HullBug: Autonomous Hull Grooming Student Name(s): Gregoire Caubel and Anthony Donatelli Mentor Name(s): Prof. Brendan Englot HOMELAND SECURITY CHALLENGE APPROACH / METHODOLOGY OUTCOMES / RESULTS CONCLUSION ACKNOWLEDGEMENTS This material is based upon work supported by the U.S. Department of Homeland Security under Cooperative Agreement No. 2014-ST-061-ML0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. The research performed during the 2018 Summer Research Institute leverages a Stevens Institute of Technology project being conducted in collaboration with SeaRobotics. The goal of the project was to determine a suite of sensors with which the HullBug, an unmanned underwater vehicle, can be adapted for enhanced autonomy and ship hull grooming capabilities. Currently available on the market, the HullBug is used for removing biofouling from the hulls of ships. With enhanced autonomy however, the hull cleaning robot has immense potential to drastically reduce the fuel consumption and carbon emissions of the U.S. military’s fleet of vessels (e.g., Navy, Coast Guard), as well as those for the private maritime industry (e.g., cargo ships, cruise ships). The sensors reviewed by the student team were chosen in conjunction with SeaRobotics for a variety of reasons, including size, cost, and their given performance. Decisions to implement the sensors were made using a state-space representation of the HullBug to determine the overall random walk that the robot would experience based on the drift rates of its sensors, as well as from a thorough review of peer reviewed research papers on autonomous robotics. - Matlab simulation based off state-space model of generic differential drive robot, using randomized input vector to simulation drift from dead reckon sensors. - Matlab Simulation shows that over 150 m (10 min of operation) the robot drifts only 2m (~1.3% off desired path. This result validates the sensor suite choices, the next step is to do real world testing of the HullBug with the aforementioned sensor suite. - The lawnmower path simulation on left is the characteristic motion for the HullBug. After numerous simulations, it was shown that the Hullbug only deviated from its final destination by less than 2m on each 200m simulation Figures 1,2,&3 (Drift of HullBug under straight lined motion, auto scale and forced scaling) (Drift of HullBug along Lawnmower bath) ALL FIGURES DEPICT MULTIPLE SIMULATIONS ON SAME PLOT To best determine how to convert the HullBug into a fully autonomous tool we used the following approach - Market Research on COTS sensors - Academic research to find justification for our choices - Computer Simulations Sensor Suite: - Oculus 750d Multibeam Sonar (Top Left) - Low Price for High Resolution - Experience using it on BlueROV Project, Already proven its ability - Small Size and Lightweight - KVH1750 Fiber Optic Gyro (Bottom Middle) - Single Axis for Heading - Small Size and Lightweight - .05 deg/hr drift - Nortek DVL1000 (Bottom Left) - Small Size and Lightweight - .2m Standoff, one of the only available on market This PHASE I paper study completed during the 2018 MSC SRI, determined the dead reckoning sensors necessary to give the HullBug fully autonomous hull grooming capabilities. The sensor choices were then validated using a state-space MATLAB model to demonstrate their accuracy and project reliability. The next steps for this research is to preform real world testing, as well as implementing feature recognition software using the Multibeam sonar to fully equip the Hullbug with navigation capabilities. This research was completed during the 2018 Maritime Security Center’s SRI, hosted by Stevens Institue of Technology. Special thanks to Professor Englot, Beth DeFares, Professor Bunin, and the Team at SeaRobotics.

HullBug: Autonomous Hull Grooming · autonomy however, the hull cleaning robot has immense potential to drastically reduce the fuel consumption and carbon emissions of the U.S. military’s

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HullBug: Autonomous Hull Grooming

Student Name(s): Gregoire Caubel and Anthony DonatelliMentor Name(s): Prof. Brendan Englot

HOMELAND SECURITY CHALLENGE

APPROACH / METHODOLOGY

OUTCOMES / RESULTS

CONCLUSION

ACKNOWLEDGEMENTS

This material is based upon work supported by the U.S. Department of Homeland Security under Cooperative Agreement No. 2014-ST-061-ML0001. The viewsand conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, eitherexpressed or implied, of the U.S. Department of Homeland Security.

Theresearchperformedduringthe2018SummerResearchInstituteleveragesaStevensInstituteofTechnologyprojectbeingconductedincollaborationwithSeaRobotics.ThegoaloftheprojectwastodetermineasuiteofsensorswithwhichtheHullBug,anunmannedunderwatervehicle,canbeadaptedforenhancedautonomyandshiphullgroomingcapabilities.Currentlyavailableonthemarket,theHullBug isusedforremovingbiofoulingfromthehullsofships.Withenhancedautonomyhowever,thehullcleaningrobothasimmensepotentialtodrasticallyreducethefuelconsumptionandcarbonemissionsoftheU.S.military’sfleetofvessels(e.g.,Navy,CoastGuard),aswellasthosefortheprivatemaritimeindustry(e.g.,cargoships,cruiseships).

ThesensorsreviewedbythestudentteamwerechoseninconjunctionwithSeaRobotics foravarietyofreasons,includingsize,cost,andtheirgivenperformance.Decisionstoimplementthesensorsweremadeusingastate-spacerepresentationoftheHullBug todeterminetheoverallrandomwalkthattherobotwouldexperiencebasedonthedriftratesofitssensors,aswellasfromathoroughreviewofpeerreviewedresearchpapersonautonomousrobotics.

- Matlab simulationbasedoffstate-spacemodelofgenericdifferentialdriverobot,usingrandomizedinputvectortosimulationdriftfromdeadreckonsensors.- Matlab Simulationshowsthatover150m(10minofoperation)therobotdrifts

only2m(~1.3%offdesiredpath.Thisresultvalidatesthesensorsuitechoices,thenextstepistodorealworldtestingoftheHullBugwiththeaforementionedsensorsuite.

- ThelawnmowerpathsimulationonleftisthecharacteristicmotionfortheHullBug.Afternumeroussimulations,itwasshownthattheHullbug onlydeviatedfromitsfinaldestinationbylessthan2moneach200msimulation

Figures1,2,&3(DriftofHullBugunderstraightlinedmotion,autoscaleandforcedscaling)(DriftofHullBugalongLawnmowerbath)ALLFIGURESDEPICTMULTIPLESIMULATIONSONSAMEPLOT

TobestdeterminehowtoconverttheHullBugintoafullyautonomoustoolweusedthefollowingapproach- MarketResearchonCOTSsensors- Academicresearchtofindjustificationforourchoices- ComputerSimulations

SensorSuite:- Oculus750dMultibeamSonar(TopLeft)

- LowPriceforHighResolution- ExperienceusingitonBlueROV Project,Alreadyprovenitsability

- SmallSizeandLightweight- KVH1750FiberOpticGyro(BottomMiddle)

- SingleAxisforHeading- SmallSizeandLightweight- .05deg/hr drift

- NortekDVL1000(BottomLeft)- SmallSizeandLightweight- .2mStandoff,oneoftheonlyavailableonmarket

ThisPHASEIpaperstudycompletedduringthe2018MSCSRI,determinedthedeadreckoningsensorsnecessarytogivetheHullBugfullyautonomoushullgroomingcapabilities.Thesensorchoiceswerethenvalidatedusingastate-spaceMATLABmodeltodemonstratetheiraccuracyand projectreliability.Thenextstepsforthisresearchistopreformrealworldtesting,aswellasimplementingfeaturerecognitionsoftwareusingthe MultibeamsonartofullyequiptheHullbug withnavigationcapabilities.

Thisresearchwascompletedduringthe2018MaritimeSecurityCenter’sSRI,hostedbyStevensInstitue ofTechnology.SpecialthankstoProfessorEnglot,BethDeFares,ProfessorBunin,andtheTeamatSeaRobotics.