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Proceedings Paper

Soft computing-based terrain visual sensing and data fusion for unmanned ground robotic systems
Author(s): Amir Shirkhodaie
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Paper Abstract

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

Paper Details

Date Published: 9 May 2006
PDF: 12 pages
Proc. SPIE 6229, Intelligent Computing: Theory and Applications IV, 62290D (9 May 2006); doi: 10.1117/12.673118
Show Author Affiliations
Amir Shirkhodaie, Tennessee State Univ. (United States)


Published in SPIE Proceedings Vol. 6229:
Intelligent Computing: Theory and Applications IV
Kevin L. Priddy; Emre Ertin, Editor(s)

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