Cross, Gary

Green, Peter

Parsons, John

Routledge, Peter

Tudor, Nick

Watson, Simon

Wood, Terry

English

The University of Manchester - Institutional Repository

The University of Manchester ResearchA2I2: Autonomous Aquatic Inspection and Intervention inNuclear Storage PondsDocument VersionAccepted author manuscriptLink to publication record in Manchester Research ExplorerCitation for published version (APA):Watson, S., Cross, G., Green, P., Parsons, J., Routledge, P., Tudor, N., & Wood, T. (Accepted/In press). A2I2:Autonomous Aquatic Inspection and Intervention in Nuclear Storage Ponds. In 2021 ANS Winter Meeting andTechnology ExpoPublished in:2021 ANS Winter Meeting and Technology ExpoCiting this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. If citing, it is advised that you check and use thepublisher's definitive version.General rightsCopyright and moral rights for the publications made accessible in the Research Explorer are retained by theauthors and/or other copyright owners and it is a condition of accessing publications that users recognise andabide by the legal requirements associated with these rights.Takedown policyIf you believe that this document breaches copyright please refer to the University of Manchester’s TakedownProcedures [http://man.ac.uk/04Y6Bo] or contact uml.scholarlycommunications@manchester.ac.uk providingrelevant details, so we can investigate your claim.Download date:09. Jun. 2022A2I2: Autonomous Aquatic Inspection and Intervention in Nuclear Storage PondsSimon Watson1, Gary Cross2, Peter Green1, John Parsons3,4, Peter Routledge5, Nick Tudor6, Terry Wood71 The University of Manchester, Manchester, UK, simon.watson@manchester.ac.uk; p.r.green@manchester.ac.uk2 Rovco Ltd., Bristol, UK, gary.cross@rovco.com3 Thales UK, Glasgow, UK4 Indigo Consulting, Henlow, UK, jfp@indigoconsulting.co.uk5 Forth Engineering (Cumbria), Cumbria, UK, peter@forth.uk.com6 D-RisQ Ltd., Malvern, UK, njt@drisq.com7 National Oceanographic Centre, Southampton, UK, terry.wood@noc.ac.ukINTRODUCTIONThe inspection of nuclear storage ponds is critical to thesafe operation of both legacy and modern nuclear facilities.Nuclear fuel ponds are traditionally used to store spent fuelrods. The water in the ponds acts as a radiological and thermalshield whilst the rods cool down before being sent for repro-cessing. There are over 1000 nuclear storage ponds globallywhich require monitoring [1]. Whilst many of these facili-ties are indoor and well maintained, there are a number oflegacy ponds, dating back to the 1950’s, which are outdoorand require decommissioning [2].Nuclear storage ponds can vary in size from small reactor-side ponds that can be 7 m x 7 m x 4 m, up to very large pondsthat can be up to 100 m x 13 m x 7 m [3]. Monitoring of theseponds is either done my sample extraction or by the use oftele-operated tethered remotely operated vehicles (ROVs).Tethered ROVs can be used for both inspections andmaintenance activities, however the tether poses a significantchallenge in terms of being snagged or tangled in complexstructured environments [4]. Tele-operation requires a humanoperator, which is a highly skilled job and creates a limitationon how long the vehicles can be operated for. By remov-ing the tether and increasing the level of autonomy, the fullcapabilities of ROVs could be realised.SYSTEM OVERVIEWThe A2I2 project was set up to develop a tetherless, au-tonomous underwater vehicle, which could be used for inspec-tions in both the nuclear and offshore industries. The consor-tium was comprised of Rovco Ltd., DRisQ Ltd., Forth Engi-neering, Thales, The National Oceanographic Centre (NOC)and the University of Manchester (UoM).Four main areas were developed within the project; plat-form design, perception and localisation, wireless communi-cation and verification of the autonomy. The rest of this paperwill detail the work done in each of these areas.Wireless CommunicationsIf an ROV is tetherless, this implies that there is eitherno communication between it and the operators, or there iswireless communications. No communication is undesirable,especially in safety critical environments, so underwater wire-less communications is required.Wireless transmission in water is highly challenging. Air-Activity Data RateAuxiliary Sensor Data 0.35 kbit/sCommand and Control 10 kbit/sReturning Point Cloud 25 kbit/sLow-res video 6,000 kbit/sHigh-res video 20,000 kbit/sTABLE I. Data Rate Requirementsbased robots use radio-frequency (RF) systems, however theydo not work in water due to the high absorption rate. Ocean-based systems often use acoustic transmission, however thedata rates are very low. Optical transmission using visiblelight is a relatively new technology to the market and is ableto transmit at much higher data rates, however it requiresgood water visibility to operate effectively and is subject tointerference from external light sources [5].In collaboration with end users, a set of data transmissionrequirements were generated, which are shown in Table I. Fig-ure 1 shows the data rates of commercially available systemscompared to these requirements.A key requirement from the end user was the transmissionof video data back to the operators. This allows both real-timemonitoring and potential wireless tele-operation. The onlytechnology that can achieve this is optical.Nuclear storage ponds are highly cluttered environments(ether structured or unstructured). This means that there isoften not a line of sight to the edge of the ponds. Wirelesscommunications requires transceivers on both the vehicle andon the shore. If there is no line of sight, there may be areaswithin the ponds which become communication dead-zones.To overcome this issue, the A2I2 project proposed theuse of a collaborative autonomous surface vehicle (ASV). Thesurface of storage ponds is kept free of obstacles, so the ASVcan navigate freely around it. By mounting the shore-sidetransceiver on the bottom of the ASV, line of sight can beguaranteed for a much larger section of the pond (as longas the ROV does not go under an obstacle). Data can betransfered from the ASV to shore by a secure wi-fi connection.For demonstration, a Hydromea Luma 500 optical com-munication system was used. It was mounted onto a BlueROVand a Mallard ASV [6]. Figure 2 shows wireless tele-operationof an ROV. A full video of the demonstration can be found athttps://youtu.be/IvKAKxh7aj4. Real-time video transmissionwas also achieved over a distance of 2 m.Fig. 1. Comparison of COTS Wireless Communication SystemsFig. 2. Tetherless Tele-operation of an ROVPlatform DevelopmentFor the demonstration of the A2I2 system, a BlueROV2was used as the subsurface vehicle, whilst as custom ASV wasdeveloped by Forth Engineering, known as the Lilypad.The BlueROV was modified to include 9 HD cameras andtwo sonars to enable 360-degree collision avoidance. It alsohoused the vision perception system used for localisation andnavigation. The Lilypad ASV had integrated thrusters andbuoyancy to allow movement around the pond. Solar panelsremoved the need for a tether for power. The ROV could dockto the ASV for deployment and retrieval and a secure wi-ficonnection allowed data to be transferred back to shore asshown in Figure 3.Perception, Localisation and NavigationThe development of autonomous systems has surged inland, air and offshore surface domains, whilst underwater au-tonomous capabilities have remained somewhat stagnant; hin-dered in part by the limited choice and availability of sensorsproviding low fidelity positioning and range measurements.Fig. 3. Left: Deployment of Lilypad and ROV. Right: Opera-tion of Lilypad and ROVConsequently, navigation and control of an ROV or AUV foroperation in unstructured and dynamic environments, remainsa largely unsolved challenge for commercial application, par-ticularly where navigation close to structures is required.Most autonomous systems are restricted to waypoint nav-igation, using predefined paths or prior models, performingtasks such a cable and pipe following or long-range geologicalsurveys. Almost no AUV performs real-time decision making,path planning with collision avoidance. A novel method hastherefore been required to enable precise sensing and position-ing, to support the accurate control required for autonomousdetailed visual inspections.The A2I2 project made use of the Rovco SubSLAM sys-tem for perception, localisation and navigation. Using a stereo-camera system with a working distance of 1.5 m, 3D recon-structions are generated which are used as the inputs to thepath planning algorithms. The system is able to localise therobot relative to the 3D reconstruction, then plan paths to ex-plore areas of interest. Figure 4 shows the system being testedand generating 3D reconstructions.Fig. 4. Left: ROV with stereo camera mounted. Middle:Deployment in test pond. Right: 3D reconstruction of the testpondVerification of AutonomyAutonomous vehicles are not in wide use because of twolinked problems: Lack of trust and the cost to gain adequatetrust. From a safety and regulatory perspective, for any systemthere are always three questions to be answered:• What is it supposed to do?• Can you show me that it doesn’t do what it is not sup-posed to do?• What will happen when, not if, something goes wrong?Most work concentrates on showing what a system willdo, but the safety of the system is much more focused on theother two questions. For an autonomous system, the problem ismuch harder because autonomy is largely thought/expected tobe vested in some form of Artificial Intelligence (AI). However,AI cannot be fully verified, so cannot be treated in the sameway as existing approaches to software. Testing increasesconfidence, but how much test is sufficient and at what cost togain sufficient confidence? Safety is hardly ever about the easyto demonstrate aspects of the system, the 99%, it is mostlyabout gaining confidence in the remaining 0.99999% thathistorically have been required for public trust. Unfortunately,AI is [relatively] easy to fool which lowers confidence sowe need mechanisms to ensure that unwanted behaviour canbe constrained. AI has tremendous advantages but will berestricted to niche applications unless safety is adequatelyaddressed.The aim for A2I2 was to extract the benefits of AI (in thiscase Rovco Vaarst sub-SLAM) on a tetherless vehicle whileensuring that the system is safe. The D-RisQ contributionwas to provide a capability that allows the sub-SLAM systemto gather the required visual coverage of the artefact but toprovide a safety monitor that can take over if safety is aboutto be compromised. This capability was transparently andcost effectively developed to provide the assurance of vehiclebehaviour.The software was developed in close collaboration withthe end user, Sellafield Ltd. and the Office for Nuclear Reg-ulation (ONR). For the demonstrations, two hazards wereidentified to be avoided:• Splash: Spray from the vehicle motors must be avoided.Fig. 5. Left: ROV with collision avoidance hardware. Right:collision avoidance in operation• Collision: The vehicle must not hit any other object inthe pond or the sides of the pond.Figure 5 shows the software in operation, acting to stopthe ROV from colliding with a wall.CONCLUSIONSThe A2I2 project has demonstrated the potential of usingcollaborative, autonomous surface and sub-surface robots forthe inspection of nuclear storage ponds. Proof-of-conceptvehicles have been demonstrated which are being developedfurther in collaboration with end users.A number of challenges still remain to be investigated,including robust wireless communications in challenging wa-ter conditions (turbidity, variable ambient lighting), variableautonomy mission planning and shared autonomy control, andthe acceptance of safe autonomous systems into safety criticalenvironments.ACKNOWLEDGMENTSThis material is based upon work supported by InnovateUK (IUK), A2I2, Project Number 104822 and Engineeringand Physical Science Research Council (EPSRC), RAIN Hub,Project Number EP/R026084/1.REFERENCES1. H. F. BEER, J. BORQUE LINAN, G. A. BROWN,D. CARLISLE, R. DEMNER, B. IRONS, M. LARAIA,V. MICHAL, A. PEKAR, O. ŠOTIĆ, L. VALENCIA, andS. WLATERS, “IAEA Nuclear Energy Series No. NW-T-2.6: Decommissioning of Pools in Nuclear Facilities,”Tech. rep., IAEA (2015).2. S. F. JACKSON, S. D. MONK, and Z. RIAZ, “An inves-tigation towards real time dose rate monitoring, and fuelrod detection in a First Generation Magnox Storage Pond(FGMSP),” Applied Radiation and Isotopes, 94, 254 – 259(2014).3. “Challenge: ROV Positioning in Featureless Pond Environ-ments,” Tech. rep., Game Changers (2021).4. O. TORTORICI, C. ANTHIERENS, V. HUGEL, andH. BARTHELEMY, “Towards active self-management ofumbilical linking ROV and USV for safer submarine mis-sions,” IFAC-PapersOnLine, 52, 21, 265–270 (2019), 12thIFAC Conference on Control Applications in Marine Sys-tems, Robotics, and Vehicles CAMS 2019.5. M. F. ALI, D. N. K. JAYAKODY, Y. A. CHURSIN,S. AFFES, and S. DMITRY, “Recent Advances and Fu-ture Directions on Underwater Wireless Communications,”Archives of Computational Methods in Engineering, 27,1379–1412 (2020).6. K. GROVES, A. WEST, K. GORNICKI, S. WATSON,J. CARRASCO, and B. LENNOX, “MallARD: An Au-tonomous Aquatic Surface Vehicle for Inspection and Mon-itoring of Wet Nuclear Storage Facilities,” Robotics, 8, 2(2019).

A2I2: Autonomous Aquatic Inspection and Intervention in Nuclear Storage Ponds

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A2I2: Autonomous Aquatic Inspection and Intervention in Nuclear Storage Ponds

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