抄録
Path planning uses map together with its cost to plan a path that contains minimum cost. Usually Euclidian distance is used for map cost. In this paper, we propose two kind of maps, 1) 3D obstacle map and 2) human usage of free area. Observing human trajectories, and clustering their histories by modified k-means method, a) working area, b) pedestrian area, and c) pedestrian crossing area. Using those information, application to robot navigation is also shown.
