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Proceedings Paper

Robot map building based on fuzzy-extending DSmT
Author(s): Xinde Li; Xinhan Huang; Zuyu Wu; Youlun Xiong
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Paper Abstract

With the extensive application of mobile robots in many different fields, map building in unknown environments has been one of the principal issues in the field of intelligent mobile robot. However, Information acquired in map building presents characteristics of uncertainty, imprecision and even high conflict, especially in the course of building grid map using sonar sensors. In this paper, we extended DSmT with Fuzzy theory by considering the different fuzzy T-norm operators (such as Algebraic Product operator, Bounded Product operator, Einstein Product operator and Default minimum operator), in order to develop a more general and flexible combinational rule for more extensive application. At the same time, we apply fuzzy-extended DSmT to mobile robot map building with the help of new self-localization method based on neighboring field appearance matching( -NFAM), to make the new tool more robust in very complex environment. An experiment is conducted to reconstruct the map with the new tool in indoor environment, in order to compare their performances in map building with four T-norm operators, when Pioneer II mobile robot runs along the same trace. Finally, a conclusion is reached that this study develops a new idea to extend DSmT, also provides a new approach for autonomous navigation of mobile robot, and provides a human-computer interactive interface to manage and manipulate the robot remotely.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67870Y (15 November 2007); doi: 10.1117/12.748884
Show Author Affiliations
Xinde Li, Huazhong Univ. of Science and Technology (China)
Xinhan Huang, Huazhong Univ. of Science and Technology (China)
Zuyu Wu, Huazhong Univ. of Science and Technology (China)
Youlun Xiong, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 6787:
MIPPR 2007: Multispectral Image Processing

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