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CLC number: TP242.6

On-line Access: 2019-03-11

Received: 2017-03-25

Revision Accepted: 2018-01-25

Crosschecked: 2019-02-15

Cited: 0

Clicked: 1321

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Kil To Chong

http://orcid.org/0000-0002-1952-0001

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.2 P.141-151

10.1631/FITEE.1700206


Adaptive network fuzzy inference system based navigation controller for mobile robot


Author(s):  Panati Subbash, Kil To Chong

Affiliation(s):  Department of Electronics and Information Engineering, Chonbuk National University, Jeonju 54896, Republic of Korea; more

Corresponding email(s):   kitchong@jbnu.ac.kr

Key Words:  Adaptive network fuzzy inference system, Additive white Gaussian noise, Autonomous navigation, Mobile robot


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Panati Subbash, Kil To Chong. Adaptive network fuzzy inference system based navigation controller for mobile robot[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 141-151.

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Abstract: 
autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system (ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.

基于自适应网络模糊推理系统的移动机器人导航控制器

摘要:在障碍物高度杂乱的未知环境中自主导航是移动机器人研究的一个基本问题。提出一种基于自适应网络模糊推理系统(ANFIS)的差分驱动移动机器人导航控制器,用超声波传感器捕捉移动机器人周围的环境信息。设计了一个基于模糊逻辑的导航控制器,用于获取数据集训练ANFIS控制器。在移动机器人导航过程中,考虑到环境噪声对传感器读数的影响,将加性高斯白噪声添加到传感器读数中并反馈给已训练的ANFIS控制器。在3种不同环境下对移动机器人进行导航,评价该导航控制器的鲁棒性。通过与已有移动机器人导航控制器(如神经网络、模糊逻辑)比较行程长度、行程效率、弯曲能量,验证ANFIS控制器性能。仿真结果表明,与其他控制器相比,ANFIS控制器具有更好性能,能够在不同环境中顺利导航且不与障碍物发生碰撞。

关键词:自适应网络模糊推理系统;加性高斯白噪声;自主导航;移动机器人

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