<title>Probabilistic methods for robotic landmine search</title>

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.