Classification-based learning by particle swarm optimization for wall-following robot navigation

Authors:
Yen-Lun Chen;Jun Cheng;Chuan Lin;Xinyu Wu;Yongsheng Ou;Yangsheng Xu
Affiliations:
Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China and Department of Mechanical and Automation E ...;Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China and Department of Mechanical and Automation E ...;Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China and Department of Mechanical and Automation E ...
Venue:
Neurocomputing
Year:
2013

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Abstract

In this paper, we study the parameter setting for a set of intelligent multi-category classifiers in wall-following robot navigation. Based on the swarm optimization theory, a particle selecting approach is proposed to search for the optimal parameters, a key property of this set of multi-category classifiers. By utilizing the particle swarm search, it is able to obtain higher classification accuracy with significant savings on the training time compared to the conventional grid search. For wall-following robot navigation, the best accuracy (98.8%) is achieved by the particle swarm search with only 1/4 of the training time by the grid search. Through communicating the social information available in particle swarms in the training process, classification-based learning can achieve higher classification accuracy without prematurity. One of such learning classifiers has been implemented in SIAT mobile robot. Experimental results validate the proposed search scheme for optimal parameter settings.