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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: 7150

Citations:  Bibtex RefMan EndNote GB/T7714


Kil To Chong


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


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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T1 - Adaptive network fuzzy inference system based navigation controller for mobile robot
A1 - Panati Subbash
A1 - Kil To Chong
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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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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