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简介：Recentresultsonthedevelopmentofanavigationsystemforasmartwheelchairarepresentedinthispaper.Inordertoreducethedevelopmentcost,amodularsolutionisdesignedbyusingcommercialandlowcostdevices.Thefunctionalitiesofthetrackingcontrolsystemaredescribed.Experimentalresultsoftheproposedassistivesystemarealsopresentedanddiscussed.

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简介：Inaerialrobots’visualnavigation,itisessentialyetverydiffculttodetecttheattitudeandpositionoftherobotsoperatedinrealtime.Byintroducinganewparametricmodel,theproblemcanbereducedfromalmostunmanageabletobepartlysolved,thoughnotfully,aspertherequirement.Inthisparametricapproach,amulti-scaleleastsquaremethodisformulatedfirst.Bypropagatingaswellasimprovingtheparametersdownfromlayertolayeroftheimagepyramid,anewglobalfeaturelinecanthenbedetectedtoparameterizetheattitudeoftherobots.Furthermore,thisapproachpavesthewayforsegmentingtheimageintodistinctparts,whichcanberealizedbydeployingaBayesianclassifieronthepicturecelllevel.ComparisonwiththeHoughtransformbasedmethodintermsofrobustnessandprecisionshowsthatthismulti-scaleleastsquarealgorithmisconsiderablymorerobusttonoises.Somediscussionsarealsogiven.

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简介：Toimprovethereliabilityandaccuracyoftheglobalpositioningsystem(GPS)/microelectromechanicalsystem(MEMS)inertialnavigationsystem(INS)integratednavigationsystem,thispaperproposestwodifferentmethods.Basedonwaveletthresholddenoisingandfunctionalcoefficientautoregressive(FAR)modeling,acombineddataprocessingmethodispresentedforMEMSinertialsensor,andGPSattitudeinformationisalsointroducedtoimprovetheestimationaccuracyofMEMSinertialsensorerrors.ThenthepositioningaccuracyduringGPSsignalshortoutageisenhanced.ToimprovethepositioningaccuracywhenaGPSsignalisblockedforlongtimeandsolvetheproblemofthetraditionaladaptiveneuro-fuzzyinferencesystem(ANFIS)methodwithpoordynamicadaptationandlargecalculationamount,aself-constructiveANFIS(SCANFIS)combinedwiththeextendedKalmanfilter(EKF)isproposedforMEMS-INSerrorsmodelingandpredicting.Experimentalroadtestresultsvalidatetheefficiencyoftheproposedmethods.

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简介：The‘FreedomofNavigation'issuehasbeenvolatileandofteninfluencedbySino-USrelationsinrecentyears.ThisproblemwasrootedindifferentinterpretationoftheUNCLOSbyChinaandtheUS.IthasalsoreflectedtheirdifferentnationalInterestsandmaritimestrategies.Thisissuehascausedfrequentfrictionsbetweenthetwosidesandmayleadtotheirfuturecontestforseapower.Atthesametime,italsourgesthebothsidestosetupnewmechanismforconfidencebuildingthatmayhelpthemtomanageitinamorerationalway.

简介：-BasedonthefeasibilitystudyofdevelopingthenavigationresourcesoftheGuanheRiverandthemodeltestresultsofthemouthbarregulation,thispaperpresentssomebasicprinciplesfortheregulationofthechannelonthemouthbar,forinstance,thedirectionofnavigationchannelshouldbeidenticalwiththatoftheebbtidecurrentandthemainwaves,andperpendiculartothebathymetriccontours.Theprinciplesforregulatingmouthbarsarealsodiscussedinthispaper.

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简介：Thecapabilityandreliabilityarecrucialcharacteristicsofmobilerobotswhilenavigatingincomplexenvironments.Theserobotsareexpectedtoperformmanyusefultaskswhichcanimprovethequalityoflifegreatly.Robotlocalizationanddecisionmakingarethemostimportantcognitiveprocessesduringnavigation.However,mostofthesealgorithmsarenotefficientandarechallengingtaskswhilerobotsnavigatethroughcomplexenvironments.Inthispaper,weproposeabiologicallyinspiredmethodforrobotdecision-making,basedonrat’sbrainsignals.Rodentsaccuratelyandrapidlynavigateincomplexspacesbylocalizingthemselvesinreferencetothesurroundingenvironmentallandmarks.Firstly,weanalyzedtherats’strategieswhilenavigatinginthecomplexY-maze,recordedlocalfieldpotentials(LFPs),simultaneously.TherecordedLFPswereprocessedanddifferentfeatureswereextractedwhichwereusedastheinputintheartificialneuralnetwork(ANN)topredicttherat’sdecision-makingineachjunction.TheANNperformancewastestedinarealrobotandgoodperformanceisachieved.Theimplementationofourmethodonarealrobot,demonstratesitsabilitiestoimitatetherat’sdecision-makingandintegratetheinternalstateswithexternalsensors,inordertoperformreliablenavigationincomplexmaze.

简介：-Thispaper,afterbrieflyreviewingtheexperimentalresearchonsedimenttransportonmuddybeachsincethe1950s,improvesandperfectsthemethodforforecastingsiltationinnavigationchannelsandharbourbasinswhichwasfirstputforwardinChinabytheauthors.Inconsiderationofsiltysedimentandsand,somefactorsinforecastingmethodshavebeenchangedandmodified.Consequently,themodifiedmethodscanbeusedeithertocomputesiltationinnavigationchannelsandharbourbasinsonmuddybeachortocomputesiltationandscouringinnavigationchannelsandharbourbasinsonbothsiltybeachandsandybeach.Theverificationoffielddatafromelevenlarge,mediumandsmallnaturalharboursshowsagoodagreementbetweentheforecastingbythemodifiedmethodandthenaturalconditions.Finally,thepaperdealswiththerationalutilizationofwaterareaaftertheconstructionoftheWestDykeinLianyungang,themaintenanceofwaterdepthofthenavigationchannelattheentrance,siltationdistribution,siltationinthenavigationchannelandharbourbasinforshipsof100thousandtonnnage.ResultsonceagainprovethattheprospectofconstructingLianyungangHarbourintoadeepwaterharbourisbright.

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简介：SeveralfiltertechniqueswereavailablefortheGPSpositionestimationproblemofmaneuveringvehiclerangingfromusingdifferentprocessnoisestoInteractiveMultipleModel(IMM).ThelimitationofusingstandardKalmanfiltersislisted.Theperformanceofproposedadaptivefilteriscomparedwiththatofthestandardones,twotypesofdynamicmodelingofthemaneuveringvehicleareused.ThesimulationisbasedonthealmanacdataoftheGPSsatellitestocomputeitsfeasibilityduringthesimulationtimeandpositiononshape8trackwithcontinuousvehiclemaneuvering.Thegoalistoobtaincomputationallyefficientfilterwithreasonableaccuracyforvehicleinmaneuveringsituation.ThefilterproposedisanalternativetothefilterproposedinRef.[1]withlowcomputationalburden.

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