Please use this identifier to cite or link to this item: https://doi.org/10.1080/08839510590901930
Title: Intelligent sensor fusion and learning for autonomous robot navigation
Authors: Tan, K.C. 
Chen, Y.J.
Wang, L.F.
Liu, D.K.
Keywords: Data fusion
Engineering applications
Fuzzy control
Robotics
Issue Date: May-2005
Source: Tan, K.C.,Chen, Y.J.,Wang, L.F.,Liu, D.K. (2005-05). Intelligent sensor fusion and learning for autonomous robot navigation. Applied Artificial Intelligence 19 (5) : 433-456. ScholarBank@NUS Repository. https://doi.org/10.1080/08839510590901930
Abstract: This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment. Copyright © 2005 Taylor & Francis Inc.
Source Title: Applied Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/56359
ISSN: 08839514
DOI: 10.1080/08839510590901930
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

9
checked on Dec 5, 2017

WEB OF SCIENCETM
Citations

7
checked on Nov 4, 2017

Page view(s)

32
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


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