Probabilistic Robotics

Author: Sebastian Thrun
Publisher: MIT Press
ISBN: 0262303809
Size: 12.36 MB
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Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Probabilistic Robotics

Author: Sebastian Thrun
Publisher: MIT Press
ISBN: 0262201623
Size: 42.79 MB
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Probablistic robotics is a growing area in the subject, concerned with perception and control in the face of uncertainty and giving robots a level of robustness in real-world situations. This book introduces techniques and algorithms in the field.

Introduction To Autonomous Mobile Robots

Author: Roland Siegwart
Publisher: MIT Press
ISBN: 0262015358
Size: 76.77 MB
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Machine generated contents note: |g 1. |t Introduction -- |g 1.1. |t Introduction -- |g 1.2. |t An Overview of the Book -- |g 2. |t Locomotion -- |g 2.1. |t Introduction -- |g 2.1.1. |t Key issues for locomotion -- |g 2.2. |t Legged Mobile Robots -- |g 2.2.1. |t Leg configurations and stability -- |g 2.2.2. |t Consideration of dynamics -- |g 2.2.3. |t Examples of legged robot locomotion -- |g 2.3. |t Wheeled Mobile Robots -- |g 2.3.1. |t Wheeled locomotion: The design space -- |g 2.3.2. |t Wheeled locomotion: Case studies -- |g 2.4. |t Aerial Mobile Robots -- |g 2.4.1. |t Introduction -- |g 2.4.2. |t Aircraft configurations -- |g 2.4.3. |t State of the art in autonomous VTOL -- |g 2.5. |t Problems -- |g 3. |t Mobile Robot Kinematics -- |g 3.1. |t Introduction -- |g 3.2. |t Kinematic Models and Constraints -- |g 3.2.1. |t Representing robot position -- |g 3.2.2. |t Forward kinematic models -- |g 3.2.3. |t Wheel kinematic constraints -- |g 3.2.4. |t Robot kinematic constraints -- |g 3.g 3.3. |t Mobile Robot Maneuverability -- |g 3.3.1. |t Degree of mobility -- |g 3.3.2. |t Degree of steerability -- |g 3.3.3. |t Robot maneuverability -- |g 3.4. |t Mobile Robot Workspace -- |g 3.4.1. |t Degrees of freedom -- |g 3.4.2. |t Holonomic robots -- |g 3.4.3. |t Path and trajectory considerations -- |g 3.5. |t Beyond Basic Kinematics -- |g 3.6. |t Motion Control (Kinematic Control) -- |g 3.6.1. |t Open loop control (trajectory-following) -- |g 3.6.2. |t Feedback control -- |g 3.7. |t Problems -- |g 4. |t Perception -- |g 4.1. |t Sensors for Mobile Robots -- |g 4.1.1. |t Sensor classification -- |g 4.1.2. |t Characterizing sensor performance -- |g 4.1.3. |t Representing uncertainty -- |g 4.1.4. |t Wheel/motor sensors -- |g 4.1.5. |t Heading sensors -- |g 4.1.6. |t Accelerometers -- |g 4.1.7. |t Inertial measurement unit (IMU) -- |g 4.1.8. |t Ground beacons -- |g 4.1.9. |t Active ranging -- |g 4.1.10. |t Motion/speed sensors -- |g 4.1.11. |t Vision sensors -- |g 4.2. |t Fundameng 4.2.5. |t Structure from stereo -- |g 4.2.6. |t Structure from motion -- |g 4.2.7. |t Motion and optical flow -- |g 4.2.8. |t Color tracking -- |g 4.3. |t Fundamentals of Image Processing -- |g 4.3.1. |t Image filtering -- |g 4.3.2. |t Edge detection -- |g 4.3.3. |t Computing image similarity -- |g 4.4. |t Feature Extraction -- |g 4.5. |t Image Feature Extraction: Interest Point Detectors -- |g 4.5.1. |t Introduction -- |g 4.5.2. |t Properties of the ideal feature detector -- |g 4.5.3. |t Corner detectors -- |g 4.5.4. |t Invariance to photometric and geometric changes -- |g 4.5.5. |t Blob detectors -- |g 4.6. |t Place Recognition -- |g 4.6.1. |t Introduction -- |g 4.6.2. |t From bag of features to visual words -- |g 4.6.3. |t Efficient location recognition by using an inverted file -- |g 4.6.4. |t Geometric verification for robust place recognition -- |g 4.6.5. |t Applications -- |g 4.6.6. |t Other image representations for place recognition -- |g 4.7. |t Feature Extraction Based ong 4.7.3. |t Range histogram features -- |g 4.7.4. |t Extracting other geometric features -- |g 4.8. |t Problems -- |g 5. |t Mobile Robot Localization -- |g 5.1. |t Introduction -- |g 5.2. |t The Challenge of Localization: Noise and Aliasing -- |g 5.2.1. |t Sensor noise -- |g 5.2.2. |t Sensor aliasing -- |g 5.2.3. |t Effector noise -- |g 5.2.4. |t An error model for odometric position estimation -- |g 5.3. |t To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions -- |g 5.4. |t Belief Representation -- |g 5.4.1. |t Single-hypothesis belief -- |g 5.4.2. |t Multiple-hypothesis belief -- |g 5.5. |t Map Representation -- |g 5.5.1. |t Continuous representations -- |g 5.5.2. |t Decomposition strategies -- |g 5.5.3. |t State of the art: Current challenges in map representation -- |g 5.6. |t Probabilistic Map-Based Localization -- |g 5.6.1. |t Introduction -- |g 5.6.2. |t The robot localization problem -- |g 5.6.3. |t Basic concepts of probability theory -- |gg 5.6.6. |t Classification of localization problems -- |g 5.6.7. |t Markov localization -- |g 5.6.8. |t Kalman filter localization -- |g 5.7. |t Other Examples of Localization Systems -- |g 5.7.1. |t Landmark-based navigation -- |g 5.7.2. |t Globally unique localization -- |g 5.7.3. |t Positioning beacon systems -- |g 5.7.4. |t Route-based localization -- |g 5.8. |t Autonomous Map Building -- |g 5.8.1. |t Introduction -- |g 5.8.2. |t SLAM: The simultaneous localization and mapping problem -- |g 5.8.3. |t Mathematical definition of SLAM -- |g 5.8.4. |t Extended Kalman Filter (EKF) SLAM -- |g 5.8.5. |t Visual SLAM with a single camera -- |g 5.8.6. |t Discussion on EKF SLAM -- |g 5.8.7. |t Graph-based SLAM -- |g 5.8.8. |t Particle filter SLAM -- |g 5.8.9. |t Open challenges in SLAM -- |g 5.8.10. |t Open source SLAM software and other resources -- |g 5.9. |t Problems -- |g 6. |t Planning and Navigation -- |g 6.1. |t Introduction -- |g 6.2. |t Competences for Navigation: Planning and Reactig 6.4. |t Obstacle avoidance -- |g 6.4.1. |t Bug algorithm -- |g 6.4.2. |t Vector field histogram -- |g 6.4.3. |t The bubble band technique -- |g 6.4.4. |t Curvature velocity techniques -- |g 6.4.5. |t Dynamic window approaches -- |g 6.4.6. |t The Schlegel approach to obstacle avoidance -- |g 6.4.7. |t Nearness diagram -- |g 6.4.8. |t Gradient method -- |g 6.4.9. |t Adding dynamic constraints -- |g 6.4.10. |t Other approaches -- |g 6.4.11. |t Overview -- |g 6.5. |t Navigation Architectures -- |g 6.5.1. |t Modularity for code reuse and sharing -- |g 6.5.2. |t Control localization -- |g 6.5.3. |t Techniques for decomposition -- |g 6.5.4. |t Case studies: tiered robot architectures -- |g 6.6. |t Problems -- |t Bibliography -- |t Books -- |t Papers -- |t Referenced Webpages.

Introduction To Ai Robotics

Author: Robin Murphy
Publisher: MIT Press
ISBN: 9780262133838
Size: 31.95 MB
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This text covers all the material needed to understand the principles behind the AI approach to robotics and to program an artificially intelligent robot for applications involving sensing, navigation, planning, and uncertainty. Robin Murphy is extremely effective at combining theoretical and practical rigor with a light narrative touch. In the overview, for example, she touches upon anthropomorphic robots from classic films and science fiction stories before delving into the nuts and bolts of organizing intelligence in robots.Following the overview, Murphy contrasts AI and engineering approaches and discusses what she calls the three paradigms of AI robotics: hierarchical, reactive, and hybrid deliberative/reactive. Later chapters explore multiagent scenarios, navigation and path-planning for mobile robots, and the basics of computer vision and range sensing. Each chapter includes objectives, review questions, and exercises. Many chapters contain one or more case studies showing how the concepts were implemented on real robots. Murphy, who is well known for her classroom teaching, conveys the intellectual adventure of mastering complex theoretical and technical material. An Instructor's Manual including slides, solutions, sample tests, and programming assignments is available to qualified professors who are considering using the book or who are using the book for class use.

Mechanics Of Robotic Manipulation

Author:
Publisher: MIT Press
ISBN: 9780262263740
Size: 44.71 MB
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"Manipulation" refers to a variety of physical changes made to the world around us. Mechanics of Robotic Manipulation addresses one form of robotic manipulation, moving objects, and the various processes involved -- grasping, carrying, pushing, dropping, throwing, and so on. Unlike most books on the subject, it focuses on manipulation rather than manipulators. This attention to processes rather than devices allows a more fundamental approach, leading to results that apply to a broad range of devices, not just robotic arms.The book draws both on classical mechanics and on classical planning, which introduces the element of imperfect information. The book does not propose a specific solution to the problem of manipulation, but rather outlines a path of inquiry.

The Robotics Primer

Author: Maja J. Matarić
Publisher: MIT Press
ISBN: 026263354X
Size: 68.51 MB
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A broadly accessible introduction to robotics that spans the most basic concepts and the most novel applications; for students, teachers, and hobbyists.

Autonomous Robots

Author: George A. Bekey
Publisher: MIT Press
ISBN: 0262292475
Size: 33.91 MB
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Autonomous robots are intelligent machines capable of performing tasks in the world by themselves, without explicit human control. Examples range from autonomous helicopters to Roomba, the robot vacuum cleaner. In this book, George Bekey offers an introduction to the science and practice of autonomous robots that can be used both in the classroom and as a reference for industry professionals. He surveys the hardware implementations of more than 300 current systems, reviews some of their application areas, and examines the underlying technology, including control, architectures, learning, manipulation, grasping, navigation, and mapping. Living systems can be considered the prototypes of autonomous systems, and Bekey explores the biological inspiration that forms the basis of many recent developments in robotics. He also discusses robot control issues and the design of control architectures.After an overview of the field that introduces some of its fundamental concepts, the book presents background material on hardware, control (from both biological and engineering perspectives), software architecture, and robot intelligence. It then examines a broad range of implementations and applications, including locomotion (wheeled, legged, flying, swimming, and crawling robots), manipulation (both arms and hands), localization, navigation, and mapping. The many case studies and specific applications include robots built for research, industry, and the military, among them underwater robotic vehicles, walking machines with four, six, and eight legs, and the famous humanoid robots Cog, Kismet, ASIMO, and QRIO. The book concludes with reflections on the future of robotics -- the potential benefits as well as the possible dangers that may arise from large numbers of increasingly intelligent and autonomous robots.

Computational Principles Of Mobile Robotics

Author: Gregory Dudek
Publisher: Cambridge University Press
ISBN: 113985559X
Size: 52.98 MB
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This textbook for advanced undergraduates and graduate students emphasizes algorithms for a range of strategies for locomotion, sensing, and reasoning. It concentrates on wheeled and legged mobile robots but discusses a variety of other propulsion systems. This edition includes advances in robotics and intelligent machines over the ten years prior to publication, including significant coverage of SLAM (simultaneous localization and mapping) and multi-robot systems. It includes additional mathematical background and an extensive list of sample problems. Various mathematical techniques that were assumed in the first edition are now briefly introduced in appendices at the end of the text to make the book more self-contained. Researchers as well as students in the field of mobile robotics will appreciate this comprehensive treatment of state-of-the-art methods and key technologies.

Behavior Based Robotics

Author: Ronald C. Arkin
Publisher: MIT Press
ISBN: 9780262011655
Size: 42.30 MB
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foreword by Michael Arbib "Hard to put down and necessary to know -- Arkin's book provides a comprehensive intellectual history of robots and a thorough compilation of robotic organizational paradigms from reflexes through social interaction." -- Chris Brown, Professor of Computer Science, University of Rochester This introduction to the principles, design, and practice of intelligent behavior-based autonomous robotic systems is the first true survey of this robotics field. The author presents the tools and techniques central to the development of this class of systems in a clear and thorough manner. Following a discussion of the relevant biological and psychological models of behavior, he covers the use of knowledge and learning in autonomous robots, behavior-based and hybrid robot architectures, modular perception, robot colonies, and future trends in robot intelligence. The text throughout refers to actual implemented robots and includes many pictures and descriptions of hardware, making it clear that these are not abstract simulations, but real machines capable of perception, cognition, and action.

Simultaneous Localization And Mapping For Mobile Robots Introduction And Methods

Author: Fernández-Madrigal, Juan-Antonio
Publisher: IGI Global
ISBN: 1466621052
Size: 35.64 MB
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As mobile robots become more common in general knowledge and practices, as opposed to simply in research labs, there is an increased need for the introduction and methods to Simultaneous Localization and Mapping (SLAM) and its techniques and concepts related to robotics. Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods investigates the complexities of the theory of probabilistic localization and mapping of mobile robots as well as providing the most current and concrete developments. This reference source aims to be useful for practitioners, graduate and postgraduate students, and active researchers alike.