Johann Borenstein

The vector field histogram-fast obstacle avoidance for mobile robots

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

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.<<ETX>>

Real-time obstacle avoidance for fast mobile robots

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

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.

VFH+: reliable obstacle avoidance for fast mobile robots

This paper presents further improvements on the earlier vector field histogram (VFH) method developed by Borenstein-Koren (1991) for real-time 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.

Mobile robot positioning: Sensors and techniques

Abstract : 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.)

Histogramic in-motion mapping for mobile robot obstacle avoidance

Histogramic in-motion mapping (HIMM) is introduced as a new method for real-time map building with a mobile robot motion. HIMM represents data in a two-dimensional array, called a histogram grid, that is updated through rapid in-motion sampling of on-board range sensors. Rapid in-motion sampling results in a map representation that is well-suited to modeling inaccurate and noisy range-sensor data, such as those produced by ultrasonic sensors, and requires minimal computational overhead. Fast map building allows the robot to use immediately the mapped information in real-time obstacle-avoidance algorithms. The benefits of this integrated approach are quick, accurate mapping and safe navigation of the robot toward a given target. HIMM has been implemented and tested on a mobile robot. Its dual functionality was demonstrated through numerous tests in which maps of unknown obstacle courses were created, while the robot simultaneously performed real-time obstacle avoidance maneuvers at speeds of up to 0.78 m/s. >

Obstacle avoidance with ultrasonic sensors

A mobile robot system, capable of performing various tasks for the physically disabled, has been developed. To avoid collision with unexpected obstacles, the mobile robot uses ultrasonic range finders for detection and mapping. The obstacle avoidance strategy used for this robot is described. Since this strategy depends heavily on the performance of the ultrasonic range finders, these sensors and the effect of their limitations on the obstacle avoidance algorithm are discussed in detail. >

VFH/sup */: local obstacle avoidance with look-ahead verification

This paper presents an enhancement to the earlier developed vector field histogram (VFH) method for mobile robot obstacle avoidance. The enhanced method, called VFH/sup */ successfully deals with situations that are problematic for purely local obstacle avoidance algorithms. The VFH/sup */ method verifies that a particular candidate direction guides the robot around an obstacle. The verification is performed by using the A/sup */ search algorithm and appropriate cost and heuristic functions.

Real-time obstacle avoidance for fast mobile robots in cluttered environments

The method described, named the vector field histogram (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. A VFH-controlled mobile robot maneuvers quickly and without stopping among densely cluttered obstacles. The VFH method uses a two-dimensional Cartesian histogram grid as a world model. This world model is updated continuously and in real time with range data sampled by the onboard ultrasonic range sensors. Based on the accumulated environmental data, the VFH method then computes a one-dimensional polar histogram that is constructed around the robots momentary location. Each sector in the polar histogram holds the polar obstacle density in that direction. Finally, the algorithm selects the most suitable sector from among all polar histogram sectors with low obstacle density, and the steering of the robot is aligned with that direction. Experimental results from a mobile robot traversing a densely cluttered obstacle course at an average speed of 0.7 m/s demonstrate the power of the VFH method.<<ETX>>