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

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Abstract

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.

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Authors and Affiliations

  1. Department of Electrical Engineering, Purdue University, School of Engineering and Technology at Indianapolis, 723 W. Michigan Street, Indianapolis, IN, 46202, USA

    Y. P. Chien

  2. Department of Computer and Information Science, Purdue University, School of Science at Indianapolis, 723 W. Michigan Street, Indianapolis, IN, 46202, USA

    Anand Hudli & Mathew Palakal

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  1. Y. P. Chien
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  2. Anand Hudli
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  3. Mathew Palakal
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Chien, Y.P., Hudli, A. & Palakal, M. Using Many-Sorted Logic in the Object-Oriented Data Model for Fast Robot Task Planning. Journal of Intelligent and Robotic Systems 23, 1–25 (1998). https://doi.org/10.1023/A:1008021418835

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  • DOI: https://doi.org/10.1023/A:1008021418835

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