The research of obstacle detection based on AK-means clustering algprithm in crosscountry

Author

Youchun Xu ; Jian Cao ; Peng Jia ; Zufeng Zhang

Author_Institution

Mil. Transp. Univ., Tianjin, China

Volume

03

fYear

2012

fDate

Oct. 30 2012-Nov. 1 2012

Firstpage

1196

Lastpage

1199

Abstract

In order to obtain obstacle information in the cross-county environment for an unmanned ground vehicle (UGV), AK-means clustering algorithm is applied in four-layer laser radar data mining in this paper. The result of clustering serves as candidate obstacles. To overcome the false clustering due to vibration of UGV, weighted Euclidean distance is used to improve Davies-Bouldin Index (DBI). The experimental results show that the proposed obstacle detection algorithm is reliable and robust in low speed driving.

Keywords

collision avoidance; control engineering computing; data mining; mobile robots; optical radar; pattern clustering; AK-means clustering algorithm; DBI; Davies-Bouldin index; UGV vibration; cross-county environment; false clustering; four-layer laser radar data mining; low speed driving; obstacle detection; unmanned ground vehicle; weighted Euclidean distance; Clustering algorithms; Indexes; Laser radar; Radar detection; Radar measurements; Vehicles; AK-mean; cross-country; laser radar; obstacle detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-1855-6

Type

conf

DOI

10.1109/CCIS.2012.6664573

Filename

6664573