ACTAUNIVERSITATIS

UPSALIENSISUPPSALA

2018

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Science and Technology 1695

Automation of underwateroperations on wave energyconverters using remotelyoperated vehicles

FLORE REMOUIT

ISSN 1651-6214ISBN 978-91-513-0388-8urn:nbn:se:uu:diva-356565

Dissertation presented at Uppsala University to be publicly examined in Häggsalen,Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, Friday, 21 September 2018 at 13:15 forthe degree of Doctor of Philosophy. The examination will be conducted in English. Facultyexaminer: Associate Professor Antonio Pascoal (Instituto Superior Tecnico, Lisbon, Portugal).

AbstractRemouit, F. 2018. Automation of underwater operations on wave energy converters usingremotely operated vehicles. Digital Comprehensive Summaries of Uppsala Dissertations fromthe Faculty of Science and Technology 1695. 74 pp. Uppsala: Acta Universitatis Upsaliensis.ISBN 978-91-513-0388-8.

In the last fifteen years, the Division of Electricity at Uppsala University has been developinga wave energy converter (WEC) concept. The concept is based on a point-absorbing buoy witha directly driven linear generator placed on the seabed. Several units are connected to a marinesubstation, whose role is to collect and smooth the power absorbed from the waves and thenbring it to the shore through one single cable.

A big challenge in the project is to reduce the costs related to the deployment and maintenanceof the WECs and substation. Currently, those operations are performed by divers, which is costlyand entail considerable risks. A possibility is to replace divers with automated solutions usingsmall robots called remotely operated vehicles (ROVs). This PhD thesis proposes and analyses amethod for deployment and maintenance of underwater devices with no use of diving operations.

Existing ROVs need additional modules and equipment in order to carry out operationswith the required force and precision. Typical missions are inspection, shackles or slingsremoval, valve closing, and cable connection. The latter demands especially high precision inthe positioning: 5 mm in distance and 5◦ in heading angle. In addition, this operation involvesforces up to 200 N. This combination power-precision is not reached by existing ROVs. ThisPhD thesis presents a positioning system for underwater robot to enable autonomous positioningof the vehicle before cable connection.

The positioning system is composed of two green lasers and a monocular camera, and isbased on image processing. Experimental results from laboratory testing show that the meanabsolute error in distance measurement is as low as 6 mm at 0.7 m from the target, whereas theheading is minimized to 2◦. The computational time for the image processing is 13.6 ms perimage, meaning the possibility of a 30 Hz measurement system. Used together with a closed-loop path-following unit, this positioning system can support autonomous docking. This PhDthesis presents the model of an autopilot and results from docking simulations, showing theperformance of the positioning system used in closed-loop.

Keywords: Remotely Operated Vehicles, wave energy, WEC deployment, cable connection,optical positioning system, autonomous underwater docking

Flore Remouit, Department of Engineering Sciences, Electricity, Box 534, UppsalaUniversity, SE-75121 Uppsala, Sweden.

© Flore Remouit 2018

ISSN 1651-6214ISBN 978-91-513-0388-8urn:nbn:se:uu:diva-356565 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-356565)

to my grand-father and fatherJean and Jean-Louis Remouit

List of papers

This thesis is based on the following papers, which are referred to in the textby their Roman numerals.

I Parwal, A., Remouit, F., Hong, Y., Francisco, F., Castellucci, V., Hai,L., Li, W., Lejerskog, E., Baudoin, A., Nasir, M., Chatzigiannakou, M.,Haikonen, K., Ekstrom, R., Bostrom, C., Goteman, M., Waters, R.,Svensson, O., Sundberg, J., Rahm, M., Engstrom, J., Savin, A., Leijon,M. ”Wave Energy Research at Uppsala University and The LysekilResearch Site, Sweden: A Status Update”. Proceedings of the 11thEuropean Wave and Tidal Energy Conference, Nantes, France,September 2015.

II Widen, J., Carpman, N., Castellucci, V., Lingfors, D., Olauson, J.,Remouit, F., Bergkvist, M., Grabbe, M., Waters, R. ”Variabilityassessment and forecasting of renewables: A review for solar, wind,wave and tidal resources”. Renewable and Sustainable Energy Review,44, 356–375, 2015.

III Remouit, F., Lopes, M., Pires, P., Sebastiao, L., Rahm, M.”Automation of subsea connection for clusters of wave energyconverters”. Proceedings of the 25th International Ocean and PolarEngineering Conference, Kona, Hawaii, USA, June 2015.

IV Remouit, F., Ruiz-Minguela, P., Engstrom, J. ”Review of Electricalconnectors for underwater applications”. IEEE Journal of OceanicEngineering, 8048605, doi: 10.1109/JOE.2017.2745598, 2017.

V Remouit, F., Chatzigiannakou, M.-A., Bender, A., Sundberg, J.,Temiz, I., Engstrom, J. ”Deployment and maintenance of Wave EnergyConverters at the Lysekil research site: a comparative study on the useof divers and Remotely Operated Vehicles”. MDPI Journal of MarineScience and Engineering, 6(2), 39, 2018.

VI Remouit, F., Abrahamsson, J., Engstrom, J. ”Optical System forUnderwater Positioning of Observation Class Remotely OperatedVehicle”. Proceedings of the 3rd Asian Wave and Tidal EnergyConference, Singapore, Singapore, October 2016.

VII Remouit, F., Galeazzi, R., Engstrom, J., ”Laser-based relativepositioning system for underwater remotely operated vehicle”.Submitted to IEEE Journal of oceanic engineering, 2018.

VIII Remouit, F., Engstrom, J., ”Autonomous docking based on opticalpositioning system for remotely operated vehicle”. Manuscript, 2018.

Reprints were made with permission from the publishers.

Contents

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.1 Renewable energy at Uppsala University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.1.1 Renewable intermittent energy and grid integration . . . . . 111.1.2 Wave energy at Uppsala University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.1.3 Today’s challenges related to wave energy at Uppsala

University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.2 Underwater robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.2.1 Remotely operated vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.2.2 Challenges related to ROVs in the context of this

thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.4 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2 Variability and forecasting of intermittent renewable energy sources . . . 212.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.1.1 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.1.2 Aim of the chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.2.1 Forecasting accuracy metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.2.2 Forecasting models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3 Automation of underwater operations on wave energy converters . . . . . . . . 273.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.1.1 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.1.2 Aim of the chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.2.1 Development of a ROV-assisted deployment method . . . 303.2.2 Comparative study on time and cost of operations . . . . . . . 30

3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.3.1 Comparative study on time and costs of deployment

operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.3.2 Analysis of the ROV-assisted deployment method . . . . . . . 34

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.4.1 Towards fully ROV-conducted WEC operations . . . . . . . . . . . 35

3.4.2 Development of assisting tools for ROV operations . . . . . 363.4.3 Adapting processes from the offshore wind energy

sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4 Laser-based relative positioning system for remotely operate vehicle . . 384.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.1.1 Docking system for ROV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384.1.2 State of the art in underwater positioning . . . . . . . . . . . . . . . . . . . . . . 394.1.3 Aim of the chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2 Background and Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.2.1 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.2.2 Hardware and test facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.2.3 BlueROV model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414.2.4 Control theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.3.1 Positioning system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.3.2 Closed-loop model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.4.1 Open-loop positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.4.2 Closed-loop simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

7 Svensk sammanfattning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

8 Resume en francais . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

9 Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

10 Summary of papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Abbreviations

Abbreviation Description

ANN Artificial Neural NetworkAR Auto-RegressiveAUV Autonomous Underwater VehicleBGR Blue-Green-Red format of an imageCG Center of gravityDoF Degree of FreedomESC Electronic Speed ControllerGA Genetic AlgorithmGPS Global Positioning SystemHSV Hue-Saturation-Value format of an imageIMU Inertial Measurement UnitLRS Lysekil Research SiteMAE Mean Absolute ErrorMAPE Mean Absolute Percentage ErrorMS Marine SubstationNWP Numerical Weather PredictionNDFD National Digital Forecast databaseRMSE Root Mean Square ErrorROS Robot Operating SystemROV Remotely Operated VehicleSI Satellite ImagesUU Uppsala UniversityWEC Wave Energy ConverterWRF Weather Research and Forecast

1. Introduction

In his novel Twenty Thousand Leagues Under The Sea, Jules Verne introducesthe famous Nautilus, the world’s first fully electrical submarine, occasioninggreat enthusiasm within the scientific community [1]. One hundred and fiftyyears later, underwater robotics has come a long way and proved to be usefulin many sectors, such as farming, military, exploration, or energy. Under-water robots are currently mostly used for deep-sea operations where a diverintervention is impossible. Although divers can operate in shallow waters, theuse of autonomous vehicles is gaining interest thanks to lower human risk,increased operational efficiency, and operational cost reduction. However, tomake robots a viable alternative to divers, one of the main challenges is togive them the capacity to interact with and act in the environment with thesame ease as humans.

The thesis presented here tells the journey towards the automation of waveenergy converters operations by underwater robots.

1.1 Renewable energy at Uppsala University1.1.1 Renewable intermittent energy and grid integrationMany energy sources are renewable and harness natural processes. Commonones are hydropower, wind power, and solar energy [2], while some others,as tidal and wave energy, are still considered at an early stage of technical de-velopment. Among renewable energies, a shared attribute among wind, solar,tidal, and wave energy, is their intermittence and varying power productionaccording to time and location. This particularity makes their integration intothe grid a complex issue [3]. Merging them without impacting neither the gridnor the electricity consumer requires to study their variability. For wave power- which is still at an experimental level compared to wind and solar energy - itresults in a need to ensure its power predictability, as well as to catch up withthe other mainstream intermittent energies in terms of temporal and spatialpower forecast.

Wave energy is an intermittent renewable energy source with a high poten-tial for several reasons: First, over 70% of the Earth’s surface is covered withwater, and it is estimated that 1 TW [4] contained in wave energy is contin-uously available in the world. Second, many cities - where there usually is alarge need for electricity - are located along the coasts, allowing a direct access

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to wave energy, among other offshore renewable sources. Wave energy is alsoan interesting potential power supply for islands that are isolated from distri-bution grids. Last, wave energy has lower variability than other intermittentrenewable energy sources [5] such as solar or wind power, facilitating a futureintegration into the grid. This aspect makes wave power a strong potentialcompetitor to other intermittent renewable energy sources on the market.

1.1.2 Wave energy at Uppsala UniversityWave energy converter model

Wave Energy Converters (WECs) vary in types and sizes. They are typicallyclassified according to their method of energy absorption [6], [7]: oscillat-ing water columns [8], overtopping devices [9], and wave-activated bodies[10]. The latter section contains devices in which one or more componentsare activated by the wave motion via an oscillatory movement relative to a fixreference or body. The Uppsala University (UU) WEC model - presented inFigure 1.1 - belongs to this category. It is a point-absorbing offshore device,which consists in a buoy at the water surface connected to a directly drivenlinear generator placed in a hull on the sea bottom. The model is designed tooperate in depths from 20 to 100 m [11].

Figure 1.1. The linear Wave Energy Converter developed at Uppsala University

Marine substation

The WEC is small in size and provides limited power. Therefore a cluster ofdevices is to be interconnected to marine substations (MSs) in order to mergethe electricity produced by each WEC and increase the total power output. Thelatest MS developed at UU (Figure 1.2) can host up to eight WECs. Its role isboth to control each WEC damping, smooth their voltage signal, combine the

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incoming electricity from the different WECs, and bring the final output to aproper AC voltage. One single cable, so called power cable, then goes fromthe MS to shore, where it is to be grid connected [12].

Figure 1.2. Model of the marine substation developed at Uppsala University

The Lysekil Research Site

The Lysekil Research Site (LRS) is a test site located at the Swedish westcoast, where full scale testing of UU WECs started in 2006, within the Lysekilproject. The LRS includes a grid connection with a measurement cabin, ameasurement buoy, and an observation tower [13]. Fourteen WECs and twoMSs have been deployed there, in depths of approximately 25 m and in an areaof around 0.5 km2.

1.1.3 Today’s challenges related to wave energy at UppsalaUniversity

Some research topics were identified as the most challenging questions con-cerning wave energy at UU (Paper I):1. Variability [3], [14]

As explained above, wave energy is an intermittent energy source and itsspatial and temporal variability should be studied for an optimized grid inte-gration, especially when combined with other intermittent energy sources.The question of forecastability is also central, as it allows potential adjust-ments in the grid, both for short and long term forecasts.

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2. Power absorption [15], [16], [17]Power absorption can be enhanced with the use of different wave absorbershapes at the water surface, but also by actively controlling the WEC. Thisresearch topic is characterized by hydrodynamical and mechanical studies,while the point below is more focused on power electronics.

3. Power production [18], [19], [20]Questions regarding the power system are various. One of them concernsthe generator and its capacity to produce maximum power with minimumlosses from the absorbed wave. Other issues are related to the active controlof the WEC. The substation plays an important role in the power productionand issues linked to power smoothing are of great interest. Finally, gridconnection is another matter, in relation to the first aforementioned topic inthis list.

4. Survivability [21], [22]Survivability is an essential topic in wave energy, and is naturally coveredby the research team at UU. Survivability concerns both the WEC mechan-ics and electronics. It can correspond to peaks related to strong waves inharsh weather, where high loads impact the chains, shackles, lines on theWEC, but also the generator and power electronics. It is also related to sur-vivability in time, where both the mechanical and electronic parts shouldwithstand a life-time of 20-25 years.

5. Farm optimization [23], [24]The WEC developed by UU is of small scale and clusters of devices areneeded to produce sufficient power. A farm could incorporate up to hundredconverters. The farm layout highly impacts the power production, but alsothe site accessibility for deployment and maintenance, electrical cablingscheme. Therefore optimization studies are at the heart of the researchconducted within the group.

6. Environment [25], [26]Environmental research is necessary to analyse the impact of wave energyparks and underwater infrastructures on marine fauna and flora. The studiesfocus mainly on noise impact and animal behaviour in the proximity of theWECs.

7. WEC production, deployment and maintenance [27], [28]To make wave power a profitable energy source, the costs of WEC con-struction should be reduced and automating the production is a natural steptowards economic sustainability. Moreover, at the current stage, high oper-ation costs are preventing many wave energy projects from reaching a com-mercial level. Improving the deployment and maintenance of WECs couldlead to a reduction of the expenses related to deployment, maintenance, anddecommissioning of wave energy parks.

Most of the questions in the above list apply to any wave energy devicedevelopment. The last point though, covers also other offshore energy sectors.

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As said above, the desire to decrease commercial diving activities has nowreached the entire offshore engineering community. One reason is obviouslysafety, another is the aspiration to go deeper in the ocean [29], and a thirdnon-negligible aspect is economic. Replacing industrial diving by robotizedsolutions is a central component of cost reduction in underwater operations.

1.2 Underwater roboticsAn underwater robot is often equipped with thrusters, either because it shouldmove to the exact position of operation, or because the action itself requiresdisplacements. For that reason most of the underwater robots are called un-manned underwater vehicles. There are two categories of unmanned underwa-ter vehicles. The first one is the group of Autonomous Underwater Vehicles(AUVs), and the second contains the Remotely Operated Vehicles (ROVs).The difference between those two categories is the presence of a tether allow-ing direct communication and, in most cases, power supply, between the ROVand the surface vessel. Without that tether, AUVs are limited in power capac-ity, but highly optimized in terms of hydrodynamical properties, which makesthem mobile and fast. Their complete independence allows them to operate atlong distance from the launching point. This type of vehicle is largely usedfor research studies, mapping, and scanning operations, where little power isrequested [30]. Due to their tether causing drag and restricting the range ofaction, ROVs are less mobile than AUVs. However they have in theory accessto unlimited power, and can therefore perform a large array of tasks. They arethus the targeted vehicles for operations on WECs.

1.2.1 Remotely operated vehiclesRemotely operated vehicles classification

ROVs vary in size, power capacity, tooling, and are usually classified into threecategories: Observation-, medium-, and working-class [31]. Observation-class ROVs have small dimension with no tooling equipment, and they usuallycarry out inspection tasks, such as valve position or gauge reading, and moni-toring tasks including checks for leakage, surface conditions, acoustic anoma-lies [29]. They can also be used for seabed studies instead of AUVs. SuchROVs typically have onboard power and a thin tether for data communicationonly, which results in low drag and high manoeuvrability.

Working-class ROVs are large vehicles intended to perform specific tasks.Those ROVs are usually stationary and not hydro-dynamically optimized, es-pecially due to their tether that is necessarily large for sufficient power trans-mission, and thus responsible for high drag. The tasks to carry out are typicallywelding, cutting, drilling, digging, lifting, dragging. Some of the robots aremultifunctional and equipped with several sensor and tooling packages; some

15

others aim to conduct a single and repetitive action that might require largeforces or high precision. In 2016, more than 80% of the ROVs in operationworldwide belonged to the working-class category [32].

Medium-class ROVs consist in mono-task and simply tooled vehicles. Thetool could be for example a simple cutter, claw, or hook, in order to performsampling, cutting, or grasping actions, or to carry light weight and small items.Such ROVs are intended to carry out maintenance activities, such as cleaning,sampling, or refilling. They enable light onboard equipment (batteries, com-puters, thrusters, and tools), and are consequently smaller in size, leading toa better manoeuvrability and a larger range of actions than the working-classvehicles.

Suitable vehicle for the Lysekil project

Around 650 ROVs are in operation around the world [32], mostly for Oil andGas and subsea mining applications. Research groups and smaller industries(fish farming, offshore wind) tend to purchase or hire ROVs, on economicgrounds, of the observation- and medium-class. Working-class vehicles canindeed cost as much as ten to twenty times medium-class robots. A high num-ber of qualified personnel is also needed to steer it, operate the manipulatorsand equipment, manage the tether, and maintain the vehicle. The ROV crewcan be reduced to approximately three persons for an elementary ROV, whichgreatly decreases the expenses. Moreover, this crew does not need to be asexperienced as for a working-class ROV because smaller vehicles are easier tomanipulate and steer. Finally, the ship hosting a large ROV should have accessto high power onboard and a large deck for launching, equipped with a crane.These costs can be saved when purchasing or renting a medium-class vehicle.For those reasons, the Lysekil project has an interest in looking at small ROVsfor its application.

A list of ROV candidates for the Lysekil project is presented in Table 1.1.From that table, the two last ROVs are considered the most suitable for the Ly-sekil project applications because of their low weight in air making them easyto handle, and of their price considering their capabilities and manoeuvrability.They will hence serve as standard model ROVs further in the thesis.

1.2.2 Challenges related to ROVs in the context of this thesisUsing small ROVs for heavy operations such as WEC deployment or mainte-nance involves large technical challenges, and today, many of them are beinginvestigated within the research community. This subsection evaluates andsummarizes some of the issues to address in order to use the ROVs proposedin Table 1.1 for operations on WECs.

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Small and low-cost ROV platforms

The challenges related to small and low-cost ROV platforms are numerous andconcern both the mechanics, hydrodynamics, and electronics of the vehicle.Some issues are considered more central in the context of this thesis:• Computing capacity [33]

When using a small vehicle, the available space for the computer is reduced.Using a small computer affects the ROV capacity, for example data com-munication, processing speed, and memory capacity. Heat dissipation canalso be impacted by a small computer container. In an observation-classROV, cooling of electronics is usually simply done via convection betweenthe surrounding water and the electronics compartment. Some fans can beadded to spread the heat away, but this matter remains an essential issue.

• Positioning [34]Underwater positioning is an endlessly studied topic. Many sensor packageshave been developed but they are both costly, usually large in size, and powerdemanding. Using a small-size vehicle requires efficient sensors of smalldimension.

• Thrust restriction [35]Using small ROVs imply a limited available thrust on board the vehicle. Alow thrust causes instability of the vehicle in strong currents and wavy en-vironments, resulting in impaired navigation. Consequently, a good controlsystem is needed to balance those limitations. Additionally, weak thrusterslead to a low available payload for the tasks to be carried out.

Semi-autonomous ROVs

According to [36], ROV control can be obtained in four different ways:1. Remote control: The operator is watching the ROV and controls its move-

ments through line of sight by overlooking the sea lanes.2. Teleoperation: The operator steers the vehicle from a remote location and

sees the ROV trajectory directly underwater through its camera.3. Logic driven: The operator enters a series of waypoints and the robot auto-

matically operates towards the indicated destination.4. Logic driven with goal orientation: The operator uploads goal instructions

and the vehicle carries out the task autonomously.

While most currently exisiting ROVs usually operate under modes 1 and 2,there is an increasing interest in implementing modes 3 and 4 into future vehi-cles [37], [38]. This semi-autonomy represents a major challenge that can bedivided in sub-topics, on which research is actively conducted.• Localisation [39], [40]

Localisation is the task of obtaining an accurate estimation of the vehicle’sposition in its environment. Research is done to enhance positioning sen-sors, but also to improve the estimation itself with the help of estimators (forexample the Kalman Filter).

17

• Path planning [41]ROVs need path planning to get back on track in case of deviation, andalso for navigation when inspecting a structure. In real conditions, ROVguidance is highly affected by the sea state (waves, winds and currents),water properties (salinity, temperature, density), and weather forecast.

• Decision making [42]Due to increasing autonomy, ROVs should, at some point, face situationswhere a decision needs to be made. It could be about further investigatinga particular spot during an inspection or deciding upon the time needed toperform a task. Decision-making algorithms are typically based on machinelearning techniques and especially concern collaborative ROV operations.

• Risk management [43]As the control of the vehicle becomes more goal oriented, the operator inter-ventions decrease. On one hand it lowers the risk induced by human actions.On the other hand it increases the requirements of the robot to autonomouslydetect risk. As the operator’s role becomes more supervisory, its actions alsorequest more abilities and responsibilities. The questions of the operator’srole and the degree of autonomy in ROVs are thus central.

1.3 Research questionsFor the last fifty years many wave energy technologies have been designedand developed but yet, none of them have accessed the energy market. Someobstacles come from the technology itself, but other aspects should be consid-ered. One of them is the difficulty to catch up with the other renewable energytechnologies on the market. Wave energy can be used advantageously togetherwith other intermittent energy sources for a better aggregate grid integration.The low variability of waves is an attribute of great interest for balancing andsmoothing the power output of wind and solar energy technologies.

Another element is the high operational costs related to deployment, main-tenance, and decommissioning of WECs. A key to decrease those expensesis the replacement of divers by ROVs, where smart solutions can enable theautomation of actions performed underwater. However, using robotized solu-tions will not reduce operational costs unless the utilized vehicle is affordableand equipped with low-cost sensors. A positioning system based on visionand lasers can decrease the overall costs of the vehicle without restricting thecontrol system accuracy nor the ease of steering.

Based on those challenges, this doctoral thesis aims at answering three re-search questions, all related to the future marketing of wave energy:1. How to better forecast wave energy and its variability together with other

intermittent renewable energies in order to enable a fully renewable powergrid?

2. How to lower the expenses related to underwater operations on WECs?

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3. What tools can enable the use of low-cost ROVs to perform underwatercable connections?

The first question is covered in Paper II. Papers III, IV and V present theoutcome of the second question. Finally the last research point is studiedin papers VI, VII, and VIII. Paper I is a more general paper on the researchconducted within the wave energy group of UU.

1.4 Thesis outlineThis thesis is divided in three topics according to the three research questions itis based on. Each topic contains the affiliated methods, results, and discussion,in separate subsections. The first topic concerns the variability and forecast ofwave power together with wind and solar energies. The second topic investi-gates the automation of underwater operations at WECs, while the third andlast topic presents the development of a positioning system for ROV basedon lasers and vision, to be used together with an autopilot for autonomousdocking.

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Table 1.1. List of ROV manufacturers and their products suitable for the UU WECdeployment and monitoring

ROV Manu-facturer

RecommendedROV

PurchaseQuote(kC)

Comments Picture

Mariscope CommanderMK II 140-175 Medium-class, instru-

ments to be added

Ocean Mod-ules

Ocean Mod-ules V8Sii 150 Medium-class, includes

a manipulator

SAAB SeaeyeCougar XT 190 Compact working-class,

includes a manipulator

Deep OceanExplorationand Research(DOER)

H2000 340Ultra-compact working-class ROV, includes amanipulator

SeamorMarine Chinook 210 Medium-class with ma-

nipulator

VideoRay Pro 4 50Ultra-compactobservation-class ROV,without tool

VideoRay Defender 150Ultra-compact medium-class, includes a manip-ulator

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2. Variability and forecasting of intermittentrenewable energy sources

2.1 Introduction2.1.1 State of the artGrid integration is the ultimate step in the development of renewable energytechnologies, and for variable energy sources such as wind, solar, tidal, andwave, where power generation fluctuates over time and space, two factorsgovern the success of this integration. First the resource spatial and tempo-ral variability determines the need for balancing power production over timeand through different grid locations. A high temporal variability will demanda faster compensation than a low variability. Second the capacity to forecastthe energy output will allow long- and short-term planning for balancing theproduction [44]. The objective remains for both matters to adjust the fluc-tuating power production to the varying power demand, over both time andspace.

Wind, solar, and wave energy industries are at different stages in addressingthose questions and have studied them separately with different approaches.However, differences between the resources variability could be used for bal-ancing and smoothing their combined energy production [45].

2.1.2 Aim of the chapterForecasting wind, solar, and wave energy has been studied under different for-mulations for each resource. Combining those studies would enable a betterplanning of the power grid management [46]. For both wind, solar, and waveenergy, a high forecast accuracy is usually linked to a smoother power produc-tion [44].

This chapter analyses both the models used to forecast those intermittentenergy sources, and compares their performances in terms of accuracy andtemporal and spatial resolution. Special focus will apply on wave energy.

The sections of this chapter are all referring to Paper II.

2.2 MethodsThe time horizon is a major parameter in the prediction of intermittent renew-able energies, and it varies significantly between solar, wind, and wave. A

21

time horizon of 1 to 72 hours is used for short-term planning, and 3 to 7 daysfor long-term planning, to forecast the energy production from wind powerplants. For wave energy, which has not yet reached a commercial stage, somestudies consider a grid management prediction from 1 to 7 days, related to thelow hourly variability of the source.

Regarding the forecast of the resource itself, the time horizons used differas well between sources. While it varies from about 30 s to 10 days for solarand wind energy, it is between 30 min and a few days for wave energy. Fore-casts between sub-seconds and minutes can be used in the active control of theturbines in wind or wave energy, while several days forecasts are necessary toplan maintenance of the farms.

Forecasting models can be broadly categorized into statistical and physicalmodels. Statistical models apply statistical methods on existing time-seriesof the resource, and do not involve any physical modelling of the resource.In contrast, physical models include a physical modelling of the atmosphere,based on different types of atmospheric data. Hybrids of physical and sta-tistical models are also commonly used by commercial forecasting software,whether for wind, wave or solar energy.

2.2.1 Forecasting accuracy metricsTo compare the performance of different forecasting models, a common metricis necessary. Yet each studied renewable source bases its forecast on differ-ent metrics and referring to different parameters. This poses a challenge incomparing the accuracy of the forecasting methods on different spatial andtemporal domains, and for three different energy sources.

The metrics and units used for forecasting wave energy are very spread out.The errors can refer to numerous parameters such as wave height, time pe-riod, energy power output, and a variation of different metrics are used suchas Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correla-tion, bias, Mean Absolute Percentage Error (MAPE). Solar energy forecast-ing models generally concern either the solar irradiance, expressed in W/m2,or the power output, expressed in kW. The metric used will typically be theRMSE, but some models also apply the MAE, standard deviation error, meanbias error, or mean square error. Forecasting wind could refer to either windspeed or produced power normalized to the turbine or plant power capacity.Commonly seen metrics are the RMSE, mean square error, standard deviationerror, MAE, and correlation. It is also frequent for wind and solar energy tocompare forecasting skills with help of a reference model, but in wave powerthis model is seldom used which makes comparisons difficult within waveforecasting models. The reference model is often the persistence model, alsocalled naive predictor, where the energy of the resource is predicted to remain

22

unchanged over the forecasted period. Other reference models exist but areless commonly used.

2.2.2 Forecasting modelsThe accuracy of forecasting models depends on the time horizon and the spa-tial resolution, and different methods are suitable for different temporal andspatial domains. A broad overview of recommended forecasting methods forthe solar, wind and wave resources, as a function of temporal and spatial res-olution, is presented in Figure 2.1. The figure shows that physical models aregenerally preferred over statistical models on longer time horizons and lowerspatial resolutions. Figure 2.1 includes the use of the persistence model aspresented in Section 2.2.1.

Figure 2.1. Comparison of forecasting methods between wind, solar, and wave energy,for different temporal and spatial resolutions (Paper II).

Among statistical models, there are more traditional approaches such asAuto-Regressive (AR) analysis, as well as more recently developed models.The latter include wavelet transforms and learning models [47] such as k-nearest neighbour or Artificial Neural Networks (ANN) [48], sometimes com-bined with Genetic Algorithms (GA) [49].

The physical models use different data inputs depending on the resourceand are specific to each renewable energy. Concerning wave energy, the in-puts are given by processes of wave generation, dissipation, and wave-waveinteractions. Those models are presently third generation, for example WAM

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Table 2.1. Details for the models compared in Figure 2.2 (Paper II).

Nr Resource Method used Site Metric Unit

1 Solar Physical: Satellite Images (SI) and Numer-ical Weather Prediction (NWP) using Na-tional Digital Forecast Database (NDFD)

6 sites across theUS

RMSE W/m2

2 Solar Physical: NWP from Weather Researchand Forecast model(WRF)

Andalusia, Spain RMSE W/m2

3 Solar Statistical: ANN Ajaccio, France RMSE J /m24 Solar Statistical: ANN+GA California RMSE W5 Solar Hybrid ANN+SI+lagged GHI Spain RMSE W/m2

6 Wave Physical: SWAN, Statistical: Spectralmodel

Four differentsites in Atlanticand one in Pacificocean

MAE m(waveheight)

7 Wave Statistical: mix of neural network and re-gressions. Two different time horizons an-alyzed: short-term and long-term

Four different lo-cations in the Pa-cific ocean

MAPE W

8 Wind Statistical: Markov-Switching AR model Two differentoffshore parks inDenmark

RMSE W

9 Wind Physical: Combined physical and statisti-cal (Fuzzy-NN)

11 wind farms inIreland

RMSE W

10 Wind Statistical: ARIMA-GARCH 64 wind farms inIreland

RMSE W

[50], SWAN [51], MIKE-21 [52], Mar3G [53]. A more detailed list of wavemodels operated by various national Meteorological Services is given in [54].

2.3 Results and discussionThe persistence model was used as a reference to compare the forecastingskills of the different models. The skill score was calculated based on themetrics used in the respective studies, as indicated in Table 2.1. Figure 2.2compares forecasting skills for different models proposed in the literature. Thedetails for the models represented by each curve are listed in Table 2.1. Foreach curve, the x-axis represents the time-horizon, and the y-axis representsthe forecasting skill. The metric is RMSE for wind and solar resources, MAEfor wave energy.

The forecasting skill value gets closer to one as the method used gets bettercompared to the persistence model. Negative values mean that the persis-tence model is better than the evaluated method. For the same resource, onecan compare different methods by looking at the forecasting skill values: thehigher they are, the better the method is. However this does not tell about thequality of the method.

In Figure 2.2 one can observe that the overall tendency for both solar, windand wave, is to have higher forecasting skill for statistical than physical modelsfor short time horizons and lower forecasting skills for long time horizons.

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Figure 2.2. Comparison of forecasting skills for different models in solar, wave, andwind energy (Paper II).

This supports the assertion that statistical models outperform physical modelsfor short time horizons, and the opposite for long time horizons.

However, the conclusions are limited by the use of persistence modelswhich differ for the different sources. The strength of the persistence mod-els is that they provide a way to compare studies that have used different errormetrics, although it should be kept in mind that different metrics can still givedifferent skill scores. However, when two different persistence models areneeded for two different sources, then the inherent differences between thepersistence models themselves introduce uncertainties into the study that ulti-mately makes it difficult to compare the variability of one resource to another.This leads us to question the use of persistence models if more than one isneeded for a study.

2.4 ConclusionIntegrating wind, solar, and wave energy into the grid could be improved byusing one single approach in the forecasting of those resources. A first aspectwould be to choose a common metric for the forecasting skill analysis. Inthis study the RMSE is often applied across the different energy domains andcould thus become the referential metric, but it is not yet systematic.

The use of a reference model is of interest when comparing different fore-casting models but is limited to each resource separately. The creation of aunique reference model that is functional for both wave, wind, and solar fore-casting models would be much more useful.

Another aspect is the predicted parameters, which varies for each source.Systematically forecasting power instead of irradiance, wave height, wind

25

speed, or any other unit, would simplify and standardize the prediction acrossall the intermittent energy sources.

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3. Automation of underwater operations onwave energy converters

3.1 IntroductionThe automation of deployment and maintenance activities on a WEC is doneto increase divers safety, and it should also address the technical challengesof reducing costs, complexity, and duration of the operations. This robotiza-tion process does not just consist in using ROVs instead of divers. It requiresa modification and transformation of the methods and tools originally devel-oped for diver-assisted operations, because an optimal method for divers is notobviously as efficient employing underwater vehicles.

3.1.1 State of the artDeployments at the Lysekil Research Site

To this day, deployments of WECs at UU are done with help of a four-diverteam for the underwater operations and in average three workers for the activ-ities on land. Among the different deployment methods that have been devel-oped at UU (Paper I, and [13]), the procedure used as a basis for this chapteris the following:

1. Device preparationThe device is prepared on land and transported to the deployment site. Thepreparation consists in placing slings at four points on the WEC foundation,as presented in Figure 3.1a. The WEC is transported on a barge from theharbour to the LRS.

2. Generator deploymentThe generator is lowered down to the seabed using a crane, at its location.It is slowly pressurized to 3.5 bars while being submerged in order to eventhe inner and outer pressure of the hull at 25 m depth. The pressurization isdone manually via a hose filling the WEC with 0.1 bar nitrogen every 1 m.Once the WEC has reached the seabed the pressurization van is closed andthe hose detached from the device by a diver. A pressurization inlet can beseen in Figure 3.1b, and more information can be found in [55].

3. Cable connection to MSThe first step in the cable connection is to move the electrical cable fromits generator to the MS. A diver picks the cable and bring it to the water

27

(a) (b)

Figure 3.1. (a) WEC L9 being deployed at the Lysekil research site, and (b) Pressur-ization inlet on WEC L9.

surface where a boat carries it to the MS. When close by the substation, thecable is dropped and a diver fetches it for the connection.The WEC is electrically paired to the MS via a four-connector cable. Theconnectors have one pin each and correspond to the three phases of the cur-rent output plus one ground. For cost reasons, the selected connectors areof dry-mate type, meaning that the connection sustains underwater environ-ment but the pairing must be carried out in a dry environment. To addressthis issue and allow a connection of the WEC to the MS underwater, Plexi-glas boxes were built to surround the plugs on the MS. They are open on thebottom, which allows divers to insert pressurized air in the casing to chaseaway the water contained in the box. When all the water has been replacedby air, the connection can be conducted in this artificially dry environment.When the connectors are mated the divers can stop inserting air and theboxes get filled with water again. That procedure is presented in the picturein Figure 3.2a.

4. Buoy preparationChains are hooked up to the buoy via shackles. Depending on the buoydesign it could be one or three chains. Those chains are then linked to thebuoy line via another shackle, which is to be attached to the generator linewhen deployed. A safety line is also added up between the buoy and thegenerator hull to prevent the buoy from being loose in case of a breakage of

28

the main line. Figure 3.2b shows a buoy developed by UU being deployedat the LRS after preparation.

(a) (b)

Figure 3.2. (a) A diver conducting a connection at the substation with help of pressur-ized air to allow the mating in an artificial dry environment, and (b) Ring buoy beingdeployed at the Lysekil research site.

5. Buoy deploymentThe buoy is carried over to the deployment site and lowered towards theWEC with the help of a crane. The translator is lifted up using airbags toallow the connection of the buoy to the generator by a diver, using a shackle,as well as the attachment of the safety line to the WEC. The buoy deploy-ment is a sensitive operation because the translator is heavy and should bekept stable when moving up, while the buoy is in motion at the water sur-face.In the deployment procedure, three steps out of the five are performed un-

derwater and could potentially be conducted by ROVs. In the inspection phase,all tasks could be robotized.

Tests for ROV-conducted cable connections

Two series of tests were conducted in Atvidaberg, Sweden, and Lisbon, Portu-gal, to evaluate the possibilities of an automated cable connection. The experi-ment consisted in grasping a connector on the bottom of a tank and connectingit to a socket mounted on the side wall. The connector comes from Subconnand is a one pin connector, identical to the one used at the substation in Ly-sekil. The test in Atvidaberg used a ROV Ocean Modules V8 Sii together witha one degree of freedom gripper from Sub Atlantic. The second experimentwas conducted with a ROV Seabotix LBV 200-4, which includes a simplegripper. Both tests are presented in Paper III.

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3.1.2 Aim of the chapterTo the author’s knowledge, research on the improvement of WEC deploymentis today very scarce. Yet, decreasing the costs and complexity of such oper-ations would allow more full scale testing of wave power plants, and a fastercommercial development. It could also extend to other sectors in marine re-newable energy, such as tidal power.

The objective of this chapter is to present a new method allowing ROV-conducted operations on point-absorber WECs, and to evaluate its repercus-sion on the costs and time of operation, compared to a traditional method usingdivers. This chapter is referring to Papers III, IV, and V.

3.2 Methods3.2.1 Development of a ROV-assisted deployment methodFrom the two methods proposed in Paper V using ROVs, one is presentedhere, where the generator is deployed together with its buoy. This is doneusing two cranes. The first crane carries the generator to the seabed, and thebuoy is lowered by the other crane to the water during the submersion. A ROVtakes off the slings and shackles, and disconnects the pressurization hose fromthe generator hull. The cable connection is done by two underwater vehicles:the first one drags the cable to the MS and performs the electrical connection,while the other fills the connection box with air. This procedure correspondsto the third method in Paper V.

The main advantage of this method is the bypassing of the buoy connectionprocedure- which is complex and entails high risks. However it requires moreequipment (two cranes and two ROVs) where one crane only is necessary fora diver-assisted deployment.

3.2.2 Comparative study on time and cost of operationsIn Paper V, a comparative analysis on the time and cost of operation evaluatesthe advantages in using one or the other deployment and monitoring method.The comparison is based on the costs and times fixed in Tables 3.1 and 3.2.Costs and times of operation are estimated and reflect ideal conditions whereno issues occur during the activity. In Table 3.1, one day is defined as an eighthours working day, and the ROV expense includes the personnel to operate it.For a diver, one day comprises eight working hours in terms of costs, but not interms of operational time. Indeed, international, European, and Swedish reg-ulations have guidelines for commercial diving where a decompression timeis to be adjusted according to the operational water depth ([56], [57]). At theLRS, where depth varies from 20 to 25 m, one day of diving with a four-diverteam will total 3.5 h of underwater activity and maximum seven dives.

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Table 3.1. WEC deployment expenses with divers and ROV assistance.

Symbol Resource Cost

Pdiver Diver 90 C/hPworker Worker for device preparation 50 C/hPboat Boat (for ROV or diver) 120 C/hPROV Pro VideoRay Pro 4 with cutter and gripper 7000 C/dayPROV Defender VideoRay Defender 7000 C/dayPROV Monitoring VideoRay Pro 4 alone 2000 C/day

Table 3.2. Time duration in hours for each step of a WEC deployment with divers andROV assistance.

Symbol Rubric Time per WEC usingdiver-assisted method

Time per WEC usingROV-assisted method

A Number of WECs - -

B WEC preparation 1 1.5C WEC submersion 0.5 0.5D Buoy preparation 1 1.5E Buoy connection 3 -F Cable connection 0.5 1

G Monitoring 0.5 0.5

Based on Tables 3.1 and 3.2, the following formulas are deduced to de-scribe the time and costs of deployment and monitoring for the diver- andROV-assisted methods. The deployment time is also calculated for each stepdescribed in Table 3.2.

Tdep div = A∗ (B+C)︸ ︷︷ ︸Generator deployment

+ A∗ (D+E)︸ ︷︷ ︸Buoy deployment

+ A∗F︸ ︷︷ ︸Cable connection

(3.1)

Tdep ROV = A∗ (B+C+D)︸ ︷︷ ︸Generator+buoy deployment

+ A∗F︸ ︷︷ ︸Cable connection

(3.2)

Tmon div =A∗G3.5

(3.3)

Tmon ROV =A∗G

8(3.4)

Cdep div = A∗ (B+D)∗3∗Pworker︸ ︷︷ ︸preparation

+A∗ (C+E +F)∗ 83.5

∗ (4∗Pdiver +Pboat)︸ ︷︷ ︸underwater work

31

(3.5)

Cdep ROV = A∗ (B+D)∗3∗Pworker︸ ︷︷ ︸preparation

+A∗ ( f loor(C8)+1)∗PROV Pro︸ ︷︷ ︸

Underwater work using 1 ROV

+A∗ ( f loor(A∗F

8)+1)∗ (PROV Pro +PROV Def︸ ︷︷ ︸

Underwater work using 2 ROVs

+A∗ (C+F)∗Pboat︸ ︷︷ ︸Boat

(3.6)

Cmon div = A∗G∗ 83.5

∗ (4∗Pdiver +Pboat) (3.7)

Cmon ROV = ( f loor(A∗G

8)+1)∗PROV Monitoring +A∗G∗Pboat , (3.8)

where Tdep div and Tdep ROV are the time of deployment for the diver- and ROV-assisted methods, respectively, expressed in hours, Tmon div and Tmon ROV arethe time of monitoring for the diver- and ROV-assisted methods, respectively,expressed in days, Cdep div, Cmon div, and Cdep ROV, Cmon ROV are the costs ofdeployment and monitoring for the diver- and ROV-assisted methods, respec-tively, expressed in C.

3.3 Results3.3.1 Comparative study on time and costs of deployment

operationsThe results of the comparative study on the diver-assisted and ROV-assistedmethods for a WEC deployment are presented in Figures 3.3a and 3.3b, andrefer to Paper V. In terms of time, the method using ROVs is more advanta-geous because it contains only two steps while the one using divers has three,of which one (the buoy connection) is rather long and complex. That stepcould become more time efficient with the help of a tool assisting the diversin the lifting of the translator, for example a winch placed on the buoy. Addi-tionally, one should stress that the time presented on the graph is expressed inhours. The divers being limited to 7 dives and 3.5 h of underwater activity perworking day, the operational time is even more restricted than what is shownon the graph. On the other hand the diver method uses less material, whereonly one crane is needed compared to two for the ROV method. This bene-fit could lead to select a diver-assisted procedure compared to ROV-assistedwhen the time difference between the two methods is small, namely for fewWECs to deploy.

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(a)

(b)

Figure 3.3. Comparison of the two deployment methods (using divers and ROVs) for anumber of WECs ranging from 5 to 100, with (a) the operational time for the differentdeployment steps, and (b) the operational cost.

In terms of costs, employing divers is preferable when the number of WECsto deploy is inferior to fifteen, while from fifteen WECs and above, ROVs willinduce less expenses. The explanation for this crossover comes from the cableconnection phase. It requires two ROVs and 0.5 h of time compared to onedive of 0.5 h per WEC. For up to 15 WECs, the total step duration using ROVstakes less than a day, but since they are charged per day, the rental price staysconstant. As for divers, the costs will start low and increase with the numberof WECs. Yet, the cable connection could be improved with the help of thetwo assisting tools described in Section 3.1.1.

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3.3.2 Analysis of the ROV-assisted deployment methodThis method has been developed based on individual missions performed withROVs on the UU WECs or in tanks. Those missions concern the followingtasks:• Valve closure• Hose disconnection• Cable connection

The other tasks of slings and shackles removal have not been tested and arecomplex to automate because the procedure to disconnect a shackle requiresboth force and accuracy. Figure 3.4 shows a bolt-shackle that is commonlyused in the Lysekil project, and the different steps to open it. The slings andshackles disconnection task, and the buoy monitoring, which basically con-sists in replacing damaged shackles, could be carried out by a ROV if specificshackles were designed for ROV use.

Figure 3.4. Illustration of a bolt shackle and the three steps to open it.

Regarding the cable connection phase, the tests described in the introduc-tion and in Paper III were performed without taking into account the plexiglasairbox. Three conclusions can be drawn from those tests. First, the position-ing of the ROV at the connecting point is the most challenging task. The taskwas considered by the operators as uneasy to perform in laboratory conditions,and deemed unachievable in a real environment. Second, an alignment tool tocoaxially line up the two connectors before pairing would significantly facil-itate the coupling. Finally, the available thrust onboard both ROVs was notsufficient to provide full contact of the connectors.

The technical challenges related to cable connections on the UU WEC aresummarized as follows:

a. Working environment with waves and current resulting in instabilityof the ROV position;

34

b. Need for an accuracy in the connector position and angle of 5 mmand 5◦ respectively, in order to insert the connector;c. Need for removing the cap from its connector when inside the airpocket;d. Payload of 200 N requested to perform the connection;e. Capability for cable disconnection;

Items a, b, and d, can be solved by developing a docking system for ROV tostabilize the vehicle and serve as support structure for the connection. Pointsb, c, and e require a special connecting tool to enable the alignment of thecables before contact and disconnection. In Paper IV, a review was conductedto find a suitable electric connector for ROV-conducted cable connections. Theoutcome of that paper is that very few connectors could be used at the LRS,and especially for ROV-conducted operations.

3.4 Discussion3.4.1 Towards fully ROV-conducted WEC operationsOverall, from Section 3.3.1, it can be emphasized that employing ROVs is ad-vantageous compared to divers, especially for large deployments. Until now,the Lysekil project has only deployed single or couples of WECs, hence thequestion of using ROVs for WEC commissioning remains.

The question of hiring or purchasing a ROV should also be discussed. InPaper V, Table 3.3 shows the break-even for purchasing two ROVs that aresuitable for operations at the LRS. They are of 25 and 23 working days, re-spectively, depending on the chosen vehicle. For a wave power plant of tenconverters, two days of underwater work are needed for the deployment andone day per year for monitoring. The ROV is hence fully refunded after 23 and21 years, respectively. For a plant of 100 WECs, the underwater work takes19 days, and the monitoring 10 days per year. Thus, the return on investmentis less than two years, which should be compared to the expected life time of25 years for a wave power park.

For large parks, and if the ROV fits both deployment and monitoring mis-sions, it would thus be economically advantageous to purchase a ROV insteadof hiring one. Nevertheless, this assertion is to balance with the need forexperimented operators to manoeuvre and maintain the vehicle, which canmake occasional ROV users prefer renting to buying. To this day, few ROVsare utilized for wave and tidal energy conversion technologies, and few worl-wide conferences are exclusively and mainly focused on underwater robots. Itproves that there is a gap between the ROV market development and the needfor user-friendly products. Developing simple ROVs with easy steering andmaintenance could create an increase of sales to small companies or researchgroups with no ROV experience.

35

Table 3.3. Costs for purchasing and renting two suitable ROVs for WEC deploymentand monitoring, and break-even time.

ROV Purchase Rates (C) Abilities Rental Rates (C/day) Break Even

Videoray Pro 4with 100 m cableand manipulator

50,000excl. VAT

Disconnect slings, removeshackles, holding the pres-surized air hose for electricalconnection

2000 for inspections,7000 for operations

From 7to 25 daysof operation

Videoray Defenderwith 400 m cableand rotatingmanipulator

150,000excl. VAT

Generator deployment, holdingthe pressurized air hose forelectrical connection, perform-ing a connection

7000 for operations 23 daysof operation

3.4.2 Development of assisting tools for ROV operationsPapers III, IV, and V attest of the lack of tools and equipment for assistingROVs in their tasks. Developments that would be most beneficial to WECdeployments are:

• automatic shackles for ROV use• electrical connectors for ROV use• specific grippers and manipulators• drums for cable dragging• docking systems• positioning systems and autopilots

The docking system tested in Paper III would typically meet the needs ofthe substation, if used together with a connecting tool. A good possibilitywould also be to develop ROV-friendly electrical connectors, where the reviewconducted in Paper IV puts forward two innovations only ([58], [59]). ROV-friendly shackles can be found at high costs and should be further developedfor a better accessibility on the market. Off-the-shelf products for ROV useare to this day very few in numbers and ought to become more affordable.

Autopilots would allow more people to operate underwater vehicles, butthey rely on accurate positioning systems which have not reached the requiredlevel of readiness. Products are still under development, both in terms of hard-ware (less energy consuming and lower cost sensors [60], [61]), and software(more advanced and optimized algorithms that can be processed by smallercomputers [62], [63]).

3.4.3 Adapting processes from the offshore wind energy sectorAs offshore wind power plants are increasing in numbers, processes associ-ated to the deployment and monitoring of their devices are being automated.In monitoring procedures ROVs are extensively used for seabed and structureinspections [64], launching and retrieving of measurement packages, plank-ton sampling, and in situ monitoring [65], which means that such ROVs areavailable for similar operations on WECs. The deployment and decommis-sioning of wind energy devices also present similarities with wave energy in

36

the use of ROVs. In [64], underwater vehicles are used for the installation oflifting attachments on structures such as foundations, jackets, or monopiles.They could easily be replicated and adapted for the commissioning of WECs,which shows identical needs. Operations related to electrical cables are alsotypically similar. The offshore wind energy sector makes great use of ROVsfor laying, trenching, dragging, and localizing cables, both for inner-array andfrom park to shore [64], [66], [67], [68]. All operations related to cables canbe applied to the wave energy field. Concerning cable connections, the chal-lenges can differ, as offshore wind power devices usually have their electricalcouplings located above the water level. Turbines are placed on top of themasts and substations are usually settled at the water surface so there is noneed for ROVs to perform the connections. Finally, [69] promotes the use ofROVs in shallow water operations, where large vessels are not exploitable. Atthose depths ROVs are a valuable mean of support for any offshore renewableenergy conversion technology deployment.

3.5 ConclusionThe automation of underwater operations on WECs by ROVs is overall bene-ficial both economically and in terms of efficiency, especially for large wavepower parks. The inspection of WECs and their cables could easily be doneby underwater robots instead of divers because such vehicles are low cost andaccessible on the market. Some tasks must be further adjusted in order to en-sure automated actions by one or two ROVs. For example the deploymentof the buoy must be done together with the generator so that no lifting of thetranslator is needed. The cable connection requires two different vehicles tobe performed: a ROV with large payload can drag the electrical cable from theWEC to the substation and carry out the connection, while a smaller ROV caninsert pressurized air in the pocket before connection.

Some additional tools would facilitate the automation of underwater oper-ations, such as ROV-friendly slings, shackles, electrical connectors, and cabledrums. Some others seem obligatory for enabling a successful ROV-conductedwork. Such equipment involves a docking system and a connecting gripper forcable connection at the substation. Some of those tools can be copied off theones used in the offshore wind industry, because the tasks performed are com-paratively similar.

There is a lack of medium-size ROVs on the market and their associatedequipment. Those ROVs should be designed for shallow water and accurateoperations, and include a navigational aid, for example an autopilot, so thatoperators can easily steer and manipulate the vehicle with little familiarisa-tion. Plug-and-play ROVs should have a place on the market so that smallcompanies or research groups with no ROV experience can have easier accessto underwater robotics.

37

4. Laser-based relative positioning system forremotely operate vehicle

4.1 Introduction4.1.1 Docking system for ROVOne of the conclusions from Chapter 3 is that the automation of cable con-nections on wave energy converters requests a docking system in order for amiddle-size ROV to perform those connections.

The design of a docking system at the substation has three objectives:• to obtain a correct ROV positioning before connection• to provide the ROV a stable structure to push against when performing the

connection• to enable a connection independently from the operator’s skills

A docking system was designed by Instituto Superior Tecninco (IST) andWavEC, Portugal, in partnership with UU, to address the requirements of theLysekil project (Paper III). It contains two parts: one is placed on the ROVand consists in a frame with four conic shaped docking slots on each corner.The other is fixed at the substation and is defined by a plate with holes in eachcorner where the cones on the ROV part would be inserted. A docking testwas carried out with the ROV Seabotix LBV 200-4 and its associated dockingsystem in the tank of Taguspark, Lisbon, Portugal, where the docking opera-tion took in average 1 min 15 sec. A picture of the test can be seen on Figure4.1.

Figure 4.1. Illustration of the ROV approaching the docking station (Paper III).

From the pilot’s perspective, a supplementary aid in the navigation wouldallow a faster and easier procedure, especially in the case where a non-experienced

38

operator uses the ROV. This aid could be a positioning system coupled to anautopilot for a fully autonomous docking.

4.1.2 State of the art in underwater positioningUnderwater positioning is a great challenge because operating conditions forpositioning sensors are different between air and aquatic environments. Underwater, at high frequency, electromagnetic waves are quickly damped due tothe water absorption and salinity [70], preventing any GPS or radio frequencysensor from transmitting signals. In the visible spectrum, water turbidity stopslight waves before they have travelled a few meters. Acoustic waves are sub-ject to noise due to the water particles density. Yet, positioning is essential inrobotics, especially for autonomous missions.

Two common methods for underwater positioning use acoustic waves andvision. Acoustic sensors are the most widely used, and a large variety of themexists on the market [71], [72], [73], [61]. Those sensors have a sensing rangestarting from a few meters and up to 10 km with an accuracy varying from 0.1m to a few meters.

For docking, the range and accuracy should be reduced because ROV posi-tioning takes place in the interval [0;5] m, and demands an accuracy of approx-imately 2 cm in distance and 5◦ in angle. Some acoustic sensors are locateddirectly on the docking system, which are not space constrained. With a biggersize, the sensor localization range and accuracy can be increased. In the caseof cable connections, the docking procedure is done multiple times accordingto the number and location of the connectors. Hence the positioning sensormust be placed directly on the vehicle.

With the enhancement of video quality and image processing speed, opticalsensors are increasingly used for underwater positioning and sensing of theenvironment [74], [62], [75], [76]. It is thus an interesting alternative toacoustic sensors. However, distance estimation of relative distance and head-ing from images or a video feed strongly relies on underwater visibility, whichis highly affected by turbidity, floating sediment, plankton and general weatherconditions. All these factors challenge the creation of reliable estimates thatenable relative positioning of the vehicle with the accuracy aforementioned.The integration of coloured lasers into the vision system can boost visibilityin underwater environment; in fact the projected light onto the target struc-ture provides strong features to be tracked by the vision algorithms used forrelative pose estimation.

4.1.3 Aim of the chapterThis chapter presents a laser-based optical system for underwater relative po-sitioning that integrates a monocular camera with line beam lasers and relies

39

on image processing for estimation of distance and heading. An autopilot ismodelled with the aim of using the positioning system for autonomous dock-ing.

The positioning system was tested experimentally and the output data areused to feed the autopilot during the simulation. The novelty of the positioningsystem relies in the computational speed of the image processing in regards toits robustness.

This chapter is principally based on Papers VI, VII, and VIII.

4.2 Background and Theory4.2.1 Problem formulationThe position of the ROV is to be found relative to the docking station. Inthis chapter, the docking station is modelled by a flat surface called Target.The developed positioning system uses the underwater images streamed froma vehicle’s camera to obtain its horizontal position relative to the target.

Two reference frames are defined prior to formulating the problem:{T}: Reference frame based on the target where the x-axis corresponds to

the target normal, and the y- and z-axis are in the target plane with z pointingdown. The x-axis is pointing towards the back of the plate, meaning that whenthe vehicle is going towards the target, both x-axis are pointing in the samedirection.

{B}: Body-fixed frame with its origin the vehicle’s center of gravity (CG),and x, y, and z-axis corresponding respectively to the vehicle’s surge, sway,and heave in the front, starboard, and down directions.

The vehicle’s position is expressed with the pair (d,ψ) where d is the dis-tance between the ROV’s CG and the target in the vehicle x-axis, and ψ is theangle between the camera axis and the target normal. The problem formula-tion is illustrated in Figure 4.2.

The principle of the developed positioning system is based on the followingassumptions:

• The relative positioning is referred to positioning in the horizontal plane.• The vehicle is stabilized in roll, pitch, and heave motion.• The target is an infinitely large flat platform.

4.2.2 Hardware and test facilitiesIn Paper VII, the BlueROV is used for the experiment. It is an observation-class ROV from Blue Robotics 1. It is powered by a battery and containssix thrusters, a depth sensor, and Inertial Measurement Unit (IMU). The on-board computer is an Odroid XU4, used together with a PixHawk autopilot

1https://www.bluerobotics.com/

40

Figure 4.2. Illustration of the problem formulation (Paper VIII).

that serves as middle-ware between the operator and the vehicle. The Odroidcomputer works on Ubuntu 14.06 and runs the Robot Operating System (ROS)for ROV control and sensor implementation.

Each propeller is controlled by an Electronic Speed Controller (ESC) whichreceives the signals sent by the operator and converts them into the current todeliver to the thruster.

The BlueROV was tested in laboratory conditions at the Technical Univer-sity of Denmark, in a water tank. One of the tank sidewalls was used as thetarget. A motion tracking unit, located outside of the water, was sensing thedisplacements of the vehicle. Those data were used as a benchmark for thetesting of the positioning system. A picture of the BlueROV equipped withthe motion tracking system is presented in Figure 4.3a. A picture of the exper-iment where the BlueROV is equipped with the laser-based positioning systemcan be seen in Figure 4.3b.

4.2.3 BlueROV modelIn Paper VIII, a mathematical model of the BlueROV is described for the con-struction of its observer and controllers. It comprises two main componentsthat are the propulsion system and the vehicle dynamics. All the numericalvalues used in this model are taken from [77].

Notations

We define the following notations:

η :=[x y z φ θ ψ

]T

41

(a) (b)

Figure 4.3. (a) Motion tracking system installed on BlueROV, and (b) Underwaterexperiment (Paper VII).

Table 4.1. Notation used for the dynamics of a 6-DoF ROV (Paper VIII).

Degree of Freedom (DoF) Position andEuler angles

Linear androtationalvelocities

Forces andmoments

1- Motions in the x-direction (surge) x u X2- Motions in the y-direction (sway) y v Y3- Motions in the z-direction (heave) z w Z4- Rotations about the x-axis (roll) φ p K

5- Rotations about the y-axis (pitch) θ q M6- Rotations about the z-axis (yaw) ψ r N

is the position of the origin of B and rotation of B expressed in T.

ν :=[u v w p q r

]T

is the linear and angular velocity of the origin of B relative to T, expressed inB.

τ :=[X Y Z K M N

]T

is the linear and angular thrust forces and moments applied to the origin of Brelative to T, expressed in B.

The BlueROV contains six thrusters and for each of them one gives thenotation ui for the control signal sent to the thruster controller, and Fi for theforce provided by it.

The notations are summarized in Tables 4.1 and 4.2.

Propulsion system

The propulsion system is modelled to describe the overall force response τ =[X ,Y,Z,K,M,N]T to the control inputs [ui]i=1:6, where ui are pulses varying

42

Table 4.2. Notation used for the thrusters on the BlueROV (Paper VIII).

BlueROV Thruster Control signal Force

T1 u1 F1T2 u2 F2T3 u3 F3T4 u4 F4T5 u5 F5T6 u6 F6

Table 4.3. Coefficients used for the thruster model, with d=40μs (Paper VIII, from[77]).

Thruster a+i b+i c+ia−i b−i c−i

T1/T2 3.12 ×10−4 -8.97 ×10−1 6.42 ×102

-2.65 ×10−4 8.25 ×10−1 -6.41 ×102

T3/T4 1.03 ×10−4 -2.46 ×10−1 1.37 ×102

-7.68 ×10−5 2.84 ×10−1 -2.52 ×102

T5 2.47 ×10−4 -6.90 ×10−1 4.77 ×102

-9.01 ×10−5 3.12 ×10−1 -2.64 ×102

T6 1.37 ×10−4 -3.78 ×10−1 2.58 ×102

-1.15 ×10−4 3.77 ×10−1 -3.07 ×102

between 1100 and 1900 μs. To protect the thruster from overheating and be-cause high pulse values could damage them, the control inputs are restrainedto the interval [1300;1700] μs.

Each thruster is modelled by a second order transfer function for each ro-tational direction, together with a dead-band d corresponding to the non re-sponse of the thruster to the command ui. It can be written:

Fi =

⎧⎪⎨⎪⎩

a+i u2i +b+i ui + c+i , ui > 1500+ d

2 ( f orwardmode)0, ui ∈ [1500− d

2 ;1500+ d2 ]

a−i u2i +b−i ui + c−i , ui < 1500− d

2 (backwardmode), (4.1)

where Fi is the force of each thruster expressed in N, ui is the control input inμs, and d is the dead-band in μs. The coefficients a+i , b+i , c+i , a−i , b−i , and c−i ,as well as d, have been experimentally identified in [77] and are summarizedin Table 4.3.

43

There is a relationship between the vector F of forces Fi from each thrusterand the resulting forces and moments τ applying on the vehicle, given by:

τ = T.

⎡⎢⎢⎢⎢⎢⎢⎣

F1F2F3F4F5F6

⎤⎥⎥⎥⎥⎥⎥⎦ , (4.2)

where T is a matrix whose values depend on the thrusters’ location. T wasdetermined from [77] as:

T =

⎡⎢⎢⎢⎢⎢⎢⎣

1 1 0 0 0 00 0 0 0 0 10 0 −1 −1 −1 00 0 0.11051 0.11051 0 −0.08734

−0.02151 −0.02151 0.16568 0.16568 −0.18592 0−0.11051 0.11051 0 0 0 0.0272

⎤⎥⎥⎥⎥⎥⎥⎦ .

ROV kinematics

Details about the kinematics and kinetics of an underwater vehicle can befound in [78]. The two following paragraphs summarize the mathematicalmodelling of the ROV behaviour.

The vehicle’s flight path relative to the reference frame T is given by avelocity transformation:

η = J1(η)ν , (4.3)

where

J1(η) =

[BR

U(φ ,θ ,ψ) 00 Tθ (φ ,θ)

],

with

BRU(φ ,θ ,ψ)) =

⎡⎣cθcψ cψsθsφ − sψcθ cψsθcφ + sψsφ

cθsψ sψsθsφ + cψcφ sψsθcφ − cψsφ−sθ cθsφ cθcφ

⎤⎦ ,

Tθ (φ ,θ) =

⎡⎣1 sφ tθ cφ tθ

0 cφ sφ0 f racsφcθ f raccφcθ

⎤⎦ ,θ �=±90◦,

and using the shorthand s· = sin(·), c· = cos(·), t· = tan(·).

44

ROV kinetics

The behaviour of a ROV can be defined by the following equation:

MRBν +CRB(ν)ν = τ + τA + τD + τR + τdist , (4.4)

where MRB is the rigid body inertia matrix, CRB(ν) represents the Coriolisand centrifugal terms and the terms on the right hand side of the equation aredefined as:• τ - As presented in paragraph 4.2.3, vector of forces and torques due to the

thrusters.• τA - The force and moment vector due to the hydrodynamic added mass:

τA =−MAν −CA(ν)ν (4.5)

where MAν and CA(ν) denote the rigid body inertia and Coriolis added massmatrices

• τD - Hydrodynamical terms due to lift, drag, skin friction:

τD =−D(ν)ν , (4.6)

where D(ν) denotes the damping matrix• τR - Forces and torques due to gravity and buoyancy, also called restoring

forces.• τdist - Forces and torques due to disturbances.

Assumptions for model simplification

This model is accurate but contains many free parameters that are difficult toestimate. Thus the model is simplified under the following assumptions:1. roll, pitch, and heave motions are stabilized (φ , p,θ ,q,z,w = 0)

It is common that roll and pitch are automatically stabilized in ROVs, aswell as to activate a depth-hold control. It is the case for the BlueROV,whose model is hence reduced to 3 DoF instead of 6:

η =

⎡⎣ x

yψ

⎤⎦ , μ =

⎡⎣u

vr

⎤⎦ , and τ =

⎡⎣X

YN

⎤⎦

2. Disturbances are limited to irrotational currents of constant velocity:

νc = [νcx,νcy]T

This assumption also results in that the terms MA and CA are independentfrom the disturbances, and depend only on the vehicle’s velocity.

3. The ROV has body-symmetry in the xz-plane (port-starboard symmet-

rical vehicle)

The rigid-body mass matrix can hence be written:

MRB = diag(m,m,m, Iz),

45

where m is the vehicle’s mass and Iz is its moment of inertia around thez-axis.

4. Off-diagonal elements of the added-mass matrix are small compared

to the diagonal

The added-mass can thus be defined as:

MA =−diag(Xu,Yv,Nr),

where Xu, Yv, and Nr are the decoupled added mass and inertia coefficientsin surge, sway, and yaw, respectively.

5. The hydrodynamical matrices CA(ν), and D(ν) are defined as follows:

CA(ν) =

⎡⎣ 0 −Yvr 0

Xuv 0 0Xur −Yvu 0

⎤⎦ ,

where Xu,Yv, and Nr are the added mass coefficients,

D(ν) =

⎡⎣−Xu −Xu|u| | u | 0 0

0 −Yv −Yv|v| | v | 00 0 −Nr −Nr|r| | r |

⎤⎦ ,

where Xu, Xu|u|, Yv, Yv|v|, Nr, and Nr|r| are the drag coefficients. In this thesis,the vehicle moves mainly in the forward direction (x-axis) and thus, the nonlinear damping from the y-axis can be neglected: Yv|v| = 0.

The BlueROV kinetics can thus be summarized as:⎡⎣u

vr

⎤⎦=

⎡⎢⎣

1m−Xu

((m−Yv)vr− (−Xu −Xu|u| | u |)u+ τu)1

m−Yv(−(m−Xu)ur− (−Yv)v+ τv)

1Iz−Nr

((Xu −Yv)uv− (−Nr −Nr|r| | r |)r+ τr)

⎤⎥⎦ (4.7)

The mass, inertial, added mass, first and second order drag parameters aregiven in Table 4.4, and were experimentally identified in [77].

4.2.4 Control theoryExtensive literature can be found on control theory. This section is using [79]and [80] as main references.

We consider the following state space model:

⎧⎪⎨⎪⎩

x = Ax+Bu+Nν1

z = Mxy =Cx+ν2

, (4.8)

46

Table 4.4. Parameters used for the BlueROV model (Paper VIII, from [77]).

DoF Coefficient Value Unit

m 7.06 kgIz 0.1995 kg m2

SurgeXu -6.02 kgXu -15.21 kg s−1

Xu|u| -25.38 kg m−1

Sway Yv -11.81 kgYv -52.233 kg s−1

YawNr -0.286 kg m2

Nr -0.7143 kg m s−1

Nr|r| -1.127 kg m

where x is the state variable, z the controlled variable, u the input variable, ythe measurement, ν1 and ν2 are white noise.

The control objective can be written as:

J(u) = limt→∞

[∫ t

t0(xT Q1x+uT Q2u)dt

], (4.9)

where Q1 and Q2, the weighting matrices, are design parameters that are sym-metric positive definite.

The optimal controller for this system is given by the linear quadratic regu-lator u =−Kx+Lrr, where x is the state variable and r the reference.

The gain K is defined by:

K = Q−12 BT P,

with P solving the Algebraic Ricatti Equation (ARE):

0 = AT P+PA+MT Q1M−PBQ−12 BT P, (4.10)

Lr is the optimal state feedback gain:

Lr = (M(−A+BK)−1B)+, where + denotes the pseudoinverse.

4.3 Methods4.3.1 Positioning systemPrinciple

The developed positioning system integrates a monocular camera with twolasers and exploits image processing techniques to estimate relative distance

47

Figure 4.4. Schematic of the positioning principle including setup errors. L is thedistance between the lasers, Fd is the focal distance of the camera. Xa and Xb aretwo distances measured in pixels on the camera image, ψ and D are the measuredvariables. α , β and da, db, are the angular errors between each laser beam and cameraaxis, respectively the distance errors between each laser source and camera lens (PaperVII).

and heading based on feature extraction from each grabbed frame. The princi-ple of the positioning system is described in Papers VI and VII and a schemeof the setup is presented in Figure 4.4.

Using Thales’ theorem and including the setup errors, the variables ψ andD are computed according to:{

ψ = arctan(

(Da+da)−(Db+db)L+2Datan(α)+2Dbtan(β )

)D = (Da+da+Da tan(α) tan(ψ))+(Db+db+Db tan(β ) tan(ψ))

2

(4.11)

with⎧⎨⎩Da =

Fd L2 −daXa

Xa−Fd tan(α)

Db =Fd L

2 −dbXbXb−Fd tan(β )

(4.12)

where L is the distance between the lasers, Fd is the focal distance of thecamera, Xa and Xb are two distances measured in pixels on the camera image,α , β and da, db, are the angular errors between each laser beam and cameraaxis, respectively the distance errors between each laser source and cameralens. Da and Db are the distances between each laser and the target.

48

Line detection algorithm

The line detection algorithm detects the laser beam projections on each imagegrabbed by the camera. The distance in pixels from each line to the imagecenter serves as a basis for tracking the ROV position relative to the target.The algorithm is summarized as follows:1. The image is converted from BGR to HSV and cut in two halves.2. The half images are filtered by applying a linear function based on their

standard deviation.3. The lines are extracted using the Probabilistic Hough transformation [81].4. The lines parameters are used to compute the positioning variables [d,ψ].5. If one line fails to be detected, the emergency mode is activated, where the

positioning variables [d,ψ] are computed from one line only.

The main functions this algorithm is composed of are further described inPaper VII. The computational time measured for each image is in average13.6 ms, for an operating frequency of 30 Hz. Figures 4.5a-4.5d illustrate theimage processing performed during the experiment.

Experiment

In Paper VII, the experimental campaign was performed in two phases whichare schematized in Figures 4.6a and 4.6b and described as follows:Phase 1 Fixed ROV orientation, varying ROV position. The vehicle is placed

perpendicularly to the target and moves forward and backward so thatonly the distance changes, with a constant heading of approximately 0◦.

Phase 2 Fixed ROV position, varying ROV orientation. The vehicle sweepsthe tank’s sidewall, maintaining its position and changing only its ori-entation around the z-axis. This second phase is done at three differentpositions along the x-axis: at 0.85 m, 0.6 m, and 1.15 m from the wall.

4.3.2 Closed-loop modelOverall model

The closed-loop model of the autopilot is presented in Figure 4.7 and describedin Paper VIII. It consists of a path-following unit, a controller, a model of theBlueROV, and an estimator using both theoretical positioning and real mea-surement data.

The propulsion and BlueROV models are designed as in Section 4.2.3. Forthe design of the other units, the following variables are defined:

X1 :=

⎡⎢⎢⎣

ψuvr

⎤⎥⎥⎦ is the state variable,

49

(a) (b)

(c) (d)

Figure 4.5. The four steps of the line detection algorithm, with (a) Original image, (b)Extracted lines on original image, (c) Detected line on left part of the filtered image,and (d) Detected Line on right part of the filtered image (Paper VII).

u :=

⎡⎣τu

τvτr

⎤⎦ is the control input,

Z :=

⎡⎣u

vψ

⎤⎦ is the controlled variable,

X2 :=[

Xψ

]is an intermediate variable used to compute the measurement Y ,

Y :=[

dψ

]is the measurement,

50

(a) (b)

Figure 4.6. Experiment schematics for both phases: (a) Phase 1- ROV perpendicularto the target, and (b) Phase 2- ROV sweeping the target from three different positions(Paper VII).

Measurement

Measurement system

Clock

Path

dist_est

yaw_est

Ref

Path-following Unit

Pos_theoretic

Measurement

Time

Pos_estimate

Estimator

Uref

X

Reference

Inner-loop controller

Disturbances

Thruster input

y

X

ROV Dynamics

Control signal Thrust

Propulsion model

PathPath to follow

-C-Disturbances

12:34Digital Clock

Figure 4.7. Closed-loop model of the autopilot (Paper VIII).

νc :=[

νcxνcy

]is the external water current, as defined in Section 4.2.3.

The following semi-linear state-space model is built:

⎧⎪⎪⎪⎨⎪⎪⎪⎩

X1 = AX1 +Bu+ν1

Z = MX1

X2 = f (X1,νc)

Y = g(X2)+ν2

, (4.13)

51

where

A =

⎡⎢⎢⎢⎣

0 0 0 10

Xu+2Xu|u|m−Xu

0 00 0 Yv

m−Yv

m−Xum−Yv

0 0 Xu−YvIz−Nr

−drIz−Nr

⎤⎥⎥⎥⎦ , B =

⎡⎢⎢⎢⎣

0 0 01

m−Xu0 0

0 1m−Yv

00 0 1

Iz−Nr

⎤⎥⎥⎥⎦ ,

M =

⎡⎣0 1 0 0

0 0 1 01 0 0 0

⎤⎦ ,

where f and g are defined by:

f (X1,νc) =

[(u+νcx)cosψ − (v+νcy)sinψ

ψ

], g(X2)

[ xcosψψ

],

and where ν1 and ν2 are white noise with intensity R1 = diag(10−5,10−5,10−5,10−8

),

respectively R2 = diag(0.0016,0.003).

Controller

The controller is designed according to the lqr method described in Section4.2.4, and using the state-space model aforementioned.

The weighting matrices Q1 and Q2 are chosen as:{Q1 = 5.diag(1,1.56,156.2,8.65)Q2 = diag(0.0062,0.04,1.13)

. (4.14)

Estimator

In Paper VIII, the computed measurement Y is fused with experimental mea-surements from the tank tests, constituted in a data set of [d,ψ]. The fusion isobtained through the following procedure:1. The variable Y = [d,ψ]T is computed from the state-space model described

in Eq. 4.13.2. A subgroup of data, called matching group is chosen from the measurement

data set, composed of all the pairs belonging to the interval:{[d −dSD;d +dSD]

[ψ −ψSD;ψ +ψSD],

where dSD and ψSD are the standard deviations of the distance and headingmeasurements:{

dSD = 4 cmψSD = 3◦

.

3. A pair [d,ψ] is randomly picked within the matching group and defines theposition Y to feed the path-following unit.

52

Path-following unit

The path is a matrix composed of way-points defined by pairs [dr,ψr]. TheROV should reach each of them and stop in the last position.

The reference is given based on the position to reach [dr(i),ψr(i)] and theestimated measurement [dest(k),ψest(k)]:

Ure f (k) =

⎡⎣ure f (k)

vre f (k)ψre f (k)

⎤⎦ .

where vre f (k) = 0, ψre f (k) = ψr(i), and ure f (k) is given by:

ure f =

{arctanεdist if | εdist |� 0.020 if | εdist |< 0.02

.

When the errors in distance and heading have been reached - 2 cm and 4◦respectively, a new direction is given by the next way-point in the Path matrix.The limits were defined by the accuracy required for the docking procedure.

Simulation

The simulation is based on the following initial conditions and initial path:⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

X0 =

⎡⎢⎢⎢⎣

π/4000

⎤⎥⎥⎥⎦

Y0 =

[−2.5π/4

]

νc =

[00

]

Path =

⎡⎢⎢⎢⎢⎢⎢⎣

−2.3 π/3−1.15 0−1.12 −0.7−0.9 0.35−0.6 0

⎤⎥⎥⎥⎥⎥⎥⎦

.

The sample time is Ts = 0.1 s.The external current is set to νc = [0,0]T .The Path was chosen so that as many positions as possible were picked

among the optical data gathered during the open-loop tests. The simulationwas thus limited in terms of path and way-points.

53

4.4 Results and discussion4.4.1 Open-loop positioningIn Paper VII, in order to evaluate the positioning accuracy, the MAE is cal-culated between reference (from the motion tracking sensor) and estimateddistance d and heading ψ for different intervals. For Phase 1, the resultingstatistics are presented in Figures 4.8a and 4.8b. For phase 2, they are pre-sented in Figures 4.9a and 4.9b.

(a)

(b)

Figure 4.8. (a) Distance and (b) Heading error analysis, Phase 1 (Paper VII).

For the error in distance, the same trend is observed in both phases. TheMAE decreases slightly when going away from the target, and then increasesagain for even larger distances. The explanation is that when the vehicle isclose to the target, the laser lines appear thicker in the image plane and lead togreater variations in the estimated values Xa and Xb. On the other hand, whenfar from the target the laser lines appear thinner on the wall and might not bedetected by the vision system. Both effects are reflected in the MAE. Thisissue could be addressed by using two sets of lasers instead of one, arrangedin two different ways. One has a larger gap and is turned on when far from

54

(a)

(b)

Figure 4.9. Error analysis for (a) the distance and (b) the heading at three differentdistances, phase 2 (Paper VII).

the target; the other, with a smaller gap, is turned on when close to the target.This solution would increase the positioning range.

For the heading, in Phase 1, the average heading estimation error is approx-imately 2.3◦, and the dispersion 1.6◦. The reference values vary a lot, whichcould question the validity of the comparison. The resolution of the motiontracking system is not known and might be lower than the studied positioningsystem. In phase 2, there is a large difference between the errors measured atthe two first distances, and the ones measured at 1.15 m (red and green com-pared to blue colours). The reason is that for large angles, one laser beamprojection is very close to the image center. At this distance, a small variationin pixels can cause large errors in the angle calculation, and the further awayfrom the target, the larger the consequences. Finally, we note, at least for the

55

red and green graphs, that the MAE is higher for positive heading angles thanfor negative ones, despite the intervals’ symmetry. This is due to the lack ofbrightness in one laser compared to the other during the experiment. For pos-itive headings, the brightness difference between the lasers widens even morethan for negative headings leading to a worse image processing.

In the experiment, the pictures used are 240*320 pixels in size. The distanceof a laser line to the image center thus varies from 1 to 160 pixels in integervalues, leading to a precision of 1.2 mm at about 0.35 m, and up to 3.9 cmat 1.3 m from the target. Moreover, at a distance of 1.21 m, a variation of1 pixel in the laser line detection has a repercussion of roughly 20◦ on theheading angle. Multiplying the camera resolution by two could divide theselarge potential errors by two.

4.4.2 Closed-loop simulationFrom Paper VIII, the simulation output is summarized in Figures 4.10a and4.10b. The graphs show that the vehicle goes through the four steps of the pathin approximately 45 s. The spikes in the thrust and position measurements aredue to the experimental measurements feeding the simulation. For the thrust,those spikes are around 1 N large for the forward thrust, and 0.15 N m for theyaw moment, which is low compared to the maximum values the BlueROVcan reach: ±2.1 N m in yaw torque, and [−18.7;28.4] N in x-axis thrust. Thepropulsion model saturates the thrust input beyond those values, which can beseen in Figure 4.10b.

The graph in Figure 4.10a shows an accuracy of 2 cm with maximum spikesof 2 cm in distance, and 2◦ with spikes of maximum 3◦ in heading. Theaccuracy needed for a docking, according to ROV operators and as stated inPaper III, is approximately 2 cm in distance and 5◦ in heading. It means thateither the laser-based positioning system or the BlueROV model could still beimproved to reach those objectives in accuracy.

Figures 4.10a and 4.10b show that the positioning system can be integratedto an autopilot for autonomous docking. The fusion of the real measurementswith the model output allows an accurate and smooth control of the distanceand heading during the path following. Limitations in availability of experi-mental data prevents from constructing different and more challenging paths.Therefore the closed-loop was tested only within the range of the laboratoryexperiment.

The model proposed is based on the control of the variables d and ψ . Thetraditional control based on x, y, and ψ , contains more information. In ourcase the y parameter is computed via the BlueROV model, but is not mergedto any sensor measurement. The positioning system must be further developedin order to detect the displacements in sway of the ROV.

56

(a)

(b)

Figure 4.10. Closed-loop simulation based on path-following, (a) Position measure-ment, and (b) Thrust response (Paper VIII).

4.5 ConclusionThe laser-based optical positioning system presented in this chapter was eval-uated through experimental tests performed in laboratory conditions. An au-topilot model for autonomous docking was designed and tested using the po-sitioning data from the open-loop experiment. The following conclusions canbe drawn:

Due to its small size, this positioning system has a promising value to beused in observation-class ROVs with little available onboard space. It is possi-

57

ble to implement this system in a small computer because it features a light im-age processing capacity compared to other vision-based positioning systems.The hardware itself is of small dimension (< 10×10×10 cm3). Finally, withan overall cost of less than 250 $, this sensor package has the potential to beused in small ROVs where the need for affordable sensors is significant.

The laser-based positioning system can be used for relative positioning in arange of [0.3;1.8] m. In distance, the MAE decreases with the distance untila range of approximately 0.5 m, where it starts rising again. In heading, theMAE increases when the vehicle’s heading gets larger. Sensor fusion withIMU data, covered in Paper VIII, would lead to an increased accuracy of thepositioning measurement.

From a hardware perspective, the positioning sensor could gain in perfor-mance with the installation of two sets of lasers, used separately depending onthe distance. Another solution to reduce errors is the use of a higher cameraresolution. Multiplied by two, potential errors could decrease from 4 to 2 cmin distance, and from 20 to 10 ◦ in heading at far distance.

The autopilot model shows accurate path-following merging the BlueROVmodel, an inner-loop controller, and experimental positioning measurements.The control provides smooth thrust inputs. However, more simulations couldbe done using additional measurement data, and where the path to follow ismore challenging.

58

5. Conclusion

This chapter summarizes the most important conclusions of the research workpresented in this thesis, structured around the three research topics. Pleasenote that more elaborate conclusions are given within each topic.

Forecasting and variability of wave energy

The research question raised for this theme was: How to better forecast waveenergy and its variability together with other intermittent renewable energiesin order to enable a fully renewable power grid?• The use of a single metric for the forecasting of wind, solar, and wave energy,

would allow to compare the forecasting skills of the different resources.• The construction of a joint persistent model applicable to all the variable

energy sources would enable the use of a single approach in the forecastingof those different sources.

Automation of underwater operations on wave energy converters

The research question asked for this theme was: How to lower the expensesrelated to underwater operations on wave energy converters?• The development of middle-size ROVs with low-cost sensors would allow

a reduction of the deployment and maintenance costs, especially for largeparks of at least fifteen WECs.

• The automation of underwater operations using small ROVs requires the de-velopment of supporting tools and supplementary equipment, such as ROV-friendly shackles, slings, specific electrical connectors, and cable drums.

• The use of a docking system for ROV would enable autonomous cable con-nections at the substation.

Laser-based positioning system

The research question posed for this theme was: What tools can enable theuse of low-cost ROVs to perform underwater cable connections?• Autonomous docking would enable a less experienced ROV operator to-

gether with a small underwater vehicle to carry out operations that requireforce and precision. Cable connection tasks are typical missions that wouldprofit from such tool.

• A laser-based positioning system could enable ROV docking at low cost.• Positioning based on vision has a better accuracy performance than acous-

tic sensors for short range relative positioning. It allows operations nearbystructures such as inspection, shackles removal, or cable connection.

59

6. Future work

This section provides a short introduction to upcoming challenges in both theautomation of underwater operations on wave energy converters, and in un-derwater relative positioning based on image processing.

Fully automated WEC deployment

The method proposed to deploy a UU WEC using ROVs only has not beenentirely tested. The natural step forward is to prove the feasibility of thismethod in a real environment and to validate the results that were theoreticallyevaluated in this thesis.

The development of a ROV-friendly shackle started during this thesis, andshould be mechanically improved and finalized.

The ROV specifically built for the Lysekil project should be completed andtested at the LRS.

The design of a gripper for cable connection started with two different pro-totypes. It should be continued as it is a promising tool for many underwaterapplications.

Underwater positioning based on vision

Vision has great potential for underwater relative positioning. Optical sensorsare a promising add-on to acoustic systems due to their short-range accuracy.The presented laser-based positioning sensor merits deeper development inboth hardware and software.

The image processing algorithm could be further developed with the use ofa filter based on a quadratic function (instead of linear in this thesis).

The operating range and accuracy of the tool could be enhanced by usingtwo sets of lasers with a different spacing.

Finally, the docking autopilot should be implemented and tested in labora-tory conditions and at sea.

60

7. Svensk sammanfattning

Avdelning for elektricitetslara vid Uppsala universitet har i femton ar utveck-lat en vagenergiomvandlare som bestar utav en boj och en linjar generatorplacerad pa havsbotten. Flera vagkraftverk ar kopplade till ett marint bot-tenstallverk vars roll ar att jamna ut effekten fran de olika aggregaten och hojaspanningsnivan till den som kravs i natet.

En stor utmaning i projektet ar att sanka kostnaderna for sjosattning ochunderhall av vagkraftverk. Hittills utfors de operationerna av dykare, vilket arbade dyrbart och osakert. En mojlighet ar att ersatta dykare med automa-tiserade losningar, genom att anvanda sma undervattensrobotar. Den dok-torsavhandlingen foreslar en metod for att gradvis sjosatta och underhallavagkraftverken utan mansklig undervattensaktivitet.

Undervattensrobotar (ROVar) kraver anpassande verktyg och utrustning foratt kunna fullgora sadant arbete som kraver bade kraft och handfardighet.Typiska uppgifter ar inspektion, shackel, sling, och slang avflyttning, ven-tilforslutning, kabelkontaktering. Den sistnamnda kraver sarskild precisionfor att slutforas: 5 mm i avstand, och 5◦ i vinkel. Dessutom innebar opera-tionen en kraft upp till 200 N. Den noggranheten tillsammans med nodvandigkraft nas inte av dagens robotar. Den har doktorsavhandlingen presenterar ettpositioneringssystem for ROV sa att farkosten kan autonomt uppna onskadposition infor kontaktering.

Positioneringssystemet bestar utav tva grona lasrar och en monokular kam-era, och baseras pa bildbehandling. Det implementerades i en ROV och tes-tades i vattentank. Experimentella resultat fran testerna visar att genomsnit-tliga absoluta avvikelsen (mean absolute error-vardet) kan minska ner till 6mm for ett avstand pa 0.7 m mellan farkost och mal, och en noggranhet i vinkelav 2◦. Berakningstid for bildbehandling ar pa 13.6 ms, vilket ger mojlighet aven 30 Hz matning frekvens. Tillsammans med ett closed-loop styrsystem,autonomt path-following kan erhallas. Den har doktorsavhandlingen framforsimuleringarna som bevisar mojligheten for det positioneringssystemet att im-plementeras i en autopilot for ROV autonomt dockning.

61

8. Resume en francais

Les appareils houlomoteurs (WECs) sont des machines qui produisent de l’electricitea partir des vagues oceaniques. Il en existe de nombreux concepts, dont l’und’eux est developpe par la division pour l’electricite a l’universite de Uppsalaen Suede. Cet appareil est compose d’une bouee flottant a la surface de l’eau,laquelle est reliee par une ligne a un generateur lineaire a aimants permanents,qui repose sur le fond marin. Avec le mouvement des vagues, la bouee sedeplace verticalement, entrainant avec elle le generateur qui, ainsi, produit uncourant electrique.

Le WEC elabore a Uppsala est de petite dimension. Par consequent, ungroupe d’appareils houlomoteurs est necessaire a la production d’une quan-tite suffisante d’energie. Les machines sont connectees a une sous-stationmarine, elle aussi localisee sous l’eau. Cette sous-station recupere et fu-sionne l’electricite produite par les differentes machines, puis envoie ensuitele courant obtenu au reseau electrique par un unique cable.

A ce jour, le deploiement de tous ces appareils est fait par des plongeurs,mais ces operations sont couteuses et dangereuses pour la securite humaine.Cette these propose une methode qui rend possible l’utilisation des robotssous-marins (ROVs) a la place de plongeurs pour effectuer ces operationssous-marines.

Les missions recurrentes sont les suivantes: fermer des valves, retirer destubes de pressurisation, enlever des courroies, et oter ou installer des manilles.Ces actions necessitent de developper des equipements speciaux pour ROV.Une autre operation consiste a connecter les cables electriques entre les WECset la sous-station. Cette tache est complexe car elle demande a la fois dela force (environ 200 N) et de la precision (5 mm pour l’alignement des con-necteurs). Les ROVs disponibles sur le marche aujourd’hui ne repondent pas acette demande. Une solution est de developper un systeme d’amarrage auquels’accroche le vehicule pour etre automatiquement aligne avec le point de con-nexion. Cela lui permet aussi de prendre une force d’appui sur cette struc-ture plutot que provenant de la poussee de propulsion. Cependant, diriger unrobot sous-marin a l’entour d’un WEC est complique, surtout en raison de lapresence de courants, et du possible manque de pratique de l’operateur. Il estdonc souhaitable d’automatiser cette operation d’amarrage.

Le deplacement autonome d’un vehicule sous l’eau n’est pas simple carles GPS ne fonctionnent pas em milieu sous-marin. Le robot doit pouvoirse localiser a l’aide d’autres outils. Cette these decrit le developpement d’unsysteme de positionnement base sur le traitement d’images. Le systeme est

62

compose d’une camera et de deux lasers projetant leur faisceau sur la structure-cible. Les images sont analysees et traitees pour en deduire la position du ROVpar rapport a cette structure. Le systeme a ete teste en bassin et montre desresultats interessants, avec une precision en distance de 6 mm at 0.7 m dela cible, et de 2◦ en angle. Les mesures ont ete integrees a un autopilote ensimulation, ce qui montre qu’il est theoriquement possible d’utiliser cet outilpour un amarrage autonome par un robot sous-marin.

63

9. Acknowledgment

I would like to acknowledge the J. Gust. Richert foundation, StandUP for En-ergy, Smalands nation, Aforsk, as well as the Marie Curie OceaNET program,for funding my research, conference travels, and training.

Jens Engstrom, det har varit spannande att ha dig som huvudhandledare.Tack for den frihet som du gett mig i min forskning, for ditt fortroende (aveni de mest kritiska tiderna :), och for att du alltid stottat mig.

Johan Abrahamsson, det var mycket givande att ha dig som bitradande han-dledare. Fran dig har jag lart mig det viktigaste: att alltid vara glad, och aldrigge upp!

Mats Leijon, jag vill tacka dig for att du valkomnade mig till avdelningen.Den forskningsgrupp jag fick vara med ar ambitios och framgangsrik tack varedig och ditt stod.

My dear office mates Victor, Francisco, Aya, Dalina, Simon, Liguo, I willnever forget those years spent with you, you are awesome!

Thanks also to Tobias, Andre, Eduard, Anke, Kaspars, Johan F., Muzafar,Nasir, for your friendship in- and outside work.

To all people who were part of the Lysekil trips, the conferences in Aalborg,Hawai, Singapore, New Orleans, etc., co-authorships, the division choir, theAW beers, a big thank you for all those unforgettable memories.

The OceaNET people, I had such a great time with you in Portugal, Spain,France, Germany, Netherlands, England, Ireland, Sweden! Nathalie, Michele,Boris, Yannick, Simon, FF, Mammad, Nicolas, Giovanni, Juan Carlos, FX,Cesc, thank you so much for making OceaNET an amazing team!

My gratitue also goes to the financial and IT people Maria Nordengren,Lena Eliasson, and Thomas Gotschl. You were of great support and made myjob so much easier here at Angstrom.

To all the bachelor and master students who helped me during this project:Johan, Ben, Sylvain, Sophie, and Hector, it was great collaborating with you!I learnt a lot from you and I would not be here today without your help.

I would like to thank the two research teams I visited during my PhD stud-ies: Antonio, Luis, Miguel, and of course Filipa and Stephanie from IST, Lis-boa; Roberto, Adriana, and Andreas from DTU, Copenhagen. Special thanksto you, Roberto, you taught me so much in such a short time! The six monthsof Thursday Skype-meetings with you show how dedicated and supportive youare. Many thanks!

64

J’aimerais remercier ma famille Remouit et belle-famille Garcia-Mouı-Pasquet:mes parents et beaux-parents, mes tres cheres soeurs et belles-soeurs Hort-ense, Pauline, Charlotte, Marie-Alex, Louise-Marie, Johanna et Emilie, mesbeaux-freres Frederic, Stephane, David, Joris, Philippe, Geoffroy, Yussef, etJames, je vous remercie pour tous ces beaux moments de vie vecus ensemble.J’espere qu’il y en aura de nombreux autres!

Enfin, je remercie ma nouvelle famille Garcia-Remouit qui fait mon plusgrand bonheur. Vincent, la fin de cette etape annonce plein de nouvelles aven-tures. J’espere que nous saurons les vivre avec autant d’energie que ces cinqdernieres annees!!

65

10. Summary of papers

This work is based on eight papers, presented in their full length at the end ofthe thesis. A short summary of each paper is described in this section.

Paper I

Wave Energy Research at Uppsala University and the Lysekil Research

Site, Sweden: A Status Update

The paper gives a status update of the Lysekil Project. After introducing theproject, the test site, and the wave power concept, it describes the develop-ment of the different WECs and their performances, the different types ofbuoys studied and tested. The substation and measuring station updates arepresented. In a 5th part the ROV project is introduced as well as a Radio Fre-quency Identification (RFID) tool to recognize the connectors underwater andtheir position at the substation. Some studies regarding the modelling of wavepower farms, survivability in extreme conditions, as well as an environmentalanalysis, are presented. Finally an update on the grid connection is given inthe last section.

The author participated in the writing of the part on ROV trials and on theRFID tool.

Paper II

Variability assessment and forecasting of renewables: A review for solar,

wind, wave and tidal resources

The paper is a review of the different renewable resources sun, wind, tide, andwave, in order to find variables that are shared in the forecasting of the sourcesand to assess their variability. Models and metrics are compared between thedifferent sources and discussed.

The author took part in everything that concerns wave power, with a specialfocus on the forecasting section.

Paper III

66

Automation of subsea connection for clusters of wave energy converters

The paper discusses the possibility of performing a cable connection using anobservation-class ROV. The first section describes the connection procedure atthe Lysekil substation. In a second part it presents the first tank trials that wereconducted to perform an electrical subsea connection with an Observation-Class ROV. Docking tests were also carried out and are summarized in the lastpart.

The author did not take part in the trials. She drew the conclusions of thetests, and wrote the paper.

Paper IV

Review of Electrical connectors for underwater applications

The paper is a review on electrical connectors for underwater applications.It extensively reviews the commercially available products and some morespecial designs. It covers the theory needed in mechanics, thermodynamics,material sciences, to design a suitable connector depending on the application.Finally, the paper analyses the deployment of underwater cables and their at-tached connectors, and discusses the possibilities to enhance the availabilityof low cost connectors, and to create more ROV-friendly products in order toautomate electrical subsea connections.

The author performed most of the work in this paper, with inputs fromcoauthors.

Paper V

Deployment and maintenance of Wave Energy Converters at the Lysekil

research site: a comparative study on the use of divers and Remotely

Operated Vehicles

This paper is a case study on the deployment and maintenance of the WECsdeveloped at Uppsala University. The goal of the paper is to propose solu-tions in order to gradually automate the deployment and maintenance of theWECs. It compares three different methods used for underwater operations onthe devices: one uses divers only, the second is a combination of divers- andROVs-conducted procedures, and the last one is a fully ROV-assisted method.The three methods are compared in terms of costs and operational time.

The author collaborated together in all the parts of the paper with two otherco-authors, with inputs from remaining co-authors. She had a specific respon-sibility for everything covering ROVs, and for the comparative study.

67

Paper VI

Optical System for Underwater Positioning of Observation Class

Remotely Operated Vehicle

The paper presents a positioning system for ROV, based on laser and vision,with experimental results from tests in air. The positioning accuracy is dis-cussed, as well as the issues related to light scattering, and the non parallelismbetween the lasers.

The author developed the positioning system, performed the tests, and wrotethe paper, with inputs from co-authors.

Paper VII

Laser-based relative positioning system for underwater remotely

operated vehicle

The paper presents the development, testing, and verification of a relative po-sitioning system for ROV, based on laser and vision. The position and ori-entation of the vehicle relative to a plane based target is estimated througha laser-based optical system and image processing. Experimental results areobtained from tank tests at the Danish Technical University.

The author developed the positioning system, the vision processing algo-rithm, implemented the sensor package into the ROV, performed the test, andwrote the paper with inputs from co-authors.

Paper VIII

Closed loop analysis of an optical relative positioning system for

remotely operated vehicles

The manuscript presents a closed loop control system based on the positioningsystem developed in Paper VII. It includes a path-following unit, a sensor fu-sion of the laser-based positioning system together with data from the InertialMeasurement Unit in the ROV, a Kalman filter estimator, and a PI controller.The closed-loop was tested in Matlab Simulink.

The author performed most of the work and wrote the manuscript, withinputs from co-authors.

68

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