Petter Norgren

Norwegian University of Science and Technology

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Martin Ludvigsen

Norwegian University of Science and Technology

Trygve Olav Fossum

Norwegian University of Science and Technology

Roger Skjetne

Norwegian University of Science and Technology

Zoé Koenig

Norwegian Polar Institute

Ilker Fer

Bjerknes Centre for Climate Research

Asgeir J. Sørensen

Norwegian University of Science and Technology

Eivind Kolås

Bjerknes Centre for Climate Research

Tore Mo-Bjørkelund

Norwegian University of Science and Technology

Finlo Cottier

Scottish Association For Marine Science

Vegard Evjen Hovstein

Maritime Robotics (Norway)

Cooperative Institutions

Norwegian University of Science and Technology

University of Bergen

University Centre in Svalbard

Bjerknes Centre for Climate Research

UiT The Arctic University of Norway

Universidade do Porto

Centre for Arctic Gas Hydrate, Environment and Climate

Akvaplan-niva

Norwegian Defence Research Establishment

NTNU Samfunnsforskning

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Adaptive Sampling of Surface Fronts in the Arctic Using an Autonomous Underwater Vehicle

IEEE Journal of Oceanic Engineering (2021)

Trygve Olav Fossum Petter Norgren Ilker Fer Frank Nilsen Zoé Koenig

Fronts between Arctic- and Atlantic-origin waters are characterized by strong lateral gradients in temperature and salinity. Ocean processes associated with fronts are complex with considerable space and time variability. Therefore, resolving the processes in frontal zones by observation is challenging but important for understanding the associated physical–biological interactions and their impact on the marine ecosystem. The use of autonomous robotic vehicles and in situ data-driven sampling can help improve and augment the traditional sampling practices, such as ships and profiling instruments. Here, we present the development and results of using an autonomous agent for detection and sampling of an Arctic front, integrated on board an autonomous underwater vehicle. The agent is based on a subsumption architecture implemented as behaviors in a finite-state machine. Once a front is detected, the front tracking behavior uses observations to continuously adapt the path of the vehicle to perform transects across the front interface. Following successful sea trials in the Trondheimsfjord, the front-tracking agent was deployed to perform a full-scale mission near 82

$^{\circ}$

N north of Svalbard, close to the sea ice edge. The agent was able to detect and track an Arctic frontal feature, performing a total of six crossings while collecting vertical profiles in the upper 90 m of the water column. Measurements yield a detailed volumetric description of the frontal feature with high resolution along the frontal zone, augmenting ship-based sampling that was run in parallel.

10.1109/joe.2021.3070912

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Citations (21)

Line-of-sight iceberg edge-following using an AUV equipped with multibeam sonar

IFAC-PapersOnLine (2015)

Petter Norgren Roger Skjetne

Obtaining 3D information about ice features, like icebergs, are of interest to researchers and offshore operators moving into the Arctic. Icebergs are affected by wind, and ocean currents, and can have unpredictable drift patterns, causing challenges when it comes to mapping objectives. Autonomous underwater vehicles (AUVs) equipped with multibeam echosounders are suitable for obtaining measurements of the underwater geometry of icebergs, but advances in autonomy are needed to map drifting icebergs reliably. This paper details a guidance algorithm for detecting and circumnavigating an iceberg - following the iceberg edge. The guidance scheme is implemented as a state machine, starting in an iceberg detection-mode. Once an iceberg is detected, the AUV will enter a mapping-mode. An edge detection algorithm will determine the position of the edge, and a line-of-sight approach will be used for edge-following. A six degree-of-freedom AUV simulator is used to perform a simulation study, to show how AUV dynamics affect the results. The simulation study presented shows the algorithm's effectiveness, both when the iceberg is assumed stationary, and when the iceberg is drifting and rotating with constant velocity.

Iceberg

10.1016/j.ifacol.2015.10.262

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Tracking and remote monitoring of an autonomous underwater vehicle using an unmanned surface vehicle in the Trondheim fjord

Petter Norgren Martin Ludvigsen Thomas Ingebretsen Vegard Evjen Hovstein

This paper presents a demonstration conducted in the Trondheim fjord, where an unmanned surface vehicle (USV) developed and operated by Maritime Robotics AS was used to relay acoustic information sent by a REMUS 100 autonomous underwater vehicle (AUV), to an onshore operation center. The AUVs mission was to conduct a survey of a Short Sunderland type aircraft, which was sunk just outside Munkholmen in 1945. The progress of the AUV and the USV were available in real-time throughout the demonstration, and was presented in Maritime Robotics' in-house Vehicle Control Station (VCS) software. The acoustic telemetry data was also relayed to the REMUS Vehicle Interface Program (VIP), used for monitoring the health of the AUV. Furthermore, the USV used the telemetry messages sent by the AUV, containing e.g. position and heading, to autonomously track the AUV during the survey. Throughout the mission, only 63% of the modem messages was received by the USV, and the longest time between telemetry updates was 6 minutes, but the USVs ability to track the AUV and relay data was successfully demonstrated.

Mission control center

10.23919/oceans.2015.7401975

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Citations (20)

Physical Process Cruise 2018

The Nansen Legacy Report Series (2020)

Ilker Fer Frank Nilsen Anthony Bosse Eva Falck Trygve Olav Fossum

The cruise KH 2018-709 aboard the Research Vessel Kronprins Haakon was the second process cruise of the project the Nansen Legacy. The cruise contributed to task T1-2, on process studies to investigate the atmospheric, oceanographic, radiative and other physical controls on sea ice and stratification, with a general aim to identify and quantify the processes that control the heat budget north of Svalbard and in the Barents Sea. The cruise aimed to deploy oceanographic moorings and gliders, an AUV, a remotely piloted unmanned aircraft, controlled meteorological balloons, collect underway measurements from ship-mounted ocean current profilers, wind profilers, radiometer and wave sensors, and collect ocean stratification, currents, and microstructure profiles along selected transects across the north Spitsbergen shelf and slope. Additionally, wave sensors were deployed at ice floes from ice edge into pack ice. This report provides an overview of the methods employed and the data collected.

Stratification (seeds)

Physical oceanography

10.7557/nlrs.5503

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Hexagonal Grid Graph as a Basis for Adaptive Sampling of Ocean Gradients using AUVs

Global Oceans 2020: Singapore – U.S. Gulf Coast (2020)

Tore Mo-Bjørkelund Trygve Olav Fossum Petter Norgren Martin Ludvigsen

In this paper we present an adaptive sampling strategy for Autonomous Underwater Vehicles (AUVs) seeking to explore and map ocean gradients using a hexagonal grid as its potential path. The laborious and deterministic method of pre-programming trajectories has gradually been replaced by intelligent and adaptive sensing strategies. These both allows more effective use of sampling resources, as well as improved spatiotemporal resolution. We present a method for adaptive and data-driven sampling in the water-column with AUVs that follows the sense-plan-act control paradigm. The method uses a hexagonal grid graph to discretize the survey area into a honeycomb-pattern, which gives both equilateral survey paths and a small branching factor. Using this graph, we present results from a typical environmental sampling application where the goal is to search for and explore temperature gradients. The main use case for this algorithm is to prioritize the strongest gradients within the AUV spatiotemporal envelope. We present results from field trials in Trondheimsfjorden, Norway, where the AUV successfully explored a river front. We find that the algorithm performs as expected, exploring the area and revisiting sections containing gradients.

Adaptive sampling

Regular grid

10.1109/ieeeconf38699.2020.9389324

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Intelligent Risk-Based Under-Ice Altitude Control for Autonomous Underwater Vehicles

Jens Einar Bremnes Petter Norgren Asgeir J. Sørensen Christoph Thieme Ingrid Bouwer Utne

Autonomous underwater vehicles (AUVs) are effective platforms for mapping and monitoring under the sea ice. However, under-ice operations impose demanding requirements to the system, as it must deal with uncertain and unstructured environments, harsh environmental conditions and reduced capabilities of the navigational sensors. This paper proposes a method for intelligent risk-based under-ice altitude control for AUVs. Firstly, an altitude guidance law for following a contour of an ice surface via pitch control using measurements from a Doppler velocity log (DVL) is proposed. Furthermore, a Bayesian network for probabilistic reasoning over the current state of risk during the operation is developed. This network is then extended to a decision network for autonomous risk-based selection and reselection of the setpoint for the altitude controller, balancing the trade-off between the reward of the setpoint and the risk involved. This will improve the system safety and reliability. Results from a simulation study are presented in order to demonstrate the performance of the proposed method.

Set point

10.23919/oceans40490.2019.8962532

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Observations of turbulence at a near-surface temperature front in the Arctic Ocean

Zoé Koenig Ilker Fer Eivind Kolås Trygve Olav Fossum Petter Norgren

Measurements made at an Arctic thermohaline front show turbulence production through convection and forced symmetric instability, a mechanism drawing energy from the frontal geostrophic current. Destabilizing surface buoyancy fluxes from a combination of heat loss to the atmosphere and cross-front Ekman transport by down-front winds reduce the potential vorticity in the upper ocean. The front, located in the Nansen Basin close to the sea ice edge, separates the cold and fresh surface melt water from the warm and saline mixed layer. High resolution temperature, salinity, current and turbulence data were collected in the upper 100 m, on 18 September 2018 across the front from a research vessel and an autonomous underwater vehicle. The AUV was deployed to autonomously collect high resolution data across the front using adaptive sampling. Both front detection and sampling location were decided by a state-based autonomous agent running onboard the AUV, optimizing data collection across and along the front.In addition to convection by heat loss to atmosphere and mechanical forcing by moderate wind in the mixed layer, forced symmetric instability contributed with comparable magnitude in generation of turbulence at the front location down to 40 m depth. This turbulence was associated with turbulent heat fluxes of up to 10 W.m-2, eroding the warm and cold intrusions observed at respectively 35 and 55 m depth. A similar frontal structure has been crossed by a Seaglider in the same region 10 days after our survey. The submesoscale-to-turbulence scale transitions and resulting mixing can be widespread and important in the Atlantic sector of the Arctic Ocean.

Cold front

10.5194/egusphere-egu2020-7026

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A Multibeam-Based SLAM Algorithm for Iceberg Mapping Using AUVs

IEEE Access (2018)

Petter Norgren Roger Skjetne

Using autonomous underwater vehicles (AUVs) for mapping underwater topography of sea-ice and icebergs, or detecting keels of ice ridges, is foreseen as enabling technology in future arctic marine operations. Wind, current, and Coriolis forces affect an iceberg's trajectory, making automated mapping difficult. This paper presents a method aiming at enabling autonomous iceberg mapping using AUVs equipped with a multibeam echosounder by estimating the position and orientation of the iceberg. The method is based on a bathymetric simultaneous localization and mapping (SLAM) algorithm, namely, the bathymetric distributed particle filter SLAM (BPSLAM) algorithm. The proposed method estimates the AUV's pose in an iceberg-fixed coordinate system. The relative states can be used for both guiding the vehicle to achieve complete coverage, as well as estimation of a consistent iceberg topography. The algorithm also provides an estimate of the iceberg's drift velocity – an important parameter for the AUV trajectory planning as well as any related ice management (IM) operations. Two new weighting algorithms for the BPSLAM method are proposed, enabling batch processing of multibeam echosounder (MBE) measurements to ensure real-time operation without discarding information. The proposed method is demonstrated using a real iceberg topography taken from the PERD iceberg sightings database, with simulated AUV and MBE range measurements. The algorithm is also evaluated on a real world bathymetric dataset, collected using the HUGIN HUS AUV.

Iceberg

Echo sounding

10.1109/access.2018.2830819

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Citations (40)

Is Ambient Light during the High Arctic Polar Night Sufficient to Act as a Visual Cue for Zooplankton?

PLoS ONE (2015)

Jonathan H. Cohen Jørgen Berge Mark A. Moline Asgeir J. Sørensen Kim S. Last

The light regime is an ecologically important factor in pelagic habitats, influencing a range of biological processes. However, the availability and importance of light to these processes in high Arctic zooplankton communities during periods of 'complete' darkness (polar night) are poorly studied. Here we characterized the ambient light regime throughout the diel cycle during the high Arctic polar night, and ask whether visual systems of Arctic zooplankton can detect the low levels of irradiance available at this time. To this end, light measurements with a purpose-built irradiance sensor and coupled all-sky digital photographs were used to characterize diel skylight irradiance patterns over 24 hours at 79°N in January 2014 and 2015. Subsequent skylight spectral irradiance and in-water optical property measurements were used to model the underwater light field as a function of depth, which was then weighted by the electrophysiologically determined visual spectral sensitivity of a dominant high Arctic zooplankter, Thysanoessa inermis. Irradiance in air ranged between 1–1.5 x 10-5 μmol photons m-2 s-1 (400–700 nm) in clear weather conditions at noon and with the moon below the horizon, hence values reflect only solar illumination. Radiative transfer modelling generated underwater light fields with peak transmission at blue-green wavelengths, with a 465 nm transmission maximum in shallow water shifting to 485 nm with depth. To the eye of a zooplankter, light from the surface to 75 m exhibits a maximum at 485 nm, with longer wavelengths (>600 nm) being of little visual significance. Our data are the first quantitative characterisation, including absolute intensities, spectral composition and photoperiod of biologically relevant solar ambient light in the high Arctic during the polar night, and indicate that some species of Arctic zooplankton are able to detect and utilize ambient light down to 20–30m depth during the Arctic polar night.

Diel vertical migration

Polar night

Sunlight

10.1371/journal.pone.0126247

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Citations (65)

Network of heterogeneous autonomous vehicles for marine research and management

Martin Ludvigsen Sigurd M. Albrektsen Krzysztof Cisek Tor Arne Johansen Petter Norgren

In April 2016, NTNU, FFI, Kongsberg Seatex, LSTS and Maritime Robotics set up an experiment to explore their capability to combine the research vessel Gunnerus, the AUV Hugin, the UAV X8 and the USV Mariner in a network of heterogeneous unmanned vehicles. Communication, manoeuvring, onboard processing and operational complexity are essential components in such networks. To provide communication the MBR broadband radio system was implemented. To show the capabilities of the system proposed, a scenario with seabed mapping and target recognition was defined. The experiment made it apparent that these networks has the potential of significantly saving cost for data collection in marine research and management by reducing ship time. To fully unlock the potential of networks of heterogeneous unmanned vehicles, the missions of each vehicle need to be more integrated.

10.1109/oceans.2016.7761494

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Citations (22)