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

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

Observations of Turbulence at a Near-Surface Temperature Front in the Arctic Ocean

High-resolution ocean temperature, salinity, current, and turbulence data were collected at an Arctic thermohaline front in the Nansen Basin. The front was close to the sea ice edge and separated the cold and fresh surface melt water from the warm and saline mixed layer. Measurements were made on 18 September 2018, in the upper 100 m, from a research vessel and an autonomous underwater vehicle. Destabilizing surface buoyancy fluxes from a combination of heat loss to the atmosphere and cross-front Ekman transport by down-front winds reduced the potential vorticity in the upper ocean. Turbulence structure in the mixed layer was generally consistent with turbulence production through convection by heat loss to atmosphere and mechanical forcing by moderate winds. Conditions at the front were favorable for forced symmetric instability, a mechanism drawing energy from the frontal geostrophic current. A clear signature of increased dissipation from symmetric instability could not be identified; however, this instability could potentially account for the increased dissipation rates at the front location down to 40 m depth that could not be explained by the atmospheric forcing. This turbulence was associated with turbulent heat fluxes of up to 10 W m−2, eroding the warm and cold intrusions observed between 30 and 60 m depth. A Seaglider sampled across a similar frontal structure 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.publishedVersio

Autonomous underwater vehicles in Arctic marine operations: Arctic marine research and ice monitoring

This thesis considers autonomous underwater vehicles (AUVs) in Arctic marine operations. It focuses on their use as a sensor platform in ice monitoring operations and the use of AUVs for Arctic marine research. Arctic AUV operations pose several challenges compared to standard AUV operations, including the presence of drifting sea-ice and navigational challenges in the polar regions. These are some of the issues addressed in this thesis. Chapter 2 introduces historic Arctic AUV operations, and it presents experiences and lessons learned through these campaigns. Challenges related to communication,navigation, fail-safes, and deployment and recovery, with a focus on Arctic operations are discussed. This chapter also motivates and assess the use of AUVs as a sensor platform for ice monitoring operations. A conceptual guidance and navigation system for Arctic AUVs is presented at the end of the chapter. Field work and experiments are important to test theory, but also to build experience and acquire knowledge. Chapter 3 presents two Arctic AUV deployments using the NTNU REMUS 100 AUV and the experiences learned from these operations. Two experiments demonstrating the use of unmanned surface vehicles (USVs) as a support tool for AUVs in the Trondheimsfjord are also presented, along with a motivation for the use of such platforms in Arctic marine research. Since Arctic AUV operations are considered as high risk, with significant costs associated, an Arctic AUV simulator environment has been developed, as presented in Chapter 4. The simulator consists of seven modules, where the modules defining the default guidance and control system, as well as the numerical AUV model, are similar to a regular AUV simulator. In addition, a nice drift model is provided to simulate drifting and rotating ice features. A multibeam echosounder (MBE) simulator is used to sense the ice topography,given as a digital elevation map (DEM). To achieve drift and rotation of the sensed terrain, the final module in the Arctic AUV simulator is a relative position and velocity module, which provides input to the MBE simulator. A special consideration has been given to iceberg mapping using AUVs in this thesis, asthe detailed topography of icebergs are important to develop iceberg trajectory models, as well as decision support in iceberg management operations (e.g.,iceberg towing). Chapter 5 details a guidance system for determining the main particulars of an iceberg that relies on MBE measurements to determine the location of the edge of the iceberg. The guidance system is implemented as astate machine, starting in an iceberg detection mode. Once an iceberg is detected, an edge-detection algorithm is used to determine the location of the edge relative to the AUV, and thereby to online generate a path along the iceberg edge. The line-of-sight (LOS) guidance scheme is used to follow the iceberg edge and circumnavigate the iceberg. Motivated by the need to estimate the relative AUV-iceberg position in order to generate a consistent iceberg topography corrected for iceberg drift and rotation, an iceberg mapping navigation system has been proposed in Chapter 6. A simultaneous localization and mapping (SLAM) algorithm based on the bathymetric distributed particle filter SLAM (BPSLAM) is used to track the position and orientation of the iceberg in the global frame. The iceberg mapping navigation filter, implemented using an Extended Kalman filter (EKF) with the SLAM states as input, provides estimates of relative pose and velocity between the iceberg and the AUV. In addition, the velocity of the iceberg is estimated in the iceberg mapping filter. The iceberg SLAM algorithm provides a real-time estimate of the iceberg topography at a fixed resolution, which along with the iceberg drift velocity estimates are important parameters in an iceberg management operation

Compact Subsea Separation Unit: - Nonlinear Model Predictive Control and Nonlinear Observers

In search of increased wealth and public prosperity, the oil industry have met new challenges in their quest for black gold. These challenges have driven Statoil's expertise to develop a compact subsea separation unit. The compact structure of this unit makes advanced process control a requirement. The study of this thesis will focus on conguring a NMPC with state process information supplied by a nonlinear observer. The study will be based on previous work on the Compact Separation unit. Statoil's internal MPC tool will be used for process control. The state and parameter estimation performance of the implemented observers has been assessed with regards to both measurement noise and model/plant mismatches. This has been performed through simulations on the implemented model, but tests have also been conducted on an off-line data set from a test rig of the compact separation unit. The observers provided sufficiently accurate state estimates, with the exception being when the estimates were based on too severe measurement noise. The parameter estimation scheme proved to be suboptimal, but provided vital information during tests on the off-line data set. The NMPC conguration developed during this project has been tested on several process disturbances, and have provided good results regarding regulation of the process within the desired control objectives. The conguration have proved to fulfill the performance criteria specied, both with the use of ideal process information and estimates supplied by the observers

A Multibeam-Based SLAM Algorithm for Iceberg Mapping Using AUVs

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

Using Autonomous Underwater Vehicles as Sensor Platforms for Ice-Monitoring

Due to the receding sea-ice extent in the Arctic, and the potentially large undiscovered petroleum resources present north of the Arctic circle, offshore activities in ice-infested waters are increasing. Due to the presence of drifting sea-ice and icebergs, ice management (IM) becomes an important part of the offshore operation, and an important part of an IM system is the ability to reliably monitor the ice conditions. An autonomous underwater vehicle (AUV) has a unique capability of high underwater spatial and temporal coverage, making it suitable for monitoring applications. Since the first Arctic AUV deployment in 1972, AUV technology has matured and has been used in complex under-ice operations. This paper motivates the use of AUVs as an ice-monitoring sensor platform. It discusses relevant sensor capabilities and challenges related to communication and navigation. This paper also presents experiences from a field campaign that took place in Ny-Aalesund at Svalbard in January 2014, where a REMUS 100 AUV was used for sea-floor mapping and collection of oceanographic parameters. Based on this, we discuss the experiences related to using AUVs for ice-monitoring. We conclude that AUVs are highly applicable for ice-monitoring, but further research is needed.(c) 2014 Norwegian Society of Automatic Control. Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. See: http://creativecommons.org/licenses/by/3.0/

A Multibeam-Based SLAM Algorithm for Iceberg Mapping Using AUVs

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.publishedVersionCopyright 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission

Observations of Turbulence at a Near-Surface Temperature Front in the Arctic Ocean

High-resolution ocean temperature, salinity, current, and turbulence data were collected at an Arctic thermohaline front in the Nansen Basin. The front was close to the sea ice edge and separated the cold and fresh surface melt water from the warm and saline mixed layer. Measurements were made on 18 September 2018, in the upper 100 m, from a research vessel and an autonomous underwater vehicle. Destabilizing surface buoyancy fluxes from a combination of heat loss to the atmosphere and cross-front Ekman transport by down-front winds reduced the potential vorticity in the upper ocean. Turbulence structure in the mixed layer was generally consistent with turbulence production through convection by heat loss to atmosphere and mechanical forcing by moderate winds. Conditions at the front were favorable for forced symmetric instability, a mechanism drawing energy from the frontal geostrophic current. A clear signature of increased dissipation from symmetric instability could not be identified; however, this instability could potentially account for the increased dissipation rates at the front location down to 40 m depth that could not be explained by the atmospheric forcing. This turbulence was associated with turbulent heat fluxes of up to 10 W m−2, eroding the warm and cold intrusions observed between 30 and 60 m depth. A Seaglider sampled across a similar frontal structure 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