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Leader |
LDR
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cam a 00 |
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Control # |
1
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2010028053 |
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Control # Id |
3
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DLC |
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Date |
5
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20190911110850.0 |
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Fixed Data |
8
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100719s2011 maua b 001 0 eng |
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LC Card |
10
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$a 2010028053 |
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Tag 16 |
16
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7 |
$a015690249$2Uk |
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ISBN |
20
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$a9780262015356 (hardcover : alk. paper) |
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ISBN |
20
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$a0262015358 (hardcover : alk. paper) |
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Local Ctrl # |
35
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$a(OCoLC)ocn649700153 |
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Obsolete |
39
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$a295670$cTLC |
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Cat. Source |
40
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$aDLC$cDLC$dYDX$dYDXCP$dCDX$dINU$dUKMGB$dDLC |
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Authen. Ctr. |
42
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$apcc |
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LC Call |
50
|
00 |
$aTJ211.415$b.S54 2011 |
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Dewey Class |
82
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00 |
$a629.8/932$222 |
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ME:Pers Name |
100
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1 |
$aSiegwart, Roland. |
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Title |
245
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10 |
$aIntroduction to autonomous mobile robots /$cRoland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza. |
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Title:Varint |
246
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38 |
$aAutonomous mobile robots |
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Edition |
250
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$a2nd ed. |
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Imprint |
260
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$aCambridge, Mass. :$bMIT Press,$cc2011. |
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Phys Descrpt |
300
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$axvi, 453 p. :$bill. ;$c24 cm. |
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Series:Diff |
490
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0 |
$aIntelligent robotics and autonomous agents |
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Note:Bibliog |
504
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$aIncludes bibliographical references and index. |
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Note:Content |
505
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00 |
$g1.$tIntroduction --$g1.1.$tIntroduction --$g1.2.$tAn Overview of the Book --$g2.$tLocomotion --$g2.1.$tIntroduction --$g2.1.1.$tKey issues for locomotion --$g2.2.$tLegged Mobile Robots --$g2.2.1.$tLeg configurations and stability --$g2.2.2.$tConsideration of dynamics --$g2.2.3.$tExamples of legged robot locomotion --$g2.3.$tWheeled Mobile Robots --$g2.3.1.$tWheeled locomotion: The design space --$g2.3.2.$tWheeled locomotion: Case studies --$g2.4.$tAerial Mobile Robots --$g2.4.1.$tIntroduction --$g2.4.2.$tAircraft configurations --$g2.4.3.$tState of the art in autonomous VTOL --$g2.5.$tProblems. |
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Note:Content |
505
|
80 |
$g3.$tMobile Robot Kinematics --$g3.1.$tIntroduction --$g3.2.$tKinematic Models and Constraints --$g3.2.1.$tRepresenting robot position --$g3.2.2.$tForward kinematic models --$g3.2.3.$tWheel kinematic constraints --$g3.2.4.$tRobot kinematic constraints --$g3.2.5.$tExamples: Robot kinematic models and constraints$g3.3.$tMobile Robot Maneuverability --$g3.3.1.$tDegree of mobility --$g3.3.2.$tDegree of steerability --$g3.3.3.$tRobot maneuverability --$g3.4.$tMobile Robot Workspace --$g3.4.1.$tDegrees of freedom --$g3.4.2.$tHolonomic robots --$g3.4.3.$tPath and trajectory considerations --$g3.5.$tBeyond Basic Kinematics --$g3.6.$tMotion Control (Kinematic Control) --$g3.6.1.$tOpen loop control (trajectory-following) --$g3.6.2.$tFeedback control --$g3.7.$tProblems -- |
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Note:Content |
505
|
00 |
$g4.$tPerception --$g4.1.$tSensors for Mobile Robots --$g4.1.1.$tSensor classification --$g4.1.2.$tCharacterizing sensor performance --$g4.1.3.$tRepresenting uncertainty --$g4.1.4.$tWheel/motor sensors --$g4.1.5.$tHeading sensors --$g4.1.6.$tAccelerometers --$g4.1.7.$tInertial measurement unit (IMU) --$g4.1.8.$tGround beacons --$g4.1.9.$tActive ranging --$g4.1.10.$tMotion/speed sensors --$g4.1.11.$tVision sensors --$g4.2.$tFundamentals of Computer Vision --$g4.2.1.$tIntroduction --$g4.2.2.$tThe digital camera --$g4.2.3.$tImage formation --$g4.2.4.$tOmnidirectional cameras$g4.2.5.$tStructure from stereo --$g4.2.6.$tStructure from motion --$g4.2.7.$tMotion and optical flow --$g4.2.8.$tColor tracking --$g4.3.$tFundamentals of Image Processing --$g4.3.1.$tImage filtering --$g4.3.2.$tEdge detection --$g4.3.3.$tComputing image similarity --$g4.4.$tFeature Extraction --$g4.5.$tImage Feature Extraction: Interest Point Detectors --$g4.5.1.$tIntroduction --$g4.5.2.$tProperties of the ideal feature detector --$g4.5.3.$tCorner detectors --$g4.5.4.$tInvariance to photometric and geometric changes --$g4.5.5.$tBlob detectors --$g4.6.$tPlace Recognition --$g4.6.1.$tIntroduction --$g4.6.2.$tFrom bag of features to visual words --$g4.6.3.$tEfficient location recognition by using an inverted file --$g4.6.4.$tGeometric verification for robust place recognition --$g4.6.5.$tApplications --$g4.6.6.$tOther image representations for place recognition --$g4.7.$tFeature Extraction Based on Range Data (Laser, Ultrasonic) --$g4.7.1.$tLine fitting --$g4.7.2.$tSix line-extraction algorithms -- $g4.7.3.$tRange histogram features --$g4.7.4.$tExtracting other geometric features --$g4.8.$tProblems. |
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Note:Content |
505
|
00 |
$g5.$tMobile Robot Localization --$g5.1.$tIntroduction --$g5.2.$tThe Challenge of Localization: Noise and Aliasing --$g5.2.1.$tSensor noise --$g5.2.2.$tSensor aliasing --$g5.2.3.$tEffector noise --$g5.2.4.$tAn error model for odometric position estimation --$g5.3.$tTo Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions --$g5.4.$tBelief Representation --$g5.4.1.$tSingle-hypothesis belief --$g5.4.2.$tMultiple-hypothesis belief --$g5.5.$tMap Representation --$g5.5.1.$tContinuous representations --$g5.5.2.$tDecomposition strategies --$g5.5.3.$tState of the art: Current challenges in map representation --$g5.6.$tProbabilistic Map-Based Localization --$g5.6.1.$tIntroduction --$g5.6.2.$tThe robot localization problem --$g5.6.3.$tBasic concepts of probability theory --$g5.6.4.$tTerminology --$g5.6.5.$tThe ingredients of probabilistic map-based localization -- $g5.6.6.$tClassification of localization problems --$g5.6.7.$tMarkov localization --$g5.6.8.$tKalman filter localization --$g5.7.$tOther Examples of Localization Systems --$g5.7.1.$tLandmark-based navigation --$g5.7.2.$tGlobally unique localization --$g5.7.3.$tPositioning beacon systems --$g5.7.4.$tRoute-based localization --$g5.8.$tAutonomous Map Building --$g5.8.1.$tIntroduction --$g5.8.2.$tSLAM: The simultaneous localization and mapping problem --$g5.8.3.$tMathematical definition of SLAM --$g5.8.4.$tExtended Kalman Filter (EKF) SLAM --$g5.8.5.$tVisual SLAM with a single camera --$g5.8.6.$tDiscussion on EKF SLAM --$g5.8.7.$tGraph-based SLAM --$g5.8.8.$tParticle filter SLAM --$g5.8.9.$tOpen challenges in SLAM --$g5.8.10.$tOpen source SLAM software and other resources --$g5.9.$tProblems. |
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Note:Content |
505
|
00 |
$g6.$tPlanning and Navigation --$g6.1.$tIntroduction --$g6.2.$tCompetences for Navigation: Planning and Reacting --$g6.3.$tPath Planning --$g6.3.1.$tGraph search --$g6.3.2.$tPotential field path planning$g6.4.$tObstacle avoidance --$g6.4.1.$tBug algorithm --$g6.4.2.$tVector field histogram --$g6.4.3.$tThe bubble band technique --$g6.4.4.$tCurvature velocity techniques --$g6.4.5.$tDynamic window approaches --$g6.4.6.$tThe Schlegel approach to obstacle avoidance --$g6.4.7.$tNearness diagram --$g6.4.8.$tGradient method --$g6.4.9.$tAdding dynamic constraints --$g6.4.10.$tOther approaches --$g6.4.11.$tOverview --$g6.5.$tNavigation Architectures --$g6.5.1.$tModularity for code reuse and sharing --$g6.5.2.$tControl localization --$g6.5.3.$tTechniques for decomposition --$g6.5.4.$tCase studies: tiered robot architectures --$g6.6.$tProblems --$tBibliography --$tBooks --$tPapers --$tReferenced Webpages. |
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Local Note |
590
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$aRecommended in Resources for College Libraries |
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Subj:Topical |
650
|
0 |
$aMobile robots. |
|
Subj:Topical |
650
|
0 |
$aAutonomous robots. |
|
AE:Pers Name |
700
|
1 |
$aNourbakhsh, Illah Reza,$d1970- |
|
AE:Pers Name |
700
|
1 |
$aScaramuzza, Davide. |
