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.

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 AUVsare very agile, and can perform challenging maneuvers that encompass the full 0 $^{\circ}$ –360 $^{\circ}$ 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.

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.

As vehicles become electrified and more intelligent in terms of sensing, actuation and processing; a number of interesting possibilities arise in controlling vehicle dynamics and driving behavior. Over-actuation with in- wheel motors, all wheel steering and active camber is one such possibility, which facilitate the control strategies that push boundaries in energy consumption and safety. Optimal control can be used to investigate the best combinations of control inputs to an over-actuated system. This paper shows how an optimal control problem can be formulated and solved for an over-actuated vehicle case, and highlights the translation of this optimal solution to a real-world scenario, enabling intelligent means to improve vehicle efficiency.This paper gives an insight into Dynamic Programming (DP) as an offline optimal control method that guarantees the global optimum. Therefore the optimal control allocation to minimize an objective function and simultaneously fulfill the defined constraints can be achieved. As a case study the effects of over-actuation on the cornering resistance were investigated in two different maneuvers i.e. step steer and sine with dwell, where in both cases the vehicle assumes to be in steady state situation. In this work the cornering resistance is the main objective function and maintaining the reference trajectory is the constraint which should be fulfilled. A parameter study is conducted on the benefits of over-actuation, and depending on the type of over-actuation about 15% to 50% reduction in cornering resistance were observed during step steer and sine with dwell maneuver respectively. From a second parameter study that focused on COG position from a safety perspective, it is more beneficial for the vehicle to be designed to under-steer than over-steer. Finally, a method is described to translate the offline optimal results to vehicle implementable controllers in the form of both feed-through lookup-tables and rule-based feed-forward control.