Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15817
Title: Segment-based map building using enhanced adaptive fuzzy clustering algorithm for mobile robot applications
Authors: Ip, YL
Rad, AB
Chow, KM
Wong, YK
Keywords: Fuzzy clustering
Mobile robots
Noise clustering
Segment-based map building
Issue Date: 2002
Publisher: Kluwer Academic Publ
Source: Journal of intelligent and robotic systems: theory and applications, 2002, v. 35, no. 3, p. 221-245 How to cite?
Journal: Journal of Intelligent and Robotic Systems: Theory and Applications 
Abstract: In this paper, we present a technique for on-line segment-based map building in an unknown indoor environment from sonar sensor observations. The world model is represented with two-dimensional line segments. The information obtained by the ultrasonic sensors is updated instantaneously while the mobile robot is moving through the workspace. An Enhanced Adaptive Fuzzy Clustering Algorithm (EAFC) along with Noise Clustering (NC) is proposed to extract and classify the line segments in order to construct a complete map for an unknown environment. Furthermore, to alleviate the problem of extensive computation associated with the process of map building, the workplace of the mobile robot is divided into square cells. A compatible line segment merging technique is then suggested to combine the similar segments after the extraction of the line segment by EAFC along with NC algorithm. The performance of the algorithm is demonstrated by experimental results on a Pioneer II mobile robot.
URI: http://hdl.handle.net/10397/15817
ISSN: 0921-0296
DOI: 10.1023/A:1021163807498
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

29
Last Week
0
Last month
0
Citations as of May 28, 2017

WEB OF SCIENCETM
Citations

21
Last Week
0
Last month
0
Citations as of May 28, 2017

Page view(s)

29
Last Week
2
Last month
Checked on May 28, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.