Johann Borenstein

Bio: Johann Borenstein is an academic researcher from University of Michigan. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 54, co-authored 129 publications receiving 15645 citations. Previous affiliations of Johann Borenstein include Technion – Israel Institute of Technology.

The vector field histogram-fast obstacle avoidance for mobile robots

Abstract: A real-time obstacle avoidance method for mobile robots which has been developed and implemented is described. This method, named the vector field histogram (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses a two-dimensional Cartesian histogram grid as a world model. This world model is updated continuously with range data sampled by onboard range sensors. The VFH method subsequently uses a two-stage data-reduction process to compute the desired control commands for the vehicle. Experimental results from a mobile robot traversing densely cluttered obstacle courses in smooth and continuous motion and at an average speed of 0.6-0.7 m/s are shown. A comparison of the VFN method to earlier methods is given. >

Potential field methods and their inherent limitations for mobile robot navigation

Abstract: Based on a rigorous mathematical analysis, the authors present a systematic overview and a critical discussion of the inherent problems of potential field methods (PFMs). The authors previously (1989) developed a PFM called the virtual force field (VFF) method. Much insight has been gained into the strengths and weaknesses of this method. Four distinct drawbacks with PFMs are identified. Because of these drawbacks, the authors abandoned potential field methods and developed a new method for fast obstacle avoidance. This method, called the vector field histogram method, produces smooth, nonoscillatory motion, while sampling time and hardware are identical to those used in the VFF method. >

Real-time obstacle avoidance for fast mobile robots

Abstract: A real-time obstacle avoidance approach for mobile robots has been developed and implemented. It permits the detection of unknown obstacles simultaneously with the steering of the mobile robot to avoid collisions and advance toward the target. The novelty of this approach, entitled the virtual force field method, lies in the integration of two known concepts: certainty grids for obstacle representation and potential fields for navigation. This combination is especially suitable for the accommodation of inaccurate sensor data as well as for sensor fusion and makes possible continuous motion of the robot with stopping in front of obstacles. This navigation algorithm also takes into account the dynamic behavior of a fast mobile robot and solves the local minimum trap problem. Experimental results from a mobile robot running at a maximum speed of 0.78 m/s demonstrate the power of the algorithm. >

Measurement and correction of systematic odometry errors in mobile robots

Abstract: 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.

Navigating Mobile Robots: Systems and Techniques

Abstract: 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

Sequential Monte Carlo methods in practice

Abstract: 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.

Planning Algorithms: Introductory Material

Abstract: 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.

Sampling-based algorithms for optimal motion planning

Abstract: During the last decade, sampling-based path planning algorithms, such as probabilistic roadmaps (PRM) and rapidly exploring random trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g. as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g. showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.

The dynamic window approach to collision avoidance

Abstract: 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.

Introduction to Autonomous Mobile Robots

Abstract: 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.