000			07850nam a2200397 i 4500
008			100719s2011 maua b 001 0 eng
010			_a2010028053
020			_a9780262015356 _q(hardcover : alk. paper)
020			_a0262015358 _q(hardcover : alk. paper)
035			_a(OCoLC)649700153
040			_aDLC _beng _cDLC _dYDX _dYDXCP _dCDX _dINU _dUKMGB _dMIX _dBDX _dBTCTA _dI3U _erda
049			_aBAUN_MERKEZ
050	0	4	_aTJ211.415 _b.S54 2011
100	1		_aSiegwart, Roland
245	1	0	_aIntroduction to autonomous mobile robots / _cRoland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza
250			_a2nd ed
264		1	_aCambridge, Mass. : _bMIT Press, _c[2011]
264		4	_c©2011
300			_axvi, 453 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aIntelligent robotics and autonomous agents
504			_aIncludes bibliographical references (pages [425]-445) and index
505	0	0	_tTable Of Contents: _tAcknowledgments _tPreface _t1 Introduction _t1.1 Introduction _t1.2 An Overview of the Book _t2 Locomotion _t2.1 Introduction _t2.1.1 Key issues for locomotion _t2.2 Legged Mobile Robots _t2.2.1 Leg configurations and stability _t2.2.2 Consideration of dynamics _t2.2.3 Examples of legged robot locomotion _t2.3 Wheeled Mobile Robots _t2.3.1 Wheeled locomotion: The design space _t2.3.2 Wheeled locomotion: Case studies _t2.4 Aerial Mobile Robots _t2.4.1 Introduction _t2.4.2 Aircraft configurations _t2.4.3 State of the art in autonomous VTOL _t2.5 Problems _t3 Mobile Robot Kinematics _t3.1 Introduction _t3.2 Kinematic Models and Constraints _t3.2.1 Representing robot position _t3.2.2 Forward kinematic models _t3.2.3 Wheel kinematic constraints _t3.2.4 Robot kinematic constraints _t3.2.5 Examples: Robot kinematic models and constraints _t3.3 Mobile Robot Maneuverability _t3.3.1 Degree of mobility _t3.3.2 Degree of steerability _t3.3.3 Robot maneuverability _t3.4 Mobile Robot Workspace _t3.4.1 Degrees of freedom _t3.4.2 Holonomic robots _t3.4.3 Path and trajectory considerations _t3.5 Beyond Basic Kinematics _t3.6 Motion Control (Kinematic Control) _t3.6.1 Open loop control (trajectory-following) _t3.6.2 Feedback control _t3.7 Problems _t4 Perception _t4.1 Sensors for Mobile Robots _t4.1.1 Sensor classification _t4.1.2 Characterizing sensor performance _t4.1.3 Representing uncertainty _t4.1.4 Wheel/motor sensors _t4.1.5 Heading sensors _t4.1.6 Accelerometers _t4.1.7 Inertial measurement unit (IMU) _t4.1.8 Ground beacons _t4.1.9 Active ranging _t4.1.10 Motion/speed sensors _t4.1.11 Vision sensors _t4.2 Fundamentals of Computer Vision _t4.2.1 Introduction _t4.2.2 The digital camera _t4.2.3 Image formation _t4.2.4 Omnidirectional cameras _t4.2.5 Structure from stereo _t4.2.6 Structure from motion _t4.2.7 Motion and optical flow _t4.2.8 Color tracking _t4.3 Fundamentals of Image Processing _t4.3.1 Image filtering _t4.3.2 Edge detection _t4.3.3 Computing image similarity _t4.4 Feature Extraction _t4.5 Image Feature Extraction: Interest Point Detectors _t4.5.1 Introduction _t4.5.2 Properties of the ideal feature detector _t4.5.3 Corner detectors _t4.5.4 Invariance to photometric and geometric changes _t4.5.5 Blob detectors _t4.6 Place Recognition _t4.6.1 Introduction _t4.6.2 From bag of features to visual words _t4.6.3 Efficient location recognition by using an inverted file _t4.6.4 Geometric verification for robust place recognition _t4.6.5 Applications _t4.6.6 Other image representations for place recognition _t4.7 Feature Extraction Based on Range Data (Laser, Ultrasonic) _t4.7.1 Line fitting _t4.7.2 Six line-extraction algorithms _t4.7.3 Range histogram features _t4.7.4 Extracting other geometric features _t4.8 Problems _t5 Mobile Robot Localization _t5.1 Introduction _t5.2 The Challenge of Localization: Noise and Aliasing _t5.2.1 Sensor noise _t5.2.2 Sensor aliasing _t5.2.3 Effector noise _t5.2.4 An error model for odometric position estimation _t5.3 To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions _t5.4 Belief Representation _t5.4.1 Single-hypothesis belief _t5.4.2 Multiple-hypothesis belief _t5.5 Map Representation _t5.5.1 Continuous representations _t5.5.2 Decomposition strategies _t5.5.3 State of the art: Current challenges in map representation _t5.6 Probabilistic Map-Based Localization _t5.6.1 Introduction _t5.6.2 The robot localization problem _t5.6.3 Basic concepts of probability theory _t5.6.4 Terminology _t5.6.5 The ingredients of probabilistic map-based localization _t5.6.6 Classification of localization problems _t5.6.7 Markov localization _t5.6.8 Kalman filter localization _t5.7 Other Examples of Localization Systems _t5.7.1 Landmark-based navigation _t5.7.2 Globally unique localization _t5.7.3 Positioning beacon systems _t5.7.4 Route-based localization _t5.8 Autonomous Map Building _t5.8.1 Introduction _t5.8.2 SLAM: The simultaneous localization and mapping problem _t5.8.3 Mathematical definition of SLAM _t5.8.4 Extended Kalman Filter (EKF) SLAM _t5.8.5 Visual SLAM with a single camera _t5.8.6 Discussion on EKF SLAM _t5.8.7 Graph-based SLAM _t5.8.8 Particle filter SLAM _t5.8.9 Open challenges in SLAM _t5.8.10 Open source SLAM software and other resources _t5.9 Problems _t6 Planning and Navigation _t6.1 Introduction _t6.2 Competences for Navigation: Planning and Reacting _t6.3 Path Planning _t6.3.1 Graph search _t6.3.2 Potential field path planning _t6.4 Obstacle avoidance _t6.4.1 Bug algorithm _t6.4.2 Vector field histogram _t6.4.3 The bubble band technique _t6.4.4 Curvature velocity techniques _t6.4.5 Dynamic window approaches _t6.4.6 The Schlegel approach to obstacle avoidance _t6.4.7 Nearness diagram _t6.4.8 Gradient method _t6.4.9 Adding dynamic constraints _t6.4.10 Other approaches _t6.4.11 Overview _t6.5 Navigation Architectures _t6.5.1 Modularity for code reuse and sharing _t6.5.2 Control localization _t6.5.3 Techniques for decomposition _t6.5.4 Case studies: tiered robot architectures _t6.6 Problems _tBibliography _tBooks _tPapers _tReferenced Webpages _tIndex
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
650		0	_aMobile robots
650		0	_aAutonomous robots
700	1		_aNourbakhsh, Illah Reza, _d1970-
700	1		_aScaramuzza, Davide
710	2		_9112198 _aM.I.T. Press
830		0	_973103 _aIntelligent robotics and autonomous agents.
900			_a34854
900			_bsatın
942			_2lcc _cKT
999			_c32125 _d32125