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Proceedings Paper

Flexible integration of path-planning capabilities
Author(s): Iain C. Stobie; Milind Tambe; Paul S. Rosenbloom
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Paper Abstract

Robots pursuing complex goals must plan paths according to several criteria of quality, including shortness, safety, speed and planning time. Many sources and kinds of knowledge, such as maps, procedures and perception, may be available or required. Both the quality criteria and sources of knowledge may vary widely over time, and in general they will interact. One approach to address this problem is to express all criteria and goals numerically in a single weighted graph, and then to search this graph to determine a path. Since this is problematic with symbolic or uncertain data and interacting criteria, we propose that what is needed instead is an integration of many kinds of planning capabilities. We describe a hybrid approach to integration, based on experiments with building simulated mobile robots using Soar, an integrated problem-solving and learning system. For flexibility, we have implemented a combination of internal planning, reactive capabilities and specialized tools. We illustrate how these components can complement each other's limitations and produce plans which integrate geometric and task knowledge.

Paper Details

Date Published: 4 May 1993
PDF: 10 pages
Proc. SPIE 1831, Mobile Robots VII, (4 May 1993); doi: 10.1117/12.143831
Show Author Affiliations
Iain C. Stobie, Univ. of Southern California (United States)
Milind Tambe, Carnegie Mellon Univ. (United States)
Paul S. Rosenbloom, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 1831:
Mobile Robots VII
William J. Wolfe; Wendell H. Chun, Editor(s)

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