基于模糊信息分解与控制规则的移动机器人沿墙导航

doi:10.11896/JsJkx.191000158

摘要/Abstract

摘要： 由于机器人导航任务对实时性要求高,以及机器人自身的非线性导致很难精确建模,而基于规则的控制可解释性好,可以实时响应。因此,文中提出了一种基于模糊信息分解(Fuzzy-based Information Decomposition,FID)与控制规则的机器人沿墙导航方法。在UCI机器人导航数据集上,首先用FID对原始类别不平衡数据集进行过采样,之后训练支持向量机(SVM),然后从SVM中提取控制规则。在提取规则过程中,仅使用支持向量以减少规则数量和提高实时性,使用这些支持向量训练随机森林,然后从中提取控制规则。实验结果表明,在相同数据集上,相较于决策树等6个经典模型,所提方法的平均F1值为0.994,对小类样本的召回率平均提升8.09%。与其他提取规则的模型相比,从SVM中提取规则的方法能平均减少171.33条规则,在测试样本上的平均单个样本决策时间仅为3.145μs。

关键词: 控制规则, 类别不平衡, 模糊信息分解, 支持向量机

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

中图分类号:

TP242.6

方梦琳, 唐文兵, 黄鸿云, 丁佐华. 基于模糊信息分解与控制规则的移动机器人沿墙导航[J]. 计算机科学, 2020, 47(6A): 79-83. https://doi.org/10.11896/JsJkx.191000158

FANG Meng-lin, TANG Wen-bing, HUANG Hong-yun and DING Zuo-hua. Wall-following Navigation of Mobile Robot Based on Fuzzy-based Information Decomposition and Control Rules[J]. Computer Science, 2020, 47(6A): 79-83. https://doi.org/10.11896/JsJkx.191000158

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