Remote Mine Identification for AUVs

KONGSBERG MARITIME, ROYAL NORWEGIAN NAVY, HII, DSTG AUSTRALIA

Overview

Remote mine identification for AUVs

Project Partners: Kongsberg Maritime, Royal Norwegian Navy, Huntington Ingalls Industries, DSTG Australia
Industry: Defence
Vehicle Used: Kongsberg Hugin AUV & HII REMUS 600
Voyis Products Used: Recon LS

Summary: Voyis Recon LS AUV payloads were trialled on representative mine countermeasure missions to demonstrate the viability of using existing AUV assets for remote mine visual identification. Current methodology uses these platforms for only detection prior to deploying clearance divers for the identification stage. It was demonstrated that 3D laser and stills data collected remotely can be used to gain a complete visual understanding of mine-like objects (MLOs), improving mission efficiency and reducing risk by limiting diver deployments in the minefield.

The Project

The Recon AUV payload was trailed on two AUV platforms – with Kongsberg Maritime on their HUGIN AUV for the Royal Norwegian Navy, and with Defence Science Technology Group (DSTG) Australia on their REMUS 600 vehicle for the Autonomous Warrior demonstration. In both cases the RECON LS, laser and stills sensor payload, was integrated with the platforms utilizing the product’s standard software API for autonomous control, and employing existing vehicle navigational data to generate georeferenced data and improve target localization.

An MCM operation consists of 4 stages: Detection, Classification, Identification (ID), and Disposal/Neutralization. With current UUV platforms, mine detection, and sometimes classification, is completed using side-scan sonar, but a vessel must then enter the minefield to deploy a clearance diver or ROV to complete the visual identification stage. It is very time consuming to ID all MLOs in areas with complex seabed due to a high probability of false detection from sonar detection, resulting in many unnecessary diver deployments with personnel and vessels in a high-risk environment.

The modular Recon LS payload is ideally suited for remote mine identification, enabling autonomous reacquire missions to be executed with the same vehicle assets used for detection. Complete visual identification data, comprised of quantitative 3D laser and qualitative stills image data, can be efficiently collected for all MLOs in a single survey mission. Once the vehicle is recovered the data can be rapidly assessed and divers or ROVs can only be deployed to neutralize high probability targets.

MLO Detection – Sidescan – REMUS

MLO Detection -Kongsberg HISAS

The Process

W

Trials were conducted at the Kongsberg facility in Norway and Jervis Bay in Australia in areas with known MLO tagets

W

Vehicles were deployed for a detection survey with the vehicle's existing sidescan sonar

W

Targets were detected and localized by manually reviewing the data, but the process could also be completed with automated target recognition (ATR) software onboard the vessel or AUV

W

Once MLO's were found and their approximate location known, a reacquire mission was planned

W

Both surveys were completed at an altitude of 5m and a speed of 4 knots

W

Once vehicles are recovered, automated software is employed to efficiently download and extract target data from large datasets

Final Results

The visual target data collected allowed for the correct remote visual identification of both targets, discovering an inactive torpedo (HUGIN) and inactive cylindrical ground mine (R600). The standard REMUS video recorder was also utilized to demonstrate the improved image clarity of stills imaging when compared to standard video cameras.

The Kongsberg HUGIN visual data was mosaiced into a wide image to gain a complete visual understanding and laser data was used to directly measure target dimensions to obtain a qualitative understanding.

It was demonstrated that existing AUV assets can be utilized with new modular optical payloads to easily execute remote target visual identification, reducing deployments into the minefield and improving confidence with higher resolution data. The result is more efficient MCM operations with a single AUV asset completing the first three stages (Detection, Classification, and Identification), improving covertness and reducing risk with fully submerged autonomous missions.