Ivan Stenius

In the near future, most deployed spacecraft will be autonomous systems. Their tasks will involve autonomous rendezvous and proximity operations with large structures, such as inspection, assembly, and maintenance of orbiting space stations, as well as human-assistance tasks over shared workspaces. Yet testing these capabilities remains challenging since microgravity conditions are difficult to simulate on Earth. Free-flying platforms, which replicate microgravity environments through nearly frictionless planar motion, have been used to provide a way to easily test and experiment on these systems without being in orbit. To promote replicable and reliable scientific results for autonomous control of spacecraft, we present the design of a space robotics laboratory based on open-source and modular software and hardware - the Autonomy Testbed for Multipurpose Orbiting Systems (ATMOS). ATMOS uses thrusters and air bearings to achieve near-frictionless motion, thereby simulating spacecraft dynamics in two dimensions and enabling realistic testing of navigation and control algorithms on Earth. The simulation software provides a software-in-the-loop architecture that seamlessly transfers simulated results to the hardware. Our results provide an insight into the performance of such a system, including comparisons of hardware and software results, as well as control and planning methodologies for controlling free-flying platforms.

Retrieving Autonomous Underwater Vehicles (AUVs) using Uncrewed 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. Our code is available at video https://youtu.be/w-ZA1QxNfQQ

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.

Fuel cell-powered Autonomous Underwater Vehicles (AUVs) represent a growing area of research as fuel cells can increase their endurance. Fuel cells consume hydrogen and oxygen to generate electricity. Typically, the fuel cell generates as much heat as electrical energy, and heat management becomes a crucial parameter when designing AUVs. For underwater applications, there is a need to store both gases and several types of storage units with different characteristics exist which have impacts on the energy density and heat behavior. This study aims at including the heat properties of the storage units in the design process of fuel cell-powered AUVs. A heat balance over the energy system of an AUV is calculated for each combination of hydrogen and oxygen storage units. In addition, a multi-criteria decision-making analysis is conducted, considering the calculated total heat, the specific energy, the energy density and the volumetric mass of each combination of storage units as criteria, enabling a comparison and ranking them using two objective criteria weighting methods. Results show that the fuel cell is the major contributor to the heat balance, and that the combinations of liquid oxygen with liquid or compressed hydrogen can be relevant and suitable for underwater applications.

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.

Autonomous underwater vehicles (AUVs) are utilized in a variety of unmanned missions, such as environmental surveys or guarding coastal waters. Currently, the most commonly used energy storage is a secondary battery pack. A potential solution for increasing the range even further on larger AUVs is to utilize hybrid fuel cell/battery systems. Fuel cell systems can have a significantly increased specific energy compared to batteries and are now starting to become well-developed technologies. However, in an underwater environment, both hydrogen and oxygen need to be stored for the fuel cell. This study considers the sizing of a hybrid fuel cell/battery system with respect to operational power profiles, i.e., how to select the best combination of fuel cell stack, battery pack, and reactant storage, and compares its volume and weight with conventional battery systems. This article presents a sizing strategy developed for this purpose, where the optimum combination of fuel cell output power and battery size is analyzed. The strategy is implemented on power profiles from real AUV missions. The sizing strategy will outline the techniques to reach the optimum hybrid configuration capable of meeting the power demand at any time of the mission. Results show that for longer missions, the volume of the hybrid system becomes significantly lower than for the battery system, meaning that the endurance can be increased thanks to the use of fuel cells.

This paper aims to examine and document the current state of practice of model‐based systems engineering (MBSE), and how organizations look toward the future. The paper is based on a survey of how MBSE has been applied and factors that influenced the perceived benefits and results. The survey was designed to evaluate the maturity and status of the implementation of MBSE (also called model‐centric systems engineering), within industry, academia, and governments. The approach to the survey development is described along with the survey results. The study indicates that one of the main hurdles to introducing a model‐based approach is the lack of clear and adopted organizational structures and an understanding of required conditions and needs at a management level. The survey also indicates that 50–75% of the respondents noted some improvement or a significant improvement across almost all systems engineering tasks.