Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

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Sequential Monte Carlo methods in practice

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This approach, designed for mobile robots equipped with synchro-drives, is derived directly from the motion dynamics of the robot. In experiments, the dynamic window approach safely controlled the mobile robot RHINO at speeds of up to 95 cm/sec, in populated and dynamic environments.

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The dynamic window approach to collision avoidance

Mobile 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.

http://www.gbv.de/dms/hebis-darmstadt/toc/120387220.pdf

Introduction to Autonomous Mobile Robots

http://www4.cs.umanitoba.ca/~jacky/Teaching/Courses/74.795-LocalVision/ReadingList/thrun01robust.pdf

Robust Monte Carlo localization for mobile robots

Odometry is the most widely used method for determining the momentary position of a mobile robot. This paper introduces practical methods for measuring and reducing odometry errors that are caused by the two dominant error sources in differential-drive mobile robots: 1) uncertainty about the effective wheelbase; and 2) unequal wheel diameters. These errors stay almost constant over prolonged periods of time. Performing an occasional calibration as proposed here will increase the odometric accuracy of the robot and reduce operation cost because an accurate mobile robot requires fewer absolute positioning updates. Many manufacturers or end-users calibrate their robots, usually in a time-consuming and nonsystematic trial and error approach. By contrast, the method described in this paper is systematic, provides near-optimal results, and it can be performed easily and without complicated equipment. Experimental results are presented that show a consistent improvement of at least one order of magnitude in odometric accuracy (with respect to systematic errors) for a mobile robot calibrated with our method.

https://www.johnloomis.org/ece445/topics/odometry/borenstein/paper58.pdf

Measurement and correction of systematic odometry errors in mobile robots

This is a survey of the state-of-the-art in sensors, systems, methods and technologies utilized by a mobile robot to determine its position in the environment The many potential "solutions" are roughly categorized into two groups: relative and absolute position measurements The first includes odometry and inertial navigation; the second comprises active beacons, artificial and natural landmark recognition and model matching The authors compare and analyze these different methods based on technical publications and on commercial product and patent information Comparison is centred around the following criteria: accuracy of position and orientation measurements; equipment needed; cost; sampling rate; effective range; computational power required; processing needs; and other special features

Navigating Mobile Robots: Systems and Techniques

: Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In the search for a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This paper provides a review of relevant mobile robot positioning technologies. The paper defines seven categories for positioning systems: (1) Odometry, (2) Inertial Navigation, (3) Magnetic Compasses, (4) Active Beacons, (5) Global Positioning Systems, (6) Landmark Navigation, and (7) Model Matching. The characteristics of each category are discussed and examples of existing technologies are given for each category. The field of mobile robot navigation is active and vibrant, with more great systems and ideas being developed continuously. For this reason the examples presented in this paper serve only to represent their respective categories; they do not represent a judgment by the authors. Many ingenious approaches can be found in the literature, although, for reasons of brevity, not all could be cited in this paper. The appendix contains a tabular comparison of the positioning systems discussed in this review that includes system and description, features, accuracy (position), accuracy (orientation), effective range, and source of information. (47 refs.)

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Mobile Robot Positioning - Sensors and Techniques

This paper presents a very simple, yet very effective method for combining measurements from a gyro with measurements from wheel encoders (odometry). The method, called gyrodometry, has been developed based on a careful study of the physical interaction between the ground and the vehicle. We present experimental evidence that non-systematic odometry error sources (such as bumps) impact the vehicle only during very short periods; typically a fraction of a second for each encounter. During these short instances the readings from the gyro and from odometry differ significantly, while in the absence of large non-systematic errors the readings are very similar. Gyrodometry makes use of this observation by using odometry data only-most of the time, while substituting gyro data only during those brief instances during which the gyro and odometry data differ substantially. In this way the ill-effects of gyro drift are almost completely eliminated, and our method can thus make use of inexpensive gyros with large drift rates. Experimental data is presented that demonstrates the effectiveness of this approach.

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Gyrodometry: a new method for combining data from gyros and odometry in mobile robots

The design and implementation of a cross-coupling motion controller for a differential-drive mobile robot is described. A new concept, the most significant error, is introduced as the control design objective. Cross-coupling control directly minimizes the most significant error by coordinating the motion of the two drive wheels. The cross-coupling controller has excellent disturbance rejection. This is advantageous when the robot is not loaded symmetrically or has large friction in its drive mechanism, and especially when it is instructed to follow curved paths. Experimental results show that cross-coupling control yields substantially smaller position and orientation errors than conventional methods. >

Liqiang Feng

Papers

Measurement and correction of systematic odometry errors in mobile robots

Navigating Mobile Robots: Systems and Techniques

Mobile Robot Positioning - Sensors and Techniques

Gyrodometry: a new method for combining data from gyros and odometry in mobile robots

Cross-coupling motion controller for mobile robots