Figure 1: Example of two trajectories of BlueROV surveying oyster-reef in a pool. On the bottom left is the current frame in the intermediate representation that the robot observes.

Autonomous navigation in the underwater environment is challenging due to limited visibility, dynamic changes, and the lack of a cost-efficient accurate localization system. We introduce UIVNAV, a novel end-to-end underwater navigation solution designed to drive robots over Objects of Interest (OOI) while avoiding obstacles, without relying on localization. UIVNAV uses imitation learning and is inspired by the navigation strategies used by human divers who do not rely on localization. UIVNAV consists of the following phases: (1) generating an intermediate representation (IR), and (2) training the navigation policy based on human-labeled IR.

By training the navigation policy on IR instead of raw data, the second phase is domain-invariant, the navigation policy does not need to be retrained if the domain or the OOI changes. We show this by deploying the same navigation policy for surveying two different OOIs, oyster and rock reefs, in two different domains, simulation, and a real pool. We compared our method with complete coverage and random walk methods which showed that our method is more efficient in gathering information for OOIs while also avoiding obstacles. The results show that UIVNAV chooses to visit the areas with larger area size of oysters or rocks with no prior information about the environment or localization. Moreover, a robot using UIVNAV compared to complete coverage method surveys on average 36% more oysters when traveling the same distances. We also demonstrate the feasibility of real-time deployment of UIVNAV in pool experiments with BlueROV underwater robot for surveying a bed of oyster shells

Xiaomin Lin, Nare Karapetyan, Kaustubh Joshi, Tianchen Liu, Nikhil Chopra, Miao Yu, Pratap Tokekar, Yiannis Aloimonos