The global expansion in offshore renewable energy, primarily through offshore wind, is associated with the proliferation of subsea power cables (SPCs) throughout marine and coastal benthic environments. The transmission of electrical power through these SPCs will introduce electromagnetic fields (EMFs) into the seabed and the adjacent water column, which raises questions regarding the potential impact on benthic fauna, particularly during critical developmental early-life stages for which research considering the effects of both the electric and magnetic components of SPC EMFs is lacking. We conducted an experiment on three benthic egg-laying species, – the elasmobranch Scyliorhinus canicula, the cephalopod Loligo vulgaris, and the cephalopod Sepia officinalis – found in areas under consideration for the routing of SPCs. We exposed the embryos to realistic EMF levels (magnetic field 4–6 μT) recreated in the laboratory using an AC power cable set-up that simulated the EMF conditions, and examined the morphological, physiological, and behavioural responses. Our findings indicate subtle responses to EMF exposure in S. canicula and L. vulgaris with faster growth rates and morphometric differences, but no responses in S. officinalis. Our results highlight the value of a multiple end point approach to determine the potential influence of chronic exposure to EMFs on embryogenesis in benthic fauna and provide a baseline for future studies to build upon. Although our study cannot extrapolate the consequences of individual-level effects to population-level impacts, it does underscore the necessity of realistic and longer-term studies to assess the potential consequences of EMFs to marine fauna.

Due to the challenges regarding the limits of their endurance and autonomous capabilities, underwater docking for autonomous underwater vehicles (AUVs) has become a topic of interest for many academic and commercial applications. Herein, we take on the problem of relative navigation for the generalized version of the docking operation, which we address as proximity operations. Proximity operations typically involve only two actors, a chaser and a target. We leverage the similarities to proximity operations (prox-ops) from spacecraft robotic missions to frame the diverse docking scenarios with a set of phases the chaser undergoes on the way to its target. We emphasize the versatility on the use of factor graphs as a generalized representation to model the underlying simultaneous trajectory estimation and relative navigation problem that arises with any prox-ops scenario, regardless of the sensor suite or the agents’ dynamic constraints. To emphasize the flexibility of factor graphs as the modeling foundation for arbitrary underwater prox-ops, we compile a list of state-of-the-art research in the field and represent the different scenario using the same factor graph representation. We detail the procedure required to model, design, and implement factor graph-based estimators by addressing a long-distance acoustic homing scenario of an AUV to a moving mothership using data sets from simulated and real-world deployments; an analysis of these results is provided to shed light on the flexibility and limitations of the dynamic assumptions of the moving target. A description of our front- and back-end is also presented together with a timing breakdown of all processes to show its potential deployment on a real-time system.

Open data are scarce in underwater robotics, making it more difficult for researchers to develop new methods that address real-world problems. In this work, we present the experimental design, execution, and curation of three datasets that represent different conditions in a realistic underwater dynamic proximity operation. The raw datasets gathered during the deployments are post-processed to improve consistency. We detail and use a joint batch optimization technique that uses a probabilistic approach to iteratively search for the set of optimal agent trajectories that are in best agreement with the relative measurements provided by a USBL positioning system and optical pose measurements from a fiducial light array. Error analysis of the relative measurements with respect to the baseline and optimized trajectories validate our results, effectively providing ground-truth trajectories of the agents. The resulting datasets, together with their documentation, are publicly available at github.com/aldoteran/asko_2024_datasets

Recent maritime incidents highlight the need for scalable underwater monitoring solutions. Traditional methods of ocean exploration face high costs and limited access, while collaborative teams of Autonomous Underwater Vehicle (AUV)s offer a promising alternative. However, underwater localization and multi-agent coordination remain challenging due to the lack of Global Navigation Satellite System (GNSS) signals underwater, the high cost of equipping all agents with advanced localization sensors, the limited acoustic communication bandwidth, and the susceptibility of acoustic communication to environmental interference. This work presents a hybrid decentralized framework for robust localization and formation control in teams of AUVs. Two leader agents equipped with GNSS or an advanced Inertial Navigation System (INS) guide a fleet consisting of an arbitrary number of followers in a circular arc formation using acoustic ranging for AUV localization. The framework is validated in a simulated environment and through field trials, demonstrating reliable acoustic localization and formation keeping. The results confirm the viability of the approach for scalable, high-precision underwater monitoring and event response.

Rao-Blackwellized particle filter (RBPF) SLAM solutions with Gaussian Process (GP) maps can both maintain multiple hypotheses of a vehicle pose estimate and perform implicit data association for loop closure detection in continuous terrain representations. Both qualities are of particular interest for SLAM with autonomous underwater vehicles (AUVs) in the open sea, where distinguishable features are scarce. However, the applicability of GP regression to parallel, real-time mapping in an RBPF framework remains limited by the size of the area to survey and the computational cost of the GP training. To overcome these constraints, in this letter we propose the adaption of Stochastic Variational GP (SVGP) regression to online mapping in combination with a novel, efficient particle trajectory storing in the RBPF. We show how the resulting RBPF-SVGP framework can achieve real-time performance in an embedded platform on two AUV surveys containing millions of points. We further test the framework on a live mission on an AUV and we make the implementation publicly available.

This paper presents the development and experimental evaluation of a self-steering mechanism for an autonomous sailing vessel. The steering mechanism is designed and tailored to relief the commonly used electro-mechanical steering during majority of the mission by the use of pure mechanical coupling between the self-trimming rig and the rudder. This significantly reduces the need for electric power for steering at constant apparent wind angle. Added to the steering, a two-layer navigation system is proposed for path-planning and navigation with algorithms tailored for low-power, low-memory microcontroller. We present experimental results from a total of 19 days of autonomous sailing in Stockholm's Archipelago. The experiments enabled us to compare the sailing performance with both active and self-steering systems for different apparent wind angles. On average, the active steering keeps a heading within ±5.1° of the target while the self-steering is able to maintain the heading within ±8.1° of the target. Another conclusion that can be drawn is that the apparent wind angle doesn't influence the steering performance. 

Rao-Blackwellized particle filter (RBPF) SLAM solutions with Gaussian Process (GP) maps can both maintain multiple hypotheses of a vehicle pose estimate and perform implicit data association for loop closure detection in continuous terrain representations. Both qualities are of particular interest for SLAM with autonomous underwater vehicles (AUVs) in the open sea, where distinguishable features are scarce. However, the applicability of GP regression to parallel, real-time mapping in an RBPF framework remains limited by the size of the survey and the computational cost of the GP training. To overcome these constraints, in this letter we propose the adaption of Stochastic Variational GP (SVGP) regression to online mapping in combination with a novel, efficient particle trajectory storing in the RBPF. We show how the resulting RBPF-SVGP framework can achieve real-time performance in an embedded platform on two AUV surveys containing millions of points. We further test the framework on a live mission on an AUV and we make the implementation publicly available.

Due to the challenges regarding the limits of their endurance and autonomous capabilities, underwater docking for autonomous underwater vehicles (AUVs) has become a topic of interest for many academic and commercial applications. Herein, we take on the problem of relative navigation for the generalized version of the docking operation, which we address as proximity operations. Proximity operations typically involve only two actors, a chaser and a target. We leverage the similarities to proximity operations (prox-ops) from spacecraft robotic missions to frame the diverse docking scenarios with a set of phases the chaser undergoes on the way to its target. We emphasize the versatility on the use of factor graphs as a generalized representation to model the underlying simultaneous trajectory estimation and relative navigation (STERN) problem that arises with any prox-ops scenario, regardless of the sensor suite or the agents' dynamic constraints. To emphasize the flexibility of factor graphs as the modeling foundation for arbitrary underwater prox-ops, we compile a list of state-of-the-art research in the field and represent the different scenario using the same factor graph representation. We detail the procedure required to model, design, and implement factor graph-based estimators by addressing a long-distance acoustic homing scenario of an AUV to a moving mothership using datasets from simulated and real-world deployments; an analysis of these results is provided to shed light on the flexibility and limitations of the dynamic assumptions of the moving target. A description of our front- and back-end is also presented together with a timing breakdown of all processes to show its potential deployment on a real-time system. 

The in-situ measurement of aerodynamic parameters on a boat evolving in natural conditions can be challenging. Nonetheless, the development of wind assistance and propulsion for cargo vessels calls for new methods to ensure the broadest possible evaluation of the aerodynamic interaction effects between the wind propulsion units and between the units and the hull. This paper proposes an experimental approach based on a scaled model of the Oceanbird concept car-carrier sailing at sea, in real conditions. A pressure measurement system was developed at KTH Royal Institute of Technology, which consists of 66 differential pressure sensors installed inside one of the four rigid wings. The capabilities and limitations of this sensor suite are explored in this article. With the implementation of specific experiments with the wings, we show that the system can detect stall and its hysteresis loop, as well as wing-wing and wing-hull interaction effects. The main limitation for the full aerodynamic characterisation of the boat comes from the lack of simultaneous measurement on all wings, which will be addressed in a second part of this paper. 

Reducing the impact of shipping on climate change is a must and new regulations try to enforce this. The technological development to allow for wind propulsion or wind assistance for ships are numerous and new methods need to be developed to ensure accurate performance prediction but also efficient and safe control of these ships. The interaction effects between the multiple wind propulsion units and the influence of the hull on the performance are still not well understood. The unsteady effects due to the fluctuating wind or the ship motion are seldom investigated and are also not fully understood. In this paper, we present experimental measurements of the aerodynamic performance of a 1:30 scaled model of a wind powered cargo vessel in real conditions, thus accounting for all interaction effects and unsteadiness of the wind. The results highlight the potential benefits of different trimming strategies, and also show the influence of unsteadiness on the aerodynamic coefficients and the overall performances.