Autonomous Underwater Vehicle
Autonomous Underwater Vehicles (AUVs) are robotic systems designed for independent operation in aquatic environments, primarily focused on efficient task completion and robust navigation in challenging conditions. Current research emphasizes improving AUV control through reinforcement learning and advanced planning algorithms, including the use of neural networks for tasks like visual odometry, object detection, and multi-sensor fusion, often incorporating techniques like factor graph optimization. These advancements are crucial for expanding AUV applications in diverse fields, such as ocean exploration, underwater infrastructure inspection, and marine resource management, by enhancing their autonomy, reliability, and operational efficiency.
Papers
Oogway: Designing, Implementing, and Testing an AUV for RoboSub 2023
Will Denton, Lilly Chiavetta, Michael Bryant, Vedarsh Shah, Rico Zhu, Ricky Weerts, Phillip Xue, Vincent Chen, Hung Le, Maxwell Lin, Austin Camacho, Drew Council, Ethan Horowitz, Jackie Ong, Morgan Chu, Alex Pool
Technical Design Review of Duke Robotics Club's Oogway: An AUV for RoboSub 2024
Will Denton, Michael Bryant, Lilly Chiavetta, Vedarsh Shah, Rico Zhu, Philip Xue, Vincent Chen, Maxwell Lin, Hung Le, Austin Camacho, Raul Galvez, Nathan Yang, Nathanael Ren, Tyler Rose, Mathew Chu, Amir Ergashev, Saagar Arya, Kaelyn Pieter, Ethan Horowitz, Maanav Allampallam, Patrick Zheng, Mia Kaarls, June Wood