underwater vehicles to perform tasks outside human capability. Acoustic modem technology allows for flexible and reliable communication over an acoustic link. We propose algorithms for cooperative navigation between a diver and an autonomous underwater vehicle as a pathway toward complex undersea human-robot teams. This thesis identifies the communication, software, and algorithmic tools to enable loosely-coupled cooperative navigation between an autonomous underwater vehicle and a diver without a surface presence. Divers present new challenges for cooperative navigation based on their unique motion profile and variable pace from diver to diver. By leveraging the vehicle’s sensor suite, acoustic modem technology, and nonlinear least-squares state estimation, we enable enhanced diver localization and navigation without a surface presence. Adaptation to environmental impacts is explored through measured ocean currents as well as updates to the diver’s motion model based on state estimation analysis. These adaptations produce more efficient diver transits with fewer heading changes. In addition, maneuvering strategies for autonomous underwater vehicles are explored to assess their impact on diver localization accuracy. Experimental validation is shown through surface platforms as proxies for the autonomous underwater vehicle and diver, demonstrating the localization accuracy within a few meters for experiments under various operating conditions. These contributions provide a foundation for undersea human-robot teams to engage in complex tasks with greater efficiency through their combined strengths.