Unmanned Ground Vehicles
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Transcript of Unmanned Ground Vehicles
-PIRATLA ADITYA
INDUSTRIAL POWER & AUTOMATIONDEPARTMENT OF ELICTRICAL ENGINEERING
NIT CALICUT
vINTRODUCTION.
vMOBILITY.
vLOCALIZATION.
vNAVIGATION.
CONTENTSCONTENTS
vNAVIGATION.
vCOMMUNICATION.
vPLANNING.
vINTERFACES.
vAPPLICATIONS.
vARMED UGV.
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INTRODUCTIONINTRODUCTION
vUGV: UNMANNED GROUND VEHICLESv Durrant-Whyte(2001) divided UGV technology into ‘5’
categories(1):
1. Localization.2. Mobility.3. Navigation.4. Planning.5. Communication.
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PLANNING
COMMUNICATION
LOCALIZATION NAVIGATION
Prior Map, goals,values
External sensors
Internal External
environmental sensors
MOBILITY
LOCALIZATION NAVIGATION
Vehicle
Internal sensors sensors
FIG1:Relationships Between components of UGV(Durrant-whyte) (1)42/17/2011
• Definition :methods through which robot updates or calculates position.(2)
LOCALIZATIONLOCALIZATION
Localization
Absolute Relative Absolute Relative
Landmark navigation
Map matching
Inertial navigation
Odometry
TOF active ranging
Active beacons
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vOptical absolute shaft ENCODER used in ODOMETRY
v81000 pulses per revolution.v 0.01µm resolution.
v GRAY CODE.
vRelative Localization: Onboard sensors like Encoders, Gyroscopes, accelerometers are used.
(3)
tpub.com
vRATE GYRO (4),(5) used in INERTIAL NAVIGATION.
vConservation of ANGULAR MOMENTUM.
vGyro fixed to vehicle frame.
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v Rotation along input axis resisted.
v Precession calibrated foroutput.
tpub.com
Pulse counting ccelerometers
vACCELEROMETERS (4),(5) used foracceleration measurement.
v Newton’s 2nd law of motion.vRELATIVE LOCALIZATION sensors
Use integration to compute position.
tpub.com
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vDRIFT in measured data.vShort durations.
vAbsolute Localization (2): Done using beacons, landmarks or maps.
vNo integration for Localization.
vActive Beacons like satellite send signals.vTriangulation & trilateration 2 commonly used
beacon techniques.vExcept location of beacon no a-priori information
required.
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vAccuracy of GPS about 1-4m.vAtmospheric & Geographic disturbance may influence signal
transmission.vAbsence of signal.v Active Beacons are Costly.
vLandmarks are fixed objects, either natural or artificial.vSonar, infrared sensing & vision sensing.vVehicle can be preloaded with landmark information.vOr may use self learning tools.
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vNoise & uncertainty in sensor reading.vOptimal use of sensed data critical.vProbabilistic BAYESIAN ESTIMATION (6) can be used
to account for uncertainty.vBayesian estimation characterized by BELIEF.vMarkov environment assumed.vHigh dimensional sensor
reading mapped to lower dimension.
vPrior & Posterior belief.10
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vMap Matching Positioning.vGlobal map matched with local maps.vMainly used in indoor mapping.
vTOF active ranging.vTOF active ranging.vUltrasonic, RF, Optical energy sources used.vLinear operation.vAccurate as long reliable echo obtained.
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vMobility (7) deals with vehicles mechanical design.
vNATO Reference Mobility Model (NRMM).vBecker Mobility Model
MOBILITYMOBILITY
vBecker Mobility ModelvMotion control technique.vVehicle-Terrain interaction.vAnalysis through principle of conservation of
energy.
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NAVIGATIONNAVIGATION
cognitionlocalization
Prior knowledge
Operator commands
Motion control
ugv
perception
Navigation control loop(2)132/17/2011
vNavigation(8)uses environment map from Localization.
vMotion strategy.vAccuracy should be in tune with vehicle
dimensions.
NAVIGATION
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NAVIGATION
PERSONALLOCALGLOBAL
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vWireless Communication.vMulti Path Fading.vReal Time Data Transfer.
COMMUNICATIONCOMMUNICATION
vReal Time Data Transfer.vData Compression.v>10 Kbps BW.
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vAd Hoc Networking(9):i. Mobility.ii. Self Organizing.iii. Multi Hooping.iv. Energy Conservation.v. Scalability.
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vi. Security.vii. Connection to the internet.
a) Hybrid Wireless Networksb) Opportunistically
extendable networks.
vInput from all other blocks and Peripherals.
vGlobal Path Planning(10).
PLANNINGPLANNING
vGlobal Path Planning(10).vMotion Planning.vNon-holonomic Path Planning.vKino-dynamic Path Planning.
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vOutdoor Planning.vIndoor Planning.vAttributes of Planning:
a) Search space.b) Configuration space.c) Actions.
vCell decomposition.2/17/2011 18
c) Actions.d) Time.e) Initial state, Goal state.f) Constraints.g) Criteria.h) Algorithm.
APPLICATIONSAPPLICATIONSvIN EXPLORATION OF INHOSPITABLE TERRITORY.
vAUTOMATED TRANSPORTATION.
vDEFENSE PURPOSE. 192/17/2011
REFERENCESREFERENCES
1. Thanh Hung Tran P.H.D thesis on modeling and control of unmanned
ground vehicles, Sep-2007.2. Dr. Georgios A. Demetriou , A Survey of Sensors for Localization of
Unmanned Ground Vehicles (UGVs).3. Wikipedia.org.
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4. Integrated Publishing, tpub.com.5. Measurement Systems, Ernest O Doebelin, McGraw Hill international
edition.6. Sebastian Thrun, Bayesian Landmark Learning for Mobile Robot
Localization. 1997.7. HyukChul Nho, Jonathan Salton, Barry Spletzer, Scott Horschel, Mobility
Analysis of Robotic Ground Vehicles Based on Energy Method.
8. Jonathan Dixon, Oliver Henlich, final report, Mobile Robot Navigation1997
9. Ad Hoc Networks Technologies and Protocols, Edited by PrasantMohapatra University of California‚ Davis Srikanth V. KrishnamurthyUniversity of California‚ Riverside.
10. J. Giesbrecht, Defence R&D Canada – SuffieldGlobal Path Planning for Unmanned Ground Vehicles.
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-THANK YOU-THANK YOU
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