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
Diver Interest via Pointing in Three Dimensions: 3D Pointing Reconstruction for Diver-AUV Communication
Chelsey Edge, Demetrious Kutzke, Megdalia Bromhal, Junaed Sattar
Sim-to-Real Transfer of Adaptive Control Parameters for AUV Stabilization under Current Disturbance
Thomas Chaffre, Jonathan Wheare, Andrew Lammas, Paulo Santos, Gilles Le Chenadec, Karl Sammut, Benoit Clement