Recommended
More Related Content
Featured
Featured (20)
OSGC Report
- 1. Development of Navigation and Path Planning Algorithms for UAVs for SAR Ops Student Researcher: Liberty MShockley Advisors: Manish Kumar, Ph.D. Kelly Cohen, Ph.D. Bryan Brown, Graduate Student University of Cincinnati Aerospace Engineering Abstract While UnmannedAerial Vehicles(UAVs) are gainingpopularityrecreationallyandcommercially,they are alsocreatingproblemsandforcingrestrictions bythe FAA.Thisandtheirhighcostis pushingthem fartherawayfrom practical usability.Forexample, firefightersusuallyhave alow budgetandare understaffed,butif theyhada UAV to surveyanarea on fire,orlookfora missingperson,itwould increase missionsuccessandefficiency.Firefightersneedalow-costUAV thatcan automaticallyconduct these searchmissions,safelyandwithease. A bigpartof the missionishow the UAV isgoingto fly, specifically,howtofindthe bestnavigationpathforfindingamissingperson. Thiswork explore the optimal pathand simulatedfourdifferentsearchpaths,agridpattern, a randomsearch, a five-point star, and a diamondshape.These shapesweredrawnbycreatinga systemof waypointsthe UAV would traverse.The optimal searchpathwasdeterminedthroughaseriesof Monte CarloSimulations,where minimizingthe time tofindthe targetwasthe primaryobjective.The study foundthe five-pointstaris the optimal searchpath forthese searchand rescue missionsforasquare searcharea. The star path can nowbe adapted forgreaterefficiency,orworkcan beginonimplementationtothe GroundStation. The Ground Station,completedwithnavigationalgorithms,human-detectingcameratechnology,andFAA approval,can begintobe marketedoutto communitiesforpublicservice. Project Objectives ThispaperdescribesmyprogressdevelopinganalgorithminMatLab that can automatically directaUAV to searchan area by creatinga systemof waypoints.The searchareaisdefinedasan arbitrary 10 kilometerby 10 kilometerplot,withthe centeractingasthe bestguessforthe locationof the target. The target representsamissingperson,walkingandwanderingaboutthe searcharea.The missionof the UAV isto findthe target.A missionissuccessful if the UAV findsthe targetinunder150 minutes. The optimal solutionisconstrainedbythistime parameter,andisfoundbyminimizingthe time,with concerngivento the successpercentage of 1000 simulations. Eachof the fourpaths underwentthese simulations,andthe statisticsof successcollected. Methodology The UAV has a scope of 10 metersby 10 meters,andthe simulationsare runwitha 90 metererror, allowingthe UAV toeffectively“see”100 metersby100 meters.Itisassumedthe UAV stays at a constantaltitude. The gridpathsearchesback andforth until the time isupor it findsthe target.The randomsearch movesinthe same manneras the target,beginninginthe middle.The starsearchmoves to the middle,and“draws”a small five pointstar,using waypoints,thenenlarges by140% and rotates aboutthe middle by30 degrees,andrepeatsuntil found. Afteranhourof searching,the star size shrinks to the original small star,andbeginsagain.Thisservestodouble-checkitdidnotmissthe targetin the centerof the searcharea. The diamondsearchbehavesexactlylike the starpattern,exceptwitha diamond shape insteadof astar.
- 2. Results Significance The grid and randomsearchpaths were testedtoshow theyare not optimal tomaximize speed. The diamondalgorithmshowsitcouldfindthe targetfaster,because there are lesslinesdrawn (diamondhasfoursides,starhas five lines) andcantherefore scanthe areamore quickly.The faster target locationcomesatthe cost of a reducedsuccessrate,diminishingoptimality. The starpattern outperformedotherpatternstestedwiththe highestoverall successrate. The average time of target locationwasreasonable andthe minimumtime totargetwasonpar withthe diamondpattern.The star patternismore thorough,withlessgapsinthe coveredarea,andthe waythe shape intersectsitself improvessuccessrate. Withanextremely hightimeconstraint,the successrate is100%. Figures Simulation_GRID Found Target! Time of Flight is 141 minutes Simulation_RANDOM Did not find Target Time of flight is 300 minutes Simulation_5PTSTAR Found Target! Time of Flight is 54 minutes Simulation_DIAMOND Found Target! Time of Flight is 84 minutes Acknowledgements I wouldlike tothankDr. KellyCohen,forgivingme the opportunity to explore this area of research and get involved with the SIERRA Team. It’s been an amazing experience so far, and has opened so many doors for me. Mr. Bryan Brown, the systems engineer of our team, introduced me to this project and showed me the introductory ropes of controls and path planning. Finally, I would like to share how much myparents,Dr. JeremiahShockleyandMrs.Leanne Shockley,have inspired andencouraged me in all of my endeavors.Theirguidance andcreative thinkinghelped me develop these unique algorithms. References USAF Combat SearchandRescue: UntappedCombat Power bydePalo, Lee K 2005, Maxwell paper, Volume no. 35; 20 May2015 Knowledge Engineering for ModellingReasoningina DiagnosisTask: Application to SearchandRescue CanadianJournal ofAdministrative Sciences Published Sept 2007 An Algorithmic Implementation ofConstrainedExtremal Control for UAVs EE BrighamYoungUniversity Optimal andEfficient Geolocation and Path Planningfor UnmannedAerialVehiclesUsingUncertaintyMeasures Sean R. Semper 1 September 2011 SARPlan:A decision support system for Canadian Search and Rescue Operations EuropeanJournal of Operational Research Published May2005 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Grid Path Simulation with Ten Second Iterations 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Grid Path Simulation with 10 Second Iterations 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Grid Path Simulation with Ten Second Iterations 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Grid Path Simulation with Ten Second Iterations