Task Planning For A Mobile Robot In An Indoor Environment Using Object-oriented Domain Information

For a mobile robot to be practical, it needs to navigate in dynamically changing environments and manipulate objects in the environment with operating ease. The main challenges to satisfying these requirements in mobile robot research include the collection of robot environment information, storage and organization of this information, and fast task planning based on available information. Conventional approaches to these problems are far from satisfactory due to their requirement of high computation time. In this paper, we specifically address the problems of storage and organization of the environment information and fast task planning in the area of robotic research. We propose an special object-oriented data model (OODM) for information storage and management in order to solve the first problem. This model explicitly represents domain knowledge and abstracts a global perspective about the robot's dynamically changing environment. To solve the second problem, we introduce a fast task planning algorithm that fully uses domain knowledge related to robot applications and to the given environment. Our OODM based task planning method presents a general frame work and representation, into which domain specific information, domain decomposition methods and specific path planners can be tailored for different task planning problems. This method unifies and integrates the salient features from various areas such as database, artificial intelligence, and robot path planning, thus increasing the planning speed significantly     

Two-dimensional Corrosion Pit Initiation and Growth Simulation Model

A two-dimensional corrosion initiation and growth model for aircraft aluminum materials is developed. The model takes into account the electro-chemical parameters as well as specific rules governing corrosion mechanisms. The simulation program is implemented in a cellular automata framework. The corrosion initiation and growth patterns obtained from simulations are compared qualitatively and quantitatively to the experimental data obtained from the Center for Materials Diagnostics at the University of Dayton Research Institute, Dayton. The results indicate that the present model effectively captures the corrosion damage process including initiation and growth. The effects of various electro-chemical parameters on the damage growth obtained from the simulation are presented and discussed. The results presented illustrate a new approach to modeling corrosion damage in aircraft aluminum materials. The simulation program is developed in a JAVA environment for ease of portability and usability.     

Genetic Sequence Classification and its Application to Cross-Species Homology Detection

Although large-scale classification studies of genetic sequence data are in progress around the world, very few studies compare different classification approaches, e.g. unsupervised and supervised, in terms of objective criteria such as classification accuracy and computational complexity. In this paper, we study such criteria for both unsupervised and supervised classification of a relatively large sequence data set. The unsupervised approach involves use of different sequence alignment algorithms (e.g., Smith-Waterman, FASTA and BLAST) followed by clustering using the Maximin algorithm. The supervised approach uses a suitable numeric encoding (relative frequencies of tuples of nucleotides followed by principal component analysis) which is fed to a Multi-layer Backpropagation Neural Network. Classification experiments conducted on IBM-SP parallel computers show that FASTA with unsupervised Maximin leads to best trade-off between accuracy and speed among all methods, followed by supervised neural networks as the second best approach. Finally, the different classifiers are applied to the problem of cross-species homology detection.     

Using Many-Sorted Logic in the Object-Oriented Data Model for Fast Robot Task Planning

Search space explosion is a critical problem in robot task planning. This problem limits current robot task planners to solve only simple block world problems and task planning in a real robot working environment to be impractical. This problem is mainly due to the lack of utilization of domain information in task planning. In this paper, we describe a fast task planner for indoor robot applications that effectively uses domain information to speed up the planning process. In this planner, domain information is explicitly represented in an object-oriented data model (OODM) that uses many-sorted logic (MSL) representation. The OODM is convenient for the management of complex data and many-sorted logic is effective for pruning in the rule search process. An inference engine is designed to take advantage of the salient features of these two techniques for fast task planning. A simulation example and complexity analysis are given to demonstrate the advantage of the proposed task planner.     

A bidding mechanism for Web-based agents involved in information classification

The impact of the World Wide Web on providing an easy information access is clearly evident in all aspects of today's life. As attractive as the information availability is, due to its sheer volume, it creates an information-overload on users. Agent-based collaborative filtering is a technique used to effectively counter this burden. For agents to collaborate successfully, and maintain an overall progress of the entire interconnected environment, a governing mechanism is necessary. In this article, we present an economic framework based on a bidding mechanism for agents to communicate and negotiate with each other, thereby achieving collaborative information classification. D-SIFTER, a system developed using this economic framework, is described along with various experiments and their results.