Approaches for Clustering Polygonal Obstacles

Abstract

Clustering a set of points in Euclidean space is a well-known problem having applications in pattern recognition, document image analysis, big-data analytics, and robotics. While there are a lot of research publications for clustering point objects, only a very few articles have been reported for clustering a distribution of obstacles. In this paper we examine the development of efficient algorithms for clustering a set of convex obstacles in the 2D plane. We present two approaches for developing efficient algorithms for extracting polygonal clusters. While the first method is based on using the nearest neighbor computation, the second one uses reduced visibility graph induced by polygonal obstacles. We also consider the extensions of the proposed algorithms for non-convex polygonal obstacles.