Wall-following Navigation of Mobile Robot Based on Fuzzy-based Information Decomposition and Control Rules
Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 79-83.doi: 10.11896/JsJkx.191000158

• Artificial Intelligence • Previous Articles     Next Articles

Wall-following Navigation of Mobile Robot Based on Fuzzy-based Information Decomposition and Control Rules

FANG Meng-lin1, TANG Wen-bing1, HUANG Hong-yun2 and DING Zuo-hua1   

  1. 1 School of Information Science and Technology, ZheJiang Sci-Tech University, Hangzhou 310018, China
    2 Center of Multimedia Big Data of Library, ZheJiang Sci-Tech University, Hangzhou 310018, China
  • Published:2020-07-07
  • About author:FANG Meng- lin, born in 1994, postgraduate.Her current research interests include robot intelligent control and machine learning.
  • Supported by:
    This work was supported by the National National Natural Science Foundation of China (61210004,61170015).

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Abstract: Due to the high real-time requirements of robot navigation task and the nonlinearity of the robot itself,it is difficult to model accurately,and the rule-based control has good interpretability and real-time response generally.Therefore,a method of robot wall-following navigation based on fuzzy-based information decomposition (FID) and control rules is proposed.In UCI robot navigation data set,the original classimbalanced data set is over-sampled by FID,and then SVM is trained,and control rules are extracted from SVM.In the process of extracting rules,only support vectors are used to reduce the number of rules and improve the real-time performance.These support vectors are used to train the random forest,which is applied to extract control rules.The experimental results show that,on the same data set,the average F1 score of the proposed method is 0.994,and the recall rate of the minority class increases by 8.09% on average,compared with the six classic models such as decision tree.Compared with other rule extraction models,the rule extraction method from SVM can reduce 171.33 rules on average,and the average decision time per sample on the test sample is only 3.145μs.

Key words: Class imbalance, Control rules, Fuzzy-based information decomposition, Support vector machine

CLC Number: 

  • TP242.6
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