Enabling autonomous robots to safely and efficiently navigate, explore, and map underwater caves is of significant importance to water resource management, hydrogeology, archaeology, and marine robotics. In this work, we demonstrate the system design and algorithmic integration of a visual servoing framework for semantically guided autonomous underwater cave exploration. We present the hardware and edge-AI design considerations to deploy this framework on a novel AUV (Autonomous Underwater Vehicle) named CavePI. The guided navigation is driven by a computationally light yet robust deep visual perception module, delivering a rich semantic understanding of the environment. Subsequently, a robust control mechanism enables CavePI to track the semantic guides and navigate within complex cave structures. We evaluate the system through field experiments in natural underwater caves and spring-water sites and further validate its ROS (Robot Operating System)-based digital twin in a simulation environment. Our results highlight how these integrated design choices facilitate reliable navigation under feature-deprived, GPS-denied, and low-visibility conditions.

Research on coastal regions traditionally involves methods like manual sampling, monitoring buoys, and remote sensing, but these methods face challenges in spatially and temporally diverse regions of interest. Autonomous surface vehicles (ASVs) with artificial intelligence (AI) are being explored, and recognized by the International Maritime Organization (IMO) as vital for future ecosystem understanding. However, there is not yet a mature technology for autonomous environmental monitoring due to typically complex coastal situations: (1) many static (e.g., buoy, dock) and dynamic (e.g., boats) obstacles not compliant with the rules of the road (COLREGs); (2) uncharted or uncertain information (e.g., non-updated nautical chart); and (3) high-cost ASVs not accessible to the community and citizen science while resulting in technology illiteracy. To address the above challenges, my research involves both system and algorithmic development: (1) a robotic boat system for stable and reliable in-water monitoring, (2) maritime perception to detect and track obstacles (such as buoys, and boats), and (3) navigational decision-making with multiple-obstacle avoidance and multi-objective optimization.