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This paper details the technical architecture of autonomous underwater vehicles designed to integrate advanced state estimation, sonar-based object detection, and vision-driven task planning for a robotics competition. By addressing mechanical stability through CFD optimization and enhancing acoustic tracking with custom PCBs, the project demonstrates significant strides in reliable multi-modal autonomy within constrained aquatic environments. The work represents a practical application of machine learning algorithms specifically tailored for underwater navigation and manipulation challenges rather than general-purpose AI research or quantum computing advancements.
Technical Design Review of Duke Robotics Club's Oogway & Crush: AUVs for RoboSub 2026
arXiv:2607.18075v1 Announce Type: new Abstract: The Duke Robotics Club presents Oogway and Crush, our AUVs for RoboSub 2026. This year's strategy expands on our previously narrowed scope, targeting all four of RoboSub's design goals for the first time: movement, vision, manipulation, and acoustic tracking. This expansion is based on sustained reliability investment across all three subsystems. Mechanically, Crush gained two additional thrusters and a CFD-optimized case, providing pitch stability. Electrically, we addressed accumulated failure points by repairing unreliable connections and upgraded thruster control hardware. We also redesigned our acoustics system, adding a new custom PCB with higher-order filters, significantly improving pinger detection reliability. On the software side, improvements to state estimation, sonar-based object detection, vision-driven task planning, and IVC enable more capable and coordinated autonomous runs. Paired with investments in our testing infrastructure to maximize our limited pool time, we can now attempt a broader set of tasks while maintaining the reliability our competition strategy demands.
Abstract:The Duke Robotics Club presents Oogway and Crush, our AUVs for RoboSub 2026. This year's strategy expands on our previously narrowed scope, targeting all four of RoboSub's design goals for the first time: movement, vision, manipulation, and acoustic tracking. This expansion is based on sustained reliability investment across all three subsystems. Mechanically, Crush gained two additional thrusters and a CFD-optimized case, providing pitch stability. Electrically, we addressed accumulated failure points by repairing unreliable connections and upgraded thruster control hardware. We also redesigned our acoustics system, adding a new custom PCB with higher-order filters, significantly improving pinger detection reliability. On the software side, improvements to state estimation, sonar-based object detection, vision-driven task planning, and IVC enable more capable and coordinated autonomous runs. Paired with investments in our testing infrastructure to maximize our limited pool time, we can now attempt a broader set of tasks while maintaining the reliability our competition strategy demands.
Robotics (cs.RO)
arXiv:2607.18075 [cs.RO]
(or arXiv:2607.18075v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18075 arXiv-issued DOI via DataCite (pending registration)
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