Share Email Print
cover

Proceedings Paper

Probabilistic methods for robotic landmine search
Author(s): Yangang Zhang; Mark J. Schervish; Ercan Umut Acar; Howie M. Choset
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

One way to improve the efficiency of a mine search, compared with a complete coverage algorithm, is to direct the search based on the spatial distribution of the minefield. The key for the success of this probabilistic approach is to efficiently extract the spatial distribution of the minefield during the process of the search. In our research, we assume that a minefield follows a regular pattern, which belongs to a family of known patterns. Likelihood and Bayesian approaches to the pattern extraction algorithm are developed to extract the underlying pattern of the minefield. Both algorithms perform well in their ability to catch the "actual" pattern. And both algorithms are efficient, therefore, online implement of the algorithm on a mobile robot is possible. Compared to the likelihood approach, the advantage of using a Bayesian approach is that this approach provides information about the uncertainty of the extracted "actual" pattern.

Paper Details

Date Published: 2 March 2001
PDF: 12 pages
Proc. SPIE 4195, Mobile Robots XV and Telemanipulator and Telepresence Technologies VII, (2 March 2001); doi: 10.1117/12.417305
Show Author Affiliations
Yangang Zhang, Carnegie Mellon Univ. (United States)
Mark J. Schervish, Carnegie Mellon Univ. (United States)
Ercan Umut Acar, Carnegie Mellon Univ. (United States)
Howie M. Choset, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 4195:
Mobile Robots XV and Telemanipulator and Telepresence Technologies VII
Matthew R. Stein; Howie M. Choset; Douglas W. Gage; Matthew R. Stein, Editor(s)

© SPIE. Terms of Use
Back to Top