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Introduction to autonomous mobile robots- [electronic resource]
Introduction to autonomous mobile robots- [electronic resource]
상세정보
- 자료유형
- 단행본
- International Standard Book Number
- 9780262295321 (electronic bk.)
- International Standard Book Number
- 0262295326 (electronic bk.)
- International Standard Book Number
- 9780262015356 (hardcover : alk. paper)
- International Standard Book Number
- 0262015358 (hardcover : alk. paper)
- Library of Congress Call Number
- TJ211.415-.S54 2011eb
- Dewey Decimal Classification Number
- 629.8932
- Main Entry-Personal Name
- Siegwart, Roland.
- Edition Statement
- 2nd ed. / Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza.
- Publication, Distribution, etc. (Imprint
- Cambridge, Mass : MIT Press, c2011
- Physical Description
- 1 online resource (xvi, 453 p) : ill.
- Series Statement
- Intelligent robotics and autonomous agents
- Bibliography, Etc. Note
- Includes bibliographical references and index.
- Formatted Contents Note
- Machine generated contents note 1. Introduction -- 1.1. Introduction -- 1.2. An Overview of the Book -- 2. Locomotion -- 2.1. Introduction -- 2.1.1. Key issues for locomotion -- 2.2. Legged Mobile Robots -- 2.2.1. Leg configurations and stability -- 2.2.2. Consideration of dynamics -- 2.2.3. Examples of legged robot locomotion -- 2.3. Wheeled Mobile Robots -- 2.3.1. Wheeled locomotion: The design space -- 2.3.2. Wheeled locomotion: Case studies -- 2.4. Aerial Mobile Robots -- 2.4.1. Introduction -- 2.4.2. Aircraft configurations -- 2.4.3. State of the art in autonomous VTOL -- 2.5. Problems -- 3. Mobile Robot Kinematics -- 3.1. Introduction -- 3.2. Kinematic Models and Constraints -- 3.2.1. Representing robot position -- 3.2.2. Forward kinematic models -- 3.2.3. Wheel kinematic constraints -- 3.2.4. Robot kinematic constraints -- 3.2.5. Examples: Robot kinematic models and constraints
- Formatted Contents Note
- 3.3. Mobile Robot Maneuverability -- 3.3.1. Degree of mobility -- 3.3.2. Degree of steerability -- 3.3.3. Robot maneuverability -- 3.4. Mobile Robot Workspace -- 3.4.1. Degrees of freedom -- 3.4.2. Holonomic robots -- 3.4.3. Path and trajectory considerations -- 3.5. Beyond Basic Kinematics -- 3.6. Motion Control (Kinematic Control) -- 3.6.1. Open loop control (trajectory-following) -- 3.6.2. Feedback control -- 3.7. Problems -- 4. Perception -- 4.1. Sensors for Mobile Robots -- 4.1.1. Sensor classification -- 4.1.2. Characterizing sensor performance -- 4.1.3. Representing uncertainty -- 4.1.4. Wheel/motor sensors -- 4.1.5. Heading sensors -- 4.1.6. Accelerometers -- 4.1.7. Inertial measurement unit (IMU) -- 4.1.8. Ground beacons -- 4.1.9. Active ranging -- 4.1.10. Motion/speed sensors -- 4.1.11. Vision sensors -- 4.2. Fundamentals of Computer Vision -- 4.2.1. Introduction -- 4.2.2. The digital camera -- 4.2.3. Image formation -- 4.2.4. Omnidirectional cameras
- Formatted Contents Note
- 4.2.5. Structure from stereo -- 4.2.6. Structure from motion -- 4.2.7. Motion and optical flow -- 4.2.8. Color tracking -- 4.3. Fundamentals of Image Processing -- 4.3.1. Image filtering -- 4.3.2. Edge detection -- 4.3.3. Computing image similarity -- 4.4. Feature Extraction -- 4.5. Image Feature Extraction: Interest Point Detectors -- 4.5.1. Introduction -- 4.5.2. Properties of the ideal feature detector -- 4.5.3. Corner detectors -- 4.5.4. Invariance to photometric and geometric changes -- 4.5.5. Blob detectors -- 4.6. Place Recognition -- 4.6.1. Introduction -- 4.6.2. From bag of features to visual words -- 4.6.3. Efficient location recognition by using an inverted file -- 4.6.4. Geometric verification for robust place recognition -- 4.6.5. Applications -- 4.6.6. Other image representations for place recognition -- 4.7. Feature Extraction Based on Range Data (Laser, Ultrasonic) -- 4.7.1. Line fitting -- 4.7.2. Six line-extraction algorithms
- Formatted Contents Note
- 4.7.3. Range histogram features -- 4.7.4. Extracting other geometric features -- 4.8. Problems -- 5. Mobile Robot Localization -- 5.1. Introduction -- 5.2. The Challenge of Localization: Noise and Aliasing -- 5.2.1. Sensor noise -- 5.2.2. Sensor aliasing -- 5.2.3. Effector noise -- 5.2.4. An error model for odometric position estimation -- 5.3. To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions -- 5.4. Belief Representation -- 5.4.1. Single-hypothesis belief -- 5.4.2. Multiple-hypothesis belief -- 5.5. Map Representation -- 5.5.1. Continuous representations -- 5.5.2. Decomposition strategies -- 5.5.3. State of the art: Current challenges in map representation -- 5.6. Probabilistic Map-Based Localization -- 5.6.1. Introduction -- 5.6.2. The robot localization problem -- 5.6.3. Basic concepts of probability theory -- 5.6.4. Terminology -- 5.6.5. The ingredients of probabilistic map-based localization
- Formatted Contents Note
- 5.6.6. Classification of localization problems -- 5.6.7. Markov localization -- 5.6.8. Kalman filter localization -- 5.7. Other Examples of Localization Systems -- 5.7.1. Landmark-based navigation -- 5.7.2. Globally unique localization -- 5.7.3. Positioning beacon systems -- 5.7.4. Route-based localization -- 5.8. Autonomous Map Building -- 5.8.1. Introduction -- 5.8.2. SLAM: The simultaneous localization and mapping problem -- 5.8.3. Mathematical definition of SLAM -- 5.8.4. Extended Kalman Filter (EKF) SLAM -- 5.8.5. Visual SLAM with a single camera -- 5.8.6. Discussion on EKF SLAM -- 5.8.7. Graph-based SLAM -- 5.8.8. Particle filter SLAM -- 5.8.9. Open challenges in SLAM -- 5.8.10. Open source SLAM software and other resources -- 5.9. Problems -- 6. Planning and Navigation -- 6.1. Introduction -- 6.2. Competences for Navigation: Planning and Reacting -- 6.3. Path Planning -- 6.3.1. Graph search -- 6.3.2. Potential field path planning
- Formatted Contents Note
- 6.4. Obstacle avoidance -- 6.4.1. Bug algorithm -- 6.4.2. Vector field histogram -- 6.4.3. The bubble band technique -- 6.4.4. Curvature velocity techniques -- 6.4.5. Dynamic window approaches -- 6.4.6. The Schlegel approach to obstacle avoidance -- 6.4.7. Nearness diagram -- 6.4.8. Gradient method -- 6.4.9. Adding dynamic constraints -- 6.4.10. Other approaches -- 6.4.11. Overview -- 6.5. Navigation Architectures -- 6.5.1. Modularity for code reuse and sharing -- 6.5.2. Control localization -- 6.5.3. Techniques for decomposition -- 6.5.4. Case studies: tiered robot architectures -- 6.6. Problems -- Bibliography -- Books -- Papers -- Referenced Webpages.
- Summary, Etc.
- 요약Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.--publisher description.
- Subject Added Entry-Topical Term
- Mobile robots
- Subject Added Entry-Topical Term
- Autonomous robots
- Subject Added Entry-Topical Term
- TECHNOLOGY & ENGINEERING / Automation.
- Added Entry-Personal Name
- Nourbakhsh, Illah Reza , 1970-
- Added Entry-Personal Name
- Scaramuzza, Davide.
- Additional Physical Form Entry
- Print versionSiegwart, Roland. Introduction to autonomous mobile robots. Cambridge, Mass. : MIT Press, c2011 9780262015356 (DLC) 2010028053 (OCoLC)649700153
- Series Added Entry-Uniform Title
- Intelligent robotics and autonomous agents.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:422738
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■24510▼aIntroduction to autonomous mobile robots▼h[electronic resource]
■24630▼aAutonomous mobile robots
■250 ▼a2nd ed.▼bRoland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza.
■260 ▼aCambridge, Mass▼bMIT Press▼cc2011
■300 ▼a1 online resource (xvi, 453 p) ▼bill.
■4901 ▼aIntelligent robotics and autonomous agents
■504 ▼aIncludes bibliographical references and index.
■50500▼gMachine generated contents note▼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 --▼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
■50500▼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 --▼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
■50500▼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
■50500▼g4.7.3.▼tRange histogram features --▼g4.7.4.▼tExtracting other geometric features --▼g4.8.▼tProblems --▼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
■50500▼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 --▼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
■50500▼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.
■520 ▼aMobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.--publisher description.
■588 ▼aDescription based on print version record.
■650 0▼aMobile robots
■650 0▼aAutonomous robots
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■655 4▼aElectronic books.
■7001 ▼aNourbakhsh, Illah Reza▼d1970-
■7001 ▼aScaramuzza, Davide.
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■830 0▼aIntelligent robotics and autonomous agents.
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