Since 1987, he has been with the Robotics Systems Division at the University of Michigan, Ann Arbor, where he is currently an Assistant Research Scientist and Head of the MEAM Mobile Robotics Laboratory. His research interest include mobile robot navigation, obstacle avoidance, path planning, real-time control, sensors for robotic applications, multisensor integration, and computer interfacing and integration. Yoram Koren (M'76-SM'88) has 25 years of research, teaching, and consulting experience in the automated manufacturing field. At present he is a Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Michigan, Ann Arbor. He has published over 100 papers and three books on machine tool control, robotics, sensing methods, and modeling of processes. His book Computer Control for Manufacturing Systems (McGraw-Hill, 1983) is used as a textbook at major universities and received the 1984 Textbook Award for the Society of Manufacturing Engineering (SME). His book, Robotics for Engineers (McGraw-Hill, 1985), was translated into Japanese and French and is used by engineers throughout the world.

Potential field methods are rapidly gaining popularity in obstacle avoidance applicationsfor mobile robots and manipulators. While the potential field principle is particularly attractive because of its elegance and simplicity, substantial shortcomings have been identified as problems that are inherent to this principle. Based upon mathematicalanalysis, this paper presents a systematic criticism of the inherent problems. The heart of this analysis is a differential equation that combines the robot and the environment into a unified system. The identified problems are discussed in qualitative and theoretical terms and documented with experimental results from actual mobile robot runs.

Thi s paper describes a practical method for reducing In a typical differential drive mobile robot incremental odometry errors caused by kinematic imperfections of a mobile encoders are mounted onto the two drive motors to count the robot. These errors, here referred to as "systematic" errors, stay wheel revolutions. After a short sampling interval I the left and almost constant over prolonged periods of time. Performing an right wheel encoders show a pulse increment of N and N , occasional calibration as described here will increase the respectively. Now, suppose that robot's odometric accuracy and reduce operation cost because an accurate mobile robot requires fewer absolute positioning c = D /nC (1) updates. Many manufacturers or end-users calibrate their robots-usually in a time-consuming and non-systematic trial where and error approach. By contrast, our method is systematic, c-Conversion factor that translates encoder pulses into provides near-optimal results, and can be performed easily and linear wheel displacement. without complicated equipment. D-Nominal wheel diameter (in mm). Experimental results are presented that show a consistent C-Encoder resolution (in pulses per revolution). improvement of at least one order of magnitude in odometric n-Gear ratio of the reduction gear between the motor and accuracy (with respect to systematic errors) for a mobile robot the drive wheel. calibrated with the procedure described in this paper.

This paper presents further developments of the earlier Vector Field Histogram (VFH) method for realtime mobile robot obstacle avoidance. The enhanced method, called VFH+, offers several improvements that result in smoother robot trajectories and greater reliability. VFH+ reduces some of the parameter tuning of the original VFH method by explicitly compensating for the robot width. Also added in VFH+ is a better approximation of the mobile robot trajectory, which results in higher reliability.

Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In 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, but 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. Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.