Abstract: We describe a fuzzy-based approach to self localization to support indoor robot navigation. Our approach is perception-based: clues extracted by the perceptual apparatus are matched against an approximate map to obtain an estimate of the robot's location in the map. Each perceptual clue is treated as a source of partial locational information, represented by a fuzzy set; other sources, like odometry or external measurements, are also treated in this way. Information coming from different sources is combined using a fuzzy aggregation operator. We illustrate our approach by showing experiments performed on a mobile robot, Flakey.

Abstract: Landmines are easy to lay but difficult to find and destroy. They are blind killers that should be forbidden as soon as possible. Nevertheless, there are about 100 million mines to be removed, and as many ready to be dispersed by hand or helicopter. A population of lightweight, low-cost, semi-autonomous robots is a clear answer to the problem of demining vast condemned areas. They will work together under close supervision of a monitoring station. They are named Pemex for "PErsonal Mine EXplorers" and this paper focuses on the original design and navigation of the 2-wheel Pemex-B. The Pemex-B robot searches for mines as a dog would. Ground pressure is low enough not to make the mine explode. It has to scan a large area, covering every square foot. In order to accelerate the exploration and get the best efficiency from the surveillance team, several robots have to be used. There are three levels of control: local scan; navigation (GPS and odometry) and collective behaviour (radio coordination).

Abstract: This paper describes practical, effective approaches to stereo perception and dead reckoning, and presents results from systems implemented for a prototype lunar rover operating in natural, outdoor environments. The stereo perception hardware includes a binocular head mounted on a motion-averaging mast. This head provides images to a normalized correlation matcher, that intelligently selects what part of the image to process (saving time), and subsamples the images (again saving time) without subsampling disparities (which would reduce accuracy). The implementation has operated successfully during long-duration field exercises, processing streams of thousands of images. The dead reckoning approach employs encoders, inclinometers, a compass, and a turn-rate sensor to maintain the position and orientation of the rover as it traverses. The approach integrates classical odometry with inertial guidance. The implementation succeeds in the face of significant sensor noise by virtue of sensor modelling, plus extensive filtering. The stereo and dead reckoning components are used by an obstacle avoidance planner that projects a finite number of arcs through the terrain map, and evaluates the traversability of each arc to choose a travel direction that is safe and effective. With these components integrated into a complete navigation system, a prototype rover has traversed over 1 km in lunar-like environments.

Abstract: We consider the problem of consistent range data registration in modeling an unknown environment. The problem is expressed as the optimal estimation of pose variables under the maximum likelihood criterion. By treating all the history of robot poses as variables and solving them simultaneously, consistency is enforced. We formulate relative pose constraints from both matched scans and odometry measurements to construct a network of measurements. Then we derive closed-form pose estimates as well as their covariance matrices. Examples of global scan registration using both real and simulated data are presented.

Abstract: The usual method for position estimation of a wheeled mobile robot is odometry. However, it has the problem of gradual error accumulation when the robot moves. To solve this problem, we place several reflectors sparsely in the robot's work-space as landmarks, so that the robot can correct its estimated position when detecting the landmark. The new estimated position of the robot is calculated by the maximum likelihood estimation (MLE) by both the position information estimated by odometry and the reflector detecting sensor system (ReDS) using the laser beam. ReDS, which we developed, can detect the reflector at most 3 meters away from the robot in the indoor environment. Practical experiments show that the estimated position obtained by this system is precise enough to be useful.

Abstract: In this paper an approach to real-time position correction and environmental modeling based on odometry, ultrasonic sensing, structured light sensing and active stereo vision (bin- and trinocular) is presented. Odometry provides the robot with a position estimation and with the help of a model of the environment sensor perceptions can be matched to predictions. Ultrasonic sensing is capable of collision avoidance and obstacle detection and so enables navigation in simply structured environments. Model-based image processing allows detection and classification of natural landmarks in the stereo images uniquely. With only one observation the robot's position and orientation relative to the observed landmark is found precisely. This sensing strategy is used when high precision is necessary for the performance of the navigation task. Finally techniques are described that allow an automatic mapping of an unknown or only partially known environment.

Abstract: This paper describes a position estimation technique based on the fusion of data obtained by two independent subsystems in a mobile robot navigation context. The first subsystem is a self-location one composed of an onboard camera, an onboard image processing unit and artificial landmarks; the second one is a dead-reckoning subsystem based on odometry. The robot navigation system integrates the position estimation obtained by the vision subsystem with the position estimated by odometry using a Kalman filter framework.

Abstract: The traditional method for controlling the trajectories of an AGV in industrial environments is based on wire-guide systems. This paper describes a multi level architecture for mobile robots that integrates a high level mission planner, a real-time trajectory generator, and real-time motion control. The positioning accuracy is guaranteed by an active localization system that integrates in real-time the odometry measures giving position and orientation with high level resolution. This architecture allows the removal of the wire guidance system in AGV technology, and the introduction of an intelligent trajectory generation system at shop floor factory level.

Abstract: This technical report develops a simple statistical error model for estimating position and orientation of a mobile robot using odometry. Once the errors are characterised, other sensor data can be combined sensibly in the estimation of position, using the Extended Kalman Filter [Kleeman, 1992 #100; Jazwinski, 1970 #117]. A closed form error covariance matrix is developed for (i) straight lines and (ii) constant curvature arcs and (iii) turning about the centre of axle of the robot. Other paths can be composed of short segments of constant curvature arcs without great loss of accuracy. The model assumes that wheel distance measurement errors are exclusively random zero mean white noise. Systematic errors due to wheel radius and wheel base measurement are ignored, since these can be removed by calibration. Previous work on developing odometry covariance relies on incrementally updating the covariance matrix in small times steps. The approach taken here integrates the noise theoretically over the entire path length to produce simple closed form expressions, allowing efficient covariance matrix updating after the completion of path segments.

Abstract: Available sensors for robot navigation are unreliable and noisy. Therefore there is a need to employ different types of sensors to acquire the information required for navigation. We discuss the different problems associated with the integration of several sensors in a mobile platform and present the approach we have developed to tackle these problems. We consider the particular problem of a mobile platform navigating in a 2D environment with a priori knowledge of its map. Unknown obstacles are allowed. In this chapter we are concerned with the integration of inertial sensors, odometry, sonars and active vision for navigation in a mobile robot.

Abstract: The design for an intelligent wheelchair to assist disabled or elderly people, whilst they are in hospital is presented. The wheelchair is intended for those who are unable to drive a normal powered wheelchair. The design consists of a central navigation unit obtaining information from environment sensors, a positioning unit and a user control unit. The sensors proposed include ultrasonics, odometry, inclinometry, passive radio beacons, infrared range scanner and voice recognition.

Abstract: This paper introduces a method for measuring odometry errors in mobile robots and for expressing these errors quantitatively. When measuring odometry errors, one must distinguish between (1) systematic errors, which are caused by kinematic imperfections of the mobile robot (for example, unequal wheel-diameters), and (2) non-systematic errors, which may be caused by wheel slippage or irregularities of the floor. Systematic errors are a property of the robot itself, and they stay almost constant over prolonged periods of time, while non- systematic errors are a function of the properties of the floor. Our method, called the University of Michigan benchmark test (UMBmark), is especially designed to uncover certain systematic errors that are likely to compensate for each other (and thus, remain undetected) in less rigorous tests. This paper explains the rationale for the UMBmark procedure and explains the procedure in detail. Experimental results from different mobile robots are also presented and discussed. Furthermore, the paper proposes a method for measuring non-systematic errors, called extended UMBmark. Although the measurement of non-systematic errors is less useful because it depends strongly on the floor characteristics, one can use the extended UMBmark test for comparison of different robots under similar conditions.

Abstract: The author compiles everything a student or experienced developmental engineer needs to know about the supporting technologies associated with the rapidly evolving field of robotics. From the table of contents: Design Considerations * Dead Reckoning * Odometry Sensors * Doppler and Inertial Navigation * Typical Mobility Configurations * Tactile and Proximity Sensing * Triangulation Ranging * Stereo Disparity * Active Triangulation * Active Stereoscopic * Hermies * Structured Light * Known Target Size * Time of Flight * Phase-Shift Measurement * Frequency Modulation * Interferometry * Range from Focus * Return Signal Intensity * Acoustical Energy * Electromagnetic Energy * Optical Energy * Microwave Radar * Collision Avoidance * Guidepath Following * Position-Location Systems * Ultrasonic and Optical Position-Location Systems * Wall, Doorway, andCeiling Referencing * Application-Specific Mission Sensors