关键词: 静态障碍, 动态障碍, KL距离, 不确定数据, 网格

Abstract: In order to solve the problem that the existing research results can not effectively solve the problem of uncertain data clustering in the dynamic obstacle space, according to whether the set of obstacles is changed, this paper proposes the uncertain data clustering algorithms in static obstacle space and dynamic obstacle space. These uncertain data clustering algorithms include STA_GOBSCAN (DBSCAN clustering algorithm for static obstacles in grid space), DYN_GOCBSCAN (DBSCAN clustering algorithm for dynamic increase of obstacles in grid space), DYN_GORBSCAN (DBSCAN clustering algorithm for dynamic reduction of obstacles in grid space) and DYN_ GOMBSCAN (DBSCAN clustering algorithm for dynamic movement of obstacles in grid space). The KL distance is used to measure the similarity of uncertain data, and the data space is divided by the grid. Theoretical research and experimental results show that the algorithms proposed in this paper have extremely high efficiency and accuracy.

Key words: static obstacles, dynamic obstacles, Kullback-Leibler divergence, uncertain data, grid