Informative path planning (IPP) applied to bathy-metric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) maps work well on small scenarios but they are short-sighted and computationally heavy when mapping larger areas, hindering deployment in real applications. To overcome this, we present a 2-layered BO IPP method that performs non-myopic, online planning in a tree search fashion over large Stochastic Variational GP maps, while respecting the AUV dynamical constraints and accounting for localization uncertainty. Our framework outperforms the standard industrial lawn-mowing pattern and a myopic baseline in a set of hardware in the loop (HIL) experiments in an embedded platform over real bathymetry areas.

The transition of seaweed farming to an alternative food source on an industrial scale relies on automating its processes through smart farming, equivalent to land agriculture. Key to this process are autonomous underwater vehicles (AUVs) via their capacity to automate crop and structural inspections. However, the current bottleneck for their deployment is ensuring safe navigation within farms, which requires an accurate, online estimate of the AUV pose and map of the infrastructure. To enable this, we propose an efficient side scan sonar-based (SSS) simultaneous localization and mapping (SLAM) framework that exploits the geometry of kelp farms via modeling structural ropes in the back-end as sequences of individual landmarks from each SSS ping detection, instead of combining detections into elongated representations. Our method outperforms state of the art solutions in hardware in the loop (HIL) experiments on a real AUV survey in a kelp farm. The framework and dataset can be found at https://github.com/julRusVal/sss_farm_slam.

Agile and hydrobatic maneuvering capabilities can enhance AUV operations in increasingly challenging scenarios. In this paper, we explore the ability of an underactuated AUV to transition to and hold a pitch angle close to 90 degrees at a particular depth, like an inverted pendulum. Holding such an orientation can be valuable in observing a calving glacier, under-ice launch and recovery, underwater docking, inspecting vertical structures, and observing targets above the water surface. However, such control is challenging because of underactuation, rapid response times and varying stability in different configurations. To address this, a control policy is derived offline using nonlinear MPC in a high-fidelity simulation environment in Simulink. For real-time control, a hybrid controller using a behavior tree (BT) is developed based on the optimal MPC policy and applied on the AUV system. The BT controller considers Safety, Transit and Stabilize behaviors. The control algorithm is validated with simulations in Simulink and Stonefish-ROS as well as field experiments with the hydrobatic SAM AUV, showing repeatable performance in the inverted pendulum maneuver.