Probabilistic methods for robotic landmine search

Yangang Zhang; Mark J. Schervish; Ercan Umut Acar; Howie M. Choset

doi:10.1117/12.417305

2 March 2001 Probabilistic methods for robotic landmine search

Yangang Zhang, Mark J. Schervish, Ercan Umut Acar, Howie M. Choset

Proceedings Volume 4195, Mobile Robots XV and Telemanipulator and Telepresence Technologies VII; (2001) https://doi.org/10.1117/12.417305
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States

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.

Citation Download Citation

Yangang Zhang, Mark J. Schervish, Ercan Umut Acar, and Howie M. Choset "Probabilistic methods for robotic landmine search", Proc. SPIE 4195, Mobile Robots XV and Telemanipulator and Telepresence Technologies VII, (2 March 2001); https://doi.org/10.1117/12.417305

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