关键词: 自主定位, 布谷鸟搜索, 模糊C均值, 聚类分析, 收敛精度

Abstract:

Aiming at the problem of autonomous positioning of plume source in 3D space, this paper introduces the historical data mechanism and proposes an autonomous positioning strategy based on the cuckoo search algorithm combined with the improved fuzzy C-means clustering algorithm. The cuckoo search algorithm is used to generate the position information of the robot positioning, which avoids the blindness of the robot collecting the plume concentration and realizes the autonomy of the positioning. The generated position information and the collected smoke plume concentration at the position constitute the feature vector, and the improvement is adopted. The fuzzy C-means clustering algorithm clusters a new set of eigenvectors composed of the eigenvectors and historical eigenvectors to obtain a three-dimensional space plume concentration distribution area, which provides a search range for the cuckoo search algorithm. The proposed method is verified by an example and compared with the positioning algorithm of the last two years. The results show that the method is better than the two-year positioning algorithm in terms of average running time and convergence precision, and can average 0.1450 meters. The convergence accuracy is autonomously located near the plume source, providing a methodological support for the plume source location.

Key words: autonomous positioning, cuckoo search, fuzzy C-means, cluster analysis, convergence accuracy