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Using many-sorted logic in the object-oriented data model for fast robot task planning
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DOI：10.1023/A:1008021418835.png)
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摘要
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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.
Keyword:
robot task planning
object-oriented data model
many-sorted logic
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