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Robotics 2014, 3(4), 349-370; doi:10.3390/robotics3040349

Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation

Department of Engineering and Design, University of Sussex, Famer, Brighton, BN1 9QJ, UK
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Received: 21 August 2014 / Revised: 10 November 2014 / Accepted: 13 November 2014 / Published: 19 November 2014

Abstract

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles. View Full-Text
Keywords: ANFIS; autonomous ground vehicle; navigation; obstacle avoidance; static environment ANFIS; autonomous ground vehicle; navigation; obstacle avoidance; static environment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Al-Mayyahi, A.; Wang, W.; Birch, P. Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation. Robotics 2014, 3, 349-370.

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