Serpentine robots are slender, multi-segmented vehicles designed to provide greater mobility than conventional wheeled or tracked robots. Serpentine robots are thus ideally suited for urban search and rescue, mine rescue, military intelligence gathering, and for surveillance and inspection tasks in hazardous and hard-to-reach environments. One such serpentine robot, developed at the University of Michigan, is the "OmniTread OT-4" [Borenstein et al., 2007]. The OT-4, shown in Figure 1, can climb over obstacles that are much higher than the robot itself, propel itself inside pipes of different diameters, and traverse even the most difficult terrain, such as rocks or the rubble of a collapsed structure.

This paper introduces a novel method for wheel-slippage detection and correction based on motor current measurements. Our proposed method estimates wheel slippage from motor current measurements, and adjusts encoder readings affected by wheel slippage accordingly. The correction of wheel slippage based on motor currents works only in the direction of motion, but not laterally, and it requires some knowledge of the terrain. However, this knowledge does not have to be provided ahead of time by human operators. Rather, we propose three tuning techniques for determining relevant terrain parameters automatically, in real time, and during motion over unknown terrain. Two of the tuning techniques require position ground truth (i.e., GPS) to be available either continuously or sporadically. The third technique does not require any position ground truth, but is less accurate than the two other methods. A comprehensive set of experimental results have been included to validate this approach

This paper introduces a new control method for pneumatic actuators, called "Proportional Position and Stiffness (PPS)" controller. The PPS method provides both position and stiffness control for a robot joint driven by a pneumatic cylinder with four ON-OFF valves. In addition, the proposed control system consumes much less compressed air than comparable strategies. These features make the PPS method highly suitable to application in mobile robots.

This paper presents a new obstacle negotiation method for mobile robot navigation on rough terrain. The proposed method first transforms a local terrain map surrounding the robot's momentary position into a grid-type traversability map by extracting the slope and roughness of a terrain patch through least-squares plane-fitting. It then computes so-called "polar obstacle densities" for the cells in the traversability map and transforms the traversability map into a traversability field histogram, from which the velocity and the steering command for the robot are determined. Simulation results show that the algorithm is able to navigate the robot to the target with a finite-length path.

This paper introduces a novel filter for terrain mapping with a two-dimensional laser rangefinder. The filter, called the certainty-assisted spatial (CAS) filter, uses the physical constraints on motion continuity and spatial continuity to identify corrupted pixels and missing data in an elevation map. The filter removes the corrupted pixels, fills in the missing data, and leaves the uncorrupted pixels intact so as to preserve the details of a terrain map. Our extensive indoor and outdoor mapping experiments show the CAS filter's superior performance in erroneous data reduction and map detail preservation over conventional filters.

Introduces the kinematic design of a vehicle capable of traversing extremely rugged terrain, such as the rubble of a collapsed building. The vehicle, called "OmniPede," is being developed at the University of Michigan's Mobile Robotics Laboratory. The foremost innovation in the OmniPede is its kinematic design: the OmniPede can be thought of as an elongated, round, flexible body that has a large number of small "hands" or "feet" all over its hull ("skin"). Ideally these hands all perform a coordinated shoveling motion that provides forward propulsion wherever a hand is in contact with any feature in the environment, regardless of how many of the hands make contact and which ones do.

Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. The paper introduces a new fuzzy logic expert rule-based navigation (FLEXnav) method for fusing data from multiple low- to medium-cost gyroscopes and accelerometers in order to estimate accurately the heading and tilt of a mobile robot. Experimental results of mobile robot runs over rugged terrain are presented, showing the effectiveness of our FLEXnav method.

This paper presents a characterization study of the Sick LMS 200 laser scanner. A number of parameters, such as operation time, data transfer rate, target surface properties, as well as the incidence angle, which may potentially affect the sensing performance, are investigated. A probabilistic range measurement model is built based on the experimental results. The paper also analyzes the mixed pixels problem of the scanner.

Fiber optic gyros with very low drift rates have become available and affordable. Because of their low drift rate attention is warranted to sources of errors that were previously considered as of secondary importance. In the KVH E-Core RD2100 gyros, we found that the temperature dependency and the non-linearity of the scale-factor caused significant errors. A precision calibration procedure, described in the paper, reduces the resulting errors by one order of magnitude. In addition to the calibration procedure, we integrated the gyro with the odometry system of a four-wheel drive, skid-steer Pioneer AT mobile robot by means of an indirect feedback Kalman filter that fuses the sensor data from both sensor modalities. Based on our experimental results the paper compares the relative effectiveness of both enhancements (precision gyro calibration and Kalman filter).

One problem oftentimes observed with arrays of multiple ultrasonic sensors (sonars) in obstacle detection and avoidance systems is crosstalk. The paper presents a method in which data generated by crosstalk is actually used to generate more reliable and accurate object detection. This is accomplished by assigning a unique code to the signals emitted by each sonar, so that the source sonar can be identified even if its signal's echo is received by another sonar. Using geometric interpolation between the various sonars increases the accuracy of the measurements, overcoming their limited resolution. Experimental results show the potential of our system for mobile robotics obstacle detection and avoidance, as well as for localization using map-matching techniques.

This paper describes two methods aimed at improving dead-reckoning accuracy with fiber-optic gyroscopes (FOGs) in mobile robots. The first method is a precision calibration procedure for FOGs, which effectively reduces the ill effects of nonlinearity of the scale-factor and temperature dependency. The second method is the implementation of an indirect feedback Kalman filter that fuses the sensor data from the FOG with the odometry system of the mobile robot. The paper also provides experimental results and compares the relative effectiveness of the two methods as implemented on a four-wheel drive/skidsteer Pioneer AT mobile robot.

The GuideCane is a device designed to help blind or visually impaired users navigate safely and quickly among obstacles and other hazards. During operation, the user pushes the lightweight GuideCane forward. When the GuideCane's ultrasonic sensors detect an obstacle, the embedded computer determines a suitable direction of motion that steers the GuideCane and the user around it. The steering action results in a very noticeable force felt in the handle, which easily guides the user without any conscious effort on his/her part.

Fiber-optics gyroscopes (gyros) are gaining importance as a means for improving dead-reckoning accuracy in mobile robots. In the past, the relatively high drift rate of moderately priced gyros presented the foremost technical limitation of these devices. More recently, fiber-optics gyros with very low drift rates have become available and affordable. Because of their low drift rate attention is warranted to sources of errors that were previously considered as of secondary importance. In the KVH E-Core RD2100 gyros that were examined at our lab we found that the nonlinearity of the scale-factor and temperature dependency caused significant errors. A calibration method, described in the paper, reduces the resulting errors by one order of magnitude.

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.

This paper presents results from the experimental evaluation of a state-of-the-art fiber-optics gyroscope. The purpose of our experiments was to evaluate the suitability of this gyroscope for enhancing dead-reckoning in mobile robots. The evaluated gyroscope was the "Autogyro Navigator" made by Andrews. For a 4/spl times/4 m square path the dead-reckoning error when using this gyro was less than /spl plusmn/1.2/spl deg/ in orientation and about 10 cm (4 in) in position.

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.

This paper presents the experimental results of test with a new method for detecting and correcting odometry errors without inertial or external-reference sensors. This method, called internal position error correction (IPEC), has been implemented on a new, commercially available mobile robot called "OmniMate", which was specifically designed for the implementation of the IPEC method. The results presented show that the OmniMate can improve odometric accuracy by one order of magnitude over conventional mobile robots. The OmniMate's odometry is almost completely insensitive to even severe irregularities of the floor. With conventional mobile robots such irregularities can cause large odometry errors with potentially catastrophic effects, thus mandating frequent external registrations to correct for possible odometry errors. The OmniMate with IPEC, on the other hand, detects and corrects such nonsystematic odometry errors, thus allowing more reliable travel over larger distances.

A blind traveler walking through an unfamiliar environment and a mobile robot navigating through a cluttered environment have an important feature in common: both have the kinematic ability to perform the motion, but they are dependent on a sensory system to detect and avoid obstacles. The paper describes the use of a mobile robot obstacle avoidance system as a guidance device for blind and visually impaired people. Just as electronic signals are sent to a mobile robot's motion controllers, auditory signals can guide the blind traveler around obstacles, or alternatively, they can provide an "acoustic image" of the surroundings. The concept has been implemented and tested in a new travel aid for the blind, called the Navbelt. The Navbelt introduces two new concepts to electronic travel aids (ETA's) for the blind: it provides information not only about obstacles along the traveled path, but also assists the user in selecting the preferred travel path. In addition, the level of assistance can be automatically adjusted according to changes in the environment and the user's needs and capabilities. Experimental results conducted with the Navbelt simulator and a portable experimental prototype are presented.

This paper introduces a new mobile robot for hazardous environments and for industrial applications. The robot, called OmniMate, has full omnidirectional motion capabilities, can detect and correct odometry errors without external references, and offers a large 183/spl times/91 cm (72/spl times/36") loading deck. A patented, so-called compliant linkage avoids the excessive wheel slippage often found in other omnidirectional platforms. This paper provides an overview over the mechanical and kinematic design of the robot, as well as over the complex, three-level onboard control system. Also explained is the unique, patented odometry error correction method, called internal position error correction (IPEC). The foremost advantage of the OmniMate with IPEC over conventional mobile robots is that the OmniMate's odometry is almost completely insensitive to even severe irregularities of the floor, such as bumps, cracks, or traversable objects. The OmniMate can travel reliably over larger distances than conventional mobile platforms, thus requiring much fewer external corrections.

This paper introduces the GuideCane, a novel device designed to help blind or visually impaired travellers to navigate safely and quickly among obstacles and other hazards faced by blind pedestrians. The GuideCane, currently under development at the University of Michigan's Mobile Robotics Lab, comprises of a long handle and a "sensor head" unit that is attached at the distal end of the handle. The sensor head is mounted on a steerable but unpowered two-wheeled steering axle. During operation, the user pushes the lightweight GuideCane ahead of him/her. Ultrasonic sensors mounted on the sensor head detect obstacles and steer the device around it. The user feels the steering command as a very noticeable physical force through the handle and is able to follow the GuideCane's path easily and without any conscious effort.

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.

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.

This paper describes a practical method for reducing odometry errors caused by kinematic imperfections of a mobile robot. These errors, here referred to as "systematic" errors, stay almost constant over a prolonged period of time. Performing an occasional calibration as described here will increase the robot's odometric accuracy 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 non-systematic trial and error approach. By contrast the authors' method is systematic, provides near-optimal results, and 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 the procedure described in this paper.

Mobile robots (unlike automated guided vehicles) usually rely on dead-reckoning between periodic absolute position updates and their performance is diminished by excessive wheel slippage. This paper introduces compliant linkage, a new concept in the control and kinematic design of multi-degree-of-freedom (MDOF) mobile robots. As the name implies, compliant linkage provides compliance between the drive wheels or drive axles of a vehicle, to accommodate control errors which would otherwise cause wheel slippage. The concept of compliant linkage was implemented and tested on a 4-DOF vehicle built at the University of Michigan's Mobile Robotics Lab. Experimental results show that control errors are effectively absorbed by the linkage, resulting in smooth and precise motion. The dead-reckoning accuracy of the compliant linkage vehicle is substantially better than that reported in the literature for other MDOF vehicles; it was found equal to, or even better than that of comparable 2-DOF vehicles.>

This paper introduces error eliminating rapid ultrasonic firing (EERUF), a new method for firing multiple ultrasonic sensors in mobile robot applications. EERUF allows ultrasonic sensors to fire at rates that are five to ten times faster than those customary in conventional applications. This is possible because EERUF reduces the number of erroneous readings due to ultrasonic noise by one to two orders of magnitude. While faster firing rates improve the reliability and robustness of mobile robot obstacle avoidance and are necessary for safe travel at higher speed (e.g., V>0.3 m/sec), they introduce more ultrasonic noise and increase the occurrence rate of crosstalk. However, EERUF almost eliminates crosstalk, making fast firing feasible. Furthermore, ERRUF's unique noise rejection capability allows multiple mobile robots to collaborate in the same environment, even if their ultrasonic sensors operate at the same frequencies. The authors have implemented and tested the EERUF method on a mobile robot and they present experimental results. With EERUF, a mobile robot was able to traverse an obstacle course of densely spaced, pencil-thin (8 mm-diameter) poles at up to 1 m/sec.>

The NavChair assistive navigation system is being developed to meet the needs of multiply handicapped people who are unable to operate available wheelchair systems. The NavChair Project was conceived as an application of mobile robot obstacle avoidance to a power wheelchair. During the course of this project, the vector field histogram (VFH) method has been adapted for use in human-machine systems and the shortcomings of the wheelchair platform have been overcome. This paper briefly reviews the VFH method, describes interesting aspects of its application to a power wheelchair, and presents an experimental evaluation of system performance. Finally, unresolved problems of obstacle avoidance in human-operated vehicles are discussed.>

Presents a new approach to accurate and reliable dead-reckoning with mobile robots. The approach makes use of special properties of the author's multi-degree-of-freedom (MDOF) mobile platform, in which two differential-drive mobile robots (called "trucks") are physically connected through a compliant linkage. Using one linear and two rotary encoders, the system can measure the relative distance and bearing between the two trucks. During operation, both trucks perform conventional dead-reckoning with their wheel encoders, but, in addition, use information about their relative position to correct dead-reckoning errors. The author's system, called Compliant Linkage Autonomous Platform with Position Error Recovery (CLAPPER), requires neither external references (such as navigation beacons, artificial landmarks, known floorplans, or satellite signals), nor inertial navigation aids (such as accelerometers or gyros). Nonetheless, the experimental results included in this paper show one to two orders of magnitude better positioning accuracy than systems based on conventional dead-reckoning.>

This paper describes the design and implementation of a model-reference adaptive motion controller for a differential-drive mobile robot. This controller uses absolute position information to modify control parameters in real time to compensate for motion errors. Robot motion errors are classified into internal and external errors. A cross-coupling control method is used to compensate for the internal errors that can be detected by wheel encoders. The adaptive controller provides compensation for external errors. The adaptive controller is analyzed, and its stability and convergence are discussed. Experiments are conducted to evaluate the control system and the results show significant improvements over conventional controllers.>

A blind traveler walking through an unfamiliar environment and a mobile robot navigating through a cluttered environment have an important feature in common: both have the kinematic ability to perform the motion, but are depended on a sensory system to detect and avoid obstacles. This paper describes the use of a mobile robot obstacle avoidance system as a guidance device for blind and visually impaired people. Just like electronic signals are sent to a mobile robot's motor controllers, auditory signals can guide the blind traveler around obstacles, or alternatively, they can provide an "acoustic image" of the surroundings. The concept has been implemented and tested in a new traveling aid for the blind, called the Navbelt. Experimental results of subjects traveling with the Navbelt in different surroundings are presented.>

This paper presents an innovative method for accurate mobile robot dead-reckoning, called internal position error correction (IPEC). In previous work, the IPEC method was successfully implemented on a specially designed mobile robot with two differential drive axles, called the multi-degree-of-freedom (MDOF) mobile robot. Experimental results with the MDOF robot showed consistently one to two orders of magnitude better dead-reckoning accuracy than systems based on conventional dead-reckoning. Yet, the IPEC system requires neither external references (such as navigation beacons, artificial landmarks, known floorplans, or satellite signals), nor inertial navigation aids (such as accelerometers or gyros). This paper focuses on our current efforts to implement the IPEC method on a device that can be added to any existing mobile robot. This device, called the "Smart Encoder Trailer" (SET), is a small, single-axle trailer with an incremental encoder on each of its two wheels. Although the SET is not functional yet, simulation results combined with experimental results from the (similarly configured) MDOF vehicle strongly suggest the feasibility of the SET implementation.>

The authors categorize different types of noise and discuss methods for rejecting each type of noise. They introduce a method called error eliminating rapid ultrasonic firing (EERUF). EERUF combines different noise rejection techniques and optimizes them for rapid firing. EERUF almost completely rejects crosstalk. Its unique noise rejection capability allows multiple mobile robots to collaborate in the same environment, even if their ultrasonic sensors operate at the same frequencies. For each noise category methods are described to identify and reject the resulting errors. These individual rejection measures were combined into one error rejection method which was then combined with a fast firing algorithm. The resulting combination was EERUF. The EERUF method was implemented on a mobile robot and experimental results are presented. With EERUF, a mobile robot was able to traverse an obstacle course of densely spaced, pencil-thin poles at up to 1 m/s.>

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.>

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.>

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.>

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 robot's 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.>

An index of performance (IOP) designed to quantitatively express the match between a sensor-built map and a precisely measured reference map is introduced. The IOP computes a single value representing the correlation between the sensed object positions in the grid and the actual object positions. With the IOP, it is easy to compare the accuracy of different map-building methods as well as the effect of different parameters within a certain method. Two grid-type map-building algorithms were compared by means of the proposed IOP. One algorithm takes panoramic snapshots while the mobile robot is standing and uses a probabilistic distribution to update the grid. The other algorithm, called histogramic in-motion mapping, is based on rapid sampling of the sensors during motion.>

A tour plan generator (TPG) specifically adapted for mobile robots is described. The TPG computes the itinerary of a tour passing through a number of locations. One typical constraint is that a single-armed mobile robot is usually unable to carry more than one object at a time in its gripper. This constraint requires the TPG to generate a tour in which each pick-up location is visited immediately prior to the corresponding drop-off location. A model that makes it possible to reduce this problem to a special case of the traveling salesman problem with asymmetric cost matrix and nonEuclidean distances is introduced. Another constraint in many mobile robots is the need to periodically visit a home location. The TPG introduced here automatically creates multiple subtours such that a predefined maximal length for each subtour is not exceeded. The algorithm obtains near-optimal solutions with short computation times by combining different heuristic tour-construction rules into a heuristic team approach.>

A novel technique for the remote guidance of fast mobile robots has been developed and implemented. With this method, the mobile robot follows the general direction prescribed by an operator. However, if the robot encounters an obstacle, it autonomously avoids collision with that obstacle while trying to match the prescribed direction as closely as possible. This novel implementation of shared control is completely transparent and transfers control between teleoperation and autonomous obstacle avoidance gradually. The method, called teleautonomous operation, allows the operator to steer vehicles and robots at high speeds and in cluttered environments, even without visual contact. Teleautonomous operation is based on the virtual force field (VFF) method, which was developed for autonomous obstacle avoidance. The VFF method is especially suited to the accommodation of inaccurate sensor data and sensor fusion, and allows the mobile robot to travel quickly without stopping for obstacles.>

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.>

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.>