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1、49thIEEEConferenceonDecisionandControlDecember15-17,2010HiltonAtlantaHotel,Atlanta,GA,USASensor-BasedRobotDeploymentAlgorithmsJeromeLeNyandGeorgeJ.PappasAbstractInrobotdeploymentproblems,thefunda-benefitofsuchgradientdescentalgorithmsisthattheymentalissueistooptimizeasteadystateperformancecaninf
2、actadapttheconfigurationtosloworinfrequentmeasurethatdependsonthespatialconfigurationofchangesintheenvironment.agroupofrobots.Forsuchproblems,aclassicalwayWenotethatasignificantpartoftheworkrelatedtoofdesigninghigh-levelfeedbackmotionplannersistoimplementagradientdescentschemeonasuitablychosenmult
3、i-robotdeploymentreliesonsuchgradientvectorobjectivefunction.Thiscanleadtocomputationallyex-fields.Thisincludesformationcontrolandflockingpensivealgorithmsthatmaynotbeadaptivetouncertain[5][9],coverage[2],[10]aswellascertainvehicledynamicenvironments.Weaddressthesechallengesbyroutingproblems[11],
4、orforagingandsourceseekingshowingthatalgorithmsforavarietyofdeployment[1],[12].Mostofthisworkassumesadeterministicscenariosinuncertainstochasticenvironmentsandwithnoisysensormeasurementscanbedesignedasstochasticoratleastknownmodeloftheenvironmentandne-gradientdescentalgorithms,andtheirconvergen
5、ceprop-glectsvarioussourcesofuncertaintythatcancomplicateertiesanalyzedviathetheoryofstochasticapproximations.implementationsandinvalidateconvergenceguarantees.ThisapproachyieldsoftensurprisinglysimplealgorithmsMorerecently,therehasbeensomeinterestinstochasticthatcanaccommodatecomplicatedobject
6、ivefunctions,deploymentscenariosinpartiallyunknownenviron-andworkwithoutadetailedmodeloftheenvironment.Toillustratetherichnessoftheframework,wediscussmentswithpossiblynoisymeasurements[1],[13][16].twoapplications,namelysourceseekingwithrealisticAnessentialideaofthispaperisthatmostofthesestochas
7、ticwirelessconnectivityconstraints,andcoveragestochasticdeploymentproblemscanbediscussedfromwithheterogeneoussensors.theunifyingpointofviewofstochasticgradientdescentI.INTRODUCTIONalgorithms,therebysimplifyingtheconvergenceproofsa
