Autonomous underwater vehicles (AUVs) show much promise in environmental sensing, aquaculture, and security applications. Robust and adaptive control strategies can immensely benefit these scenarios by increasing autonomy and endurance. However, AUVs are nonlinear systems whose dynamics are challenging to model, especially during agile maneuvers at high angles of attack. To better capture these nonlinear effects, this paper proposes a physics-informed system identification scheme that combines prior knowledge of the system dynamics with data-driven regression. Strategies including Sparse Identification of Nonlinear Dynamics (SINDy), nonlinear least squares regression, and Gaussian processes (GPs) are used to learn the AUV dynamics online from measured data. These data-driven models are then implemented in an adaptive model predictive controller (MPC) for agile maneuvering that drives the system to a set point while updating the prediction model when new measurements are available. The performance of these three system identification strategies is evaluated on two different 6-DOF AUV platforms. All three strategies show good real-time performance, while the GP model offers the best balance between accuracy, speed and robustness. Field experimental data from the SAM AUV and the MOLA AUV are used for performance evaluation.

Retrieving Autonomous Underwater Vehicles (AUVs) using Unmanned Aerial Vehicles (UAVs) presents significant challenges due to wave-induced motion and wind disturbances during recovery. While prior research has primarily addressed air-to-air refueling and ground-to-air deployments, this work introduces an eagle-inspired UAV trajectory generation and control system specifically designed for AUV retrieval. Drawing inspiration from avian flight dynamics, the proposed approach mitigates the effects of downward airflow and environmental disturbances, enabling a stable and efficient recovery process. We propose a novel trajectory planning method that minimizes snap, integrates a cost function to account for the UAV’s airflow effects on the target, and adapts dynamically to wave-induced movements. A specialized connection mechanism, consisting of a hook-equipped UAV and a buoy-rope assembly attached to the AUV, is developed and validated through reduced-scale in-water field experiments. Post-connection, a suspended load controller ensures stability by compensating for shifts in inertia and external forces. Additionally, we present a Unity-based simulation environment that allows customization of vehicle parameters and mission scenarios. This work bridges the gap in maritime operations, offering a reliable and flexible solution for AUV recovery in dynamic marine environments.

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 appli- cations. To overcome this, we present a 2-layered BO IPP method that performs non-myopic, real-time planning in a tree Search fashion over large Stochastic Variational GP maps, while respecting the AUV motion 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. 

Developing new functionality for underwater robots and testing them in the real world is time-consuming and resource-intensive. Simulation environments allow for rapid testing before field deployment. However, existing tools lack certain functionality for use cases in our project: i) developing learning-based methods for underwater vehicles; ii) creating teams of autonomous underwater, surface, and aerial vehicles; iii) integrating the simulation with mission planning for field experiments. A holistic solution to these problems presents great potential for bringing novel functionality into the underwater domain. In this paper we present SMaRCSim, a set of simulation packages that we have developed to help us address these issues.

Terminal docking is an important step towards long-term underwater residency of Autonomous Underwater Vehicles (AUVs). An important part is to correctly estimate the relative position between the AUV and the docking station. While there are many solutions to this problem, it is unclear how they perform with respect to each other in terms of accuracy and computational performance. We propose a side by side comparison of a Rao-Blackwellized particle filter (RBPF) with a Maximum-A-Posteriori (MAP) method in a vision-based terminal homing scenario. Both methods are evaluated in a simulation study based on performance under different uncertainties. Subsequently, they are validated using real-world data from field tests. The comparison shows that in the simulation study, the smoothing performs more accurate than the RBPF, whereas on the experimental data, they perform equally. However, the smoothing requires less computational power compared to the RBPF.

The exploration of underwater environments presents unique challenges that conventional propeller-based underwater robots struggle to overcome due to their inherent limitations in maneuverability and environmental disturbance. This has lead to the interest in bioinspired robotic systems capable of multimodal locomotion, inspired by the versatile movements of marine animals that excel in both swimming and benthic locomotion. This article introduces the design framework of a novel bioinspired underwater vehicle, RoboIguana, which draws inspiration from the marine iguana's ability to perform both undulatory swimming and benthic legged locomotion. We present in detail the design of different subsystems of the robot, including legged and swimming locomotion, vision, electronics, and chassis. The integration of these subsystems aims to enable RoboIguana to navigate complex underwater terrains effectively, offering an innovative tool for marine exploration and research.

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in speed and range as well as agile in maneuvering, thereby enabling new use cases in ocean production, environmental sensing, and security. However, such robots are underactuated, have highly nonlinear dynamics at high angles of attack, and will be used in applications with high requirements for robustness. This paper explores the use of reinforcement learning (RL) to control hydrobatic AUVs, using the agile SAM AUV as a case study. The focus is on controlling the depth and pitch simultaneously, where there is a tight coupling between the states. This maneuver offers a simple, yet interesting test case to compare different control strategies. The twin-delay deep deterministic policy gradient (TD3) algorithm is applied to this AUV control problem. The resulting trained RL controller offers good robustness to noise and performs at a similar level as a Proportional-Integral-Derivative (PID) controller within the Stonefish simulation environment. The agent is also deployed and run on the robot hardware, with high overshoot. While the RL agent has good performance in simulation, the transfer from simulation to reality still leaves some open questions.

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.

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise-time, overshoot, steady-state error, and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.

This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup.