Probabilistic methods for robotic landmine search

Abstract

One way to improve the efficiency of 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. A Bayesian approach to the pattern extraction is developed to extract the underlying pattern of the minefield. The algorithm performs well in its ability to catch the "actual" pattern in the situation where placement and detector errors exist, and the algorithm is 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.