Phillips, John, authorGallegos, Erika, advisorSimske, Steven, committee memberVans, Marie, committee memberWise, Dan, committee member2025-09-012025-09-012025https://hdl.handle.net/10217/241856https://doi.org/10.25675/3.02176The urgency to more rapidly field capability is critical to the US Navy's future; particularly as senior naval leadership has challenged the naval enterprise to accelerate fielding of robotics and autonomous systems. However, traditional requirements, resourcing, and acquisition processes often take over a decade to field needed capability. The objective of this dissertation is to describe a novel framework developed to meet this challenge, which is demonstrated using a case study for fielding small Unmanned Surface Vehicles (sUSVs) for the US Pacific Fleet from 2022 to 2025. The framework begins with an adaptation of the innovation pipeline, executing a 12-week sprint process of problem sourcing, curation, discovery, incubation, and scaling. The results of the sprint process provide the case that a solution has warfighting utility, is technically feasible, and has a path to scale. This output is provided to leadership to make an informed decision to transition into a prototype project phase, which aims to continue learning while validating sprint results. The second part of this framework uses the sUSV prototypes to build a campaign of learning, leveraging Fleet experiments while implementing a DevOps model utilizing both government and industry to rapidly learn, adapt, and improve the capability. This step focused on demonstrating warfighting utility, technical feasibility, and building advocacy to the path to scale. The third step implements an in-parallel but collaborative approach to acquisition, systems engineering, and Fleet adoption, leveraging the lessons learned from the previous steps to more rapidly field and employ the sUSV capability at scale. This dissertation provides an overview and lessons learned from a real Navy case study in which time to field capability was reduced significantly. While this may not be applicable to all Navy programs, this research offers insights to help the naval enterprise when challenged to rapidly field capability.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.boatsroboticsUSVNavyautonomousunmannedText