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Unmanned Underwater Vehicles (UUVs) have a promising future to explore the polar regions. In this paper, we present our progress on developing a self-contain inertial odometry for under-ice navigation. Firstly, a microcontroller-based hardware time synchronization for multiple devices is demonstrated. Moreover, we present a new IMU, Doppler Velocity Log (DVL) and Pressure dead-reckoning (DR) for state estimation and a robust initialization approach for underwater vehciels. Field trials have been conducted in Utqiagvik, Alaska in March 2022 to gather multi-sensor data under the sea ice. In this paper, we highlight the performance of our method by comparing to the robot_localization algorithm, a widely used open-source localization algorithm. 

Seabed mapping is a common application for marine robots, and it is often framed as a coverage path planning problem in robotics. During a robot-based survey, the coverage of perceptual sensors (e.g., cameras, LIDARS and sonars) changes, especially in underwater environments. Therefore, online path planning is needed to accommodate the sensing changes in order to achieve the desired coverage ratio. In this paper, we present a sensing confidence model and a uncertainty-driven sampling-based online coverage path planner (SO-CPP) to assist in-situ robot planning for seabed mapping and other survey-type applications. Different from conventional lawnmower pattern, the SO-CPP will pick random points based on a probability map that is updated based on in-situ sonar measurements using a sensing confidence model. The SO-CPP then constructs a graph by connecting adjacent nodes with edge costs determined using a multi-variable cost function. Finally, the SO-CPP will select the best route and generate the desired waypoint list using a multi-variable objective function. The SO-CPP has been evaluated in a simulation environment with an actual bathymetric map, a 6-DOF AUV dynamic model and a ray-tracing sonar model. We have performed Monte Carlo simulations with a variety of environmental settings to validate that the SO-CPP is applicable to a convex workspace, a non-convex workspace, and unknown occupied workspace. So-CPP is found outperform regular lawnmower pattern survey by reducing the resulting traveling distance by upto 20%. Besides that, we observed that the prior knowledge about the obstacles in the environment has minor effects on the overall traveling distance. In the paper, limitation and real-world implementation are also discussed along with our plan in the future. 

An affordable Remotely Operated Vehicle (ROV) has been modified for under-ice sensing. In this paper, we present the system upgrade, including sensor integration, electronics and navigation stack. The new ROV is equipped with a Doppler Velocity Log (DVL) and an attitude heading reference system (AHRS) for navigation, and a stereo camera and a forward-looking imaging sonar for perception. Field experiments were conducted in March 2021 on a frozen waterway in Michigan. The ROV was controlled to stay within 2 meters away from the ice keel. Dead-reckoning navigation based on the DVL, AHRS and Extended Kalman Filter (EKF) are implemented with results presented in the paper. Using the navigation result and DVL beam range measurements, ice-thickness was estimated along the vehicle’s path. The ice thickness is found to be about 25 to 30 cm that is coincident with manual observation from drilled ice holes. Besides that, we also present and discuss interesting features embedded in the frozen ice observed by our stereo camera and the forward-looking imaging sonar.