Hydrobatics refers to agile maneuvering of under-water vehicles just like aerobatics represents agile maneuvering of aerial vehicles. Performance trade-offs between flight and hover style autonomous underwater vehicles (AUVs) means that either maneuverability or range is compromised. Hydrobatic capabilities in flight style AUVs can bridge this gap and lead to more efficient and agile vehicles; thereby encouraging disruptive designs. As this is a relatively new area of research with very limited published research work, the focus of this paper is to present a multidisciplinary literature review to provide a path forward for further research. Relevant impact areas in ocean production, environmental sensing and security are discussed. Technical challenges are described in underactuated control and flight dynamics modeling. Synergies and opportunities are explored with aerospace engineering, robotics and artificial intelligence. As a part of this study, a simulation and verification framework is suggested and ongoing work is presented.

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.

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 AUVsare very agile, and can perform challenging maneuvers that encompass the full 0 degrees-360 degrees flight envelope. Such AUVs can be beneficial in novel use cases in ocean production, environmental sensing, and security, by enabling new capabilities for docking, inspection, or under-ice operations. To further explore their capabilities in such scenarios, it is crucial to be able to model their flight dynamics over the full envelope, which includes strong nonlinear effects and turbulence at high angles of attack. With accurate and efficient simulation models, new hydrobatic maneuvers can be generated and control strategies can be developed. Therefore, this article contributes with a strategy to perform efficient and accurate simulations of hydrobatic maneuvers in real time. A multifidelity hydrodynamic database is synthesized by combining analytical, semiempirical, and numerical methods, thereby capturing fluid forces and moments over the full envelope. A component buildup workflow is used to assemble a nonlinear flight dynamics model using lookup tables generated from the database. This simulation model is used to perform real-time simulations of advanced hydrobatic maneuvers. Simulation results show agreement with literature and experiment, and the simulator shows utility as a development tool in designing new maneuvers and control strategies.

Autonomous underwater vehicles (AUVs) show much promise in applications in environmental sensing, aquacultures, and security. Robust and adaptive control strategies can benefit these scenarios immensely by increasing the autonomy and en- durance. However, AUVs are nonlinear systems whose dynamics are challenging to model, and in several cases, also underactu- ated. Data-driven methods such as the Sparse Identification of Nonlinear Dynamics (SINDy), nonlinear least squares regression and Gaussian processes (GPs) can be beneficial to learn the AUV dynamics from measured data. Further, such data-driven models can be integrated into an adaptive model predictive control (MPC) scheme that drives the system to a setpoint while updating the prediction model when new measurements are available. This paper evaluates the performance of such data-driven methods for nonlinear system identification of two 6-DOF AUV systems, and implements them in an adaptive MPC scheme. Real experimental data from the SAM AUV and the MOLA AUV are used for performance evaluation. 

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. 

This paper investigates using satellite data to improve adaptive sampling missions, particularly for front tracking scenarios such as with algal blooms. Our proposed solution to find and track algal bloom fronts uses an Autonomous Underwater Vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a and satellite data. The proposed method learns the kernel parameters for a Gaussian process (GP) model using satellite images of chlorophyll a from the previous days. Then, using the data collected by the AUV, it models chlorophyll a concentration online. We take the gradient of this model to obtain the direction of the algal bloom front and feed it to our control algorithm. The performance of this method is evaluated through realistic simulations for an algal bloom front in the Baltic sea, using the models of the AUV and the chlorophyll a sensor. We compare the performance of different estimation methods, from GP to curve interpolation using least squares. Sensitivity analysis is performed to evaluate the impact of sensor noise on the methods’ performance. We implement our method on an AUV and run experiments in the Stockholm archipelago in the summer of 2022. 

Cyber-physical systems (CPSs) encompass a network of sensors and actuators that are monitored, controlled and integrated by a computing and communication core. As autonomous underwater vehicles (AUVs) become more intelligent and connected, new use cases in ocean production, security and environmental monitoring become feasible. Swarms of small, affordable and hydrobatic AUVs can be beneficial in substance cloud tracking and algae farming, and a CPS linking the AUVs with multi-fidelity simulations can improve performance while reducing risks and costs. In this paper, we present a CPS concept tightly linking the AUV network in ROS to virtual validation using Simulink and Gazebo. A robust hardware-software interface using the open-source UAVCAN-ROS bridge is described for enabling hardware-in-the-loop validation. Hardware features of the hydrobatic SAM AUV are described, with a focus on subsystem integration. Results presented include pre-tuning of controllers, validation of mission plans in simulation and real time subsystem performance in tank tests. These first results demonstrate the interconnection between different system elements and offer a proof of concept.

Cyber-physical systems (CPSs) comprise a network of sensors and actuators that are integrated with a computing and communication core. Hydrobatic Autonomous Underwater Vehicles (AUVs) can be efficient and agile, offering new use cases in ocean production, environmental sensing and security. In this paper, a CPS concept for hydrobatic AUVs is validated in real-world field trials with the hydrobatic AUV SAM developed at the Swedish Maritime Robotics Center (SMaRC). We present system integration of hardware systems, software subsystems for mission planning using Neptus, mission execution using behavior trees, flight and trim control, navigation and dead reckoning. Together with the software systems, we show simulation environments in Simulink and Stonefish for virtual validation of the entire CPS. Extensive field validation of the different components of the CPS has been performed. Results of a field demonstration scenario involving the search and inspection of a submerged Mini Cooper using payload cameras on SAM in the Baltic Sea are presented. The full system including the mission planning interface, behavior tree, controllers, dead-reckoning and object detection algorithm is validated. The submerged target is successfully detected both in simulation and reality, and simulation tools show tight integration with target hardware.

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.