The PSEIKI Report—Version 2. Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System

A fundamental goal of computer vision is the development of systems capable of carrying out scene interpretation while taking into account all the available knowledge. In this report, we have focused on how the interpretation task may be aided by expected-scene information which, in most cases, would not be in registration with the perceived scene. In this report, we describe PSEIKI, a framework for expectation-driven interpretation of image data. PSEIKI builds abstraction hierarchies in image data using, for cues, supplied abstraction hierarchies in a scene expectation map. Hypothesized abstractions in the image data are geometrically compared with the known abstractions in the expected scene; the metrics used for these comparisons translate into belief values. The Dempster-Shafer formalism is used to accumulate beliefs for the synthesized abstractions in the image data. For accumulating belief values, a computationally efficient variation of Dempster’s rule of combination is developed to enable the system to deal with the overwhelming amount of information present in most images. This variation of Dempster’s rule allows the reasoning process to be embedded into the abstraction hierarchy by allowing for the propagation of belief values between elements at different levels of abstraction. The system has been implemented as a 2- panel, 5-level blackboard in OPS 83. This report also discusses the control aspects of the blackboard, achieved via a distributed monitor using the OPS83 demons and a scheduler. Various knowledge sources for forming groupings in the image data and for labeling such groupings with abstractions from the scene expectation map are also discussed

Vision-Guided Mobile Robot Navigation

This report discusses the use of vision feedback for autonomous navigation by a mobile robot in indoor environments. In particular, we have discussed in detail the issues of camera calibration and how binocular and monocular vision may be utilized for self-location by the robot. A noteworthy feature of monocular vision is that the camera image is compared with a CAD model of the interior of the hallways using the PSEIKI reasoning system; this reasoning system allows the comparison to take place at different levels of geometric detail

Knowledge-based control for robot self-localization

Autonomous robot systems are being proposed for a variety of missions including the Mars rover/sample return mission. Prior to any other mission objectives being met, an autonomous robot must be able to determine its own location. This will be especially challenging because location sensors like GPS, which are available on Earth, will not be useful, nor will INS sensors because their drift is too large. Another approach to self-localization is required. In this paper, we describe a novel approach to localization by applying a problem solving methodology. The term 'problem solving' implies a computational technique based on logical representational and control steps. In this research, these steps are derived from observing experts solving localization problems. The objective is not specifically to simulate human expertise but rather to apply its techniques where appropriate for computational systems. In doing this, we describe a model for solving the problem and a system built on that model, called localization control and logic expert (LOCALE), which is a demonstration of concept for the approach and the model. The results of this work represent the first successful solution to high-level control aspects of the localization problem

E-Learning: Case Studies in Web-Controlled Devices and Remote Manipulation

Chances are that distance learning will transparently extend colleges and institutes of education and could plausibly overtake and turn into a preferred choice of higher education, especially for adult and working students. The main idea in e-learning is to build adequate solutions that can assure educational training over the Internet, without requiring a personal presence at the degree offering institution. The advantages are immediate and of unique importance, to enumerate a few: Education costs can be reduced dramatically, both from a student's perspective and the institution's (no need for room and board, for example); The tedious immigration and naturalization issues common with international students are eliminated; The limited campus facilities, faculty members and course schedules an institution can offer are no longer a boundary; Working adults can consider upgrading skills without changing their lifestyles We are presenting through this material a sequence of projects developed at University of Bridgeport and than can serve well in distance learning education ranging from simple "hobby" style training to professional guidance material. The projects have an engineering / laboratory flavor and are being presented in an arbitrary order, topics ranging from vision and sensing to engineering design, scheduling, remote control and operation

Mobile robot positioning: Sensors and techniques

Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In search of a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This article provides a review of relevant mobile robot positioning technologies. The article 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 article 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 brevily, not all could be cited in this article. © 1997 John Wiley & Sons, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34938/1/2_ftp.pd

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Argumentation as a practical foundation for decision theory

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