Adaptive Neuro-Fuzzy Technique for Autonomous Ground  Vehicle Navigation

Al-Mayyahi, Auday; Wang, William Yang; Birch, Philip

doi:10.3390/robotics3040349

Cited by 51 publications

(27 citation statements)

References 16 publications

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“…Such a system is made of two controllers: the first one uses sensors positioned in the front of the vehicle to detect obstacles, while the second controller evaluates the difference between the heading and the target angle [6]. Furthermore, in Al-Mayyahi et al (2014), the authors used an adaptive neuro-fuzzy inference system for navigation purposes by fusing sensor information. Such a system was made of four controllers: two are used for angular velocity regulation for reaching the target position and the other two are used for obstacle avoidance [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, in Al-Mayyahi et al (2014), the authors used an adaptive neuro-fuzzy inference system for navigation purposes by fusing sensor information. Such a system was made of four controllers: two are used for angular velocity regulation for reaching the target position and the other two are used for obstacle avoidance [7][8][9][10]. Rajashekaraiah et al (2017) proposed the MATLAB/Simulink simulation environment as a powerful tool for implementing the PTEM algorithm (Probabilistic Threat Exposure Map) to improve the obstacle avoidance capability for moving and stationary obstacles [11].…”

Section: Introductionmentioning

confidence: 99%

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Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

2018

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Artificial intelligence is the ability of a computer to perform the functions and reasoning typical of the human mind. In its purely informatic aspect, it includes the theory and techniques for the development of algorithms that allow machines to show an intelligent ability and/or perform an intelligent activity, at least in specific areas. In particular, there are automatic learning algorithms based on the same mechanisms that are thought to be the basis of all the cognitive processes developed by the human brain. Such a powerful tool has already started to produce a new class of self-driving vehicles. With the projections of population growth that will increase until the year 2100 up to 11.2 billion, research on innovating agricultural techniques must be continued. In order to improve the efficiency regarding precision agriculture, the use of autonomous agricultural machines must become an important issue. For this reason, it was decided to test the use of the "Neural Network Toolbox" tool already present in MATLAB to design an artificial neural network with supervised learning suitable for classification and pattern recognition by using data collected by an ultrasonic sensor. The idea is to use such a protocol to retrofit kits for agricultural machines already present on the market.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

2018

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“…The proposed model received data from the sonar sensors to control the turning angle of CLMR. Al-Mayyahi et al [89] have applied ANFIS technique for autonomous ground vehicle (AGV) navigation. In this work, they have designed four ANFIS controllers to control the left and right angular velocities, and angle between the robot and target (heading angle).…”

Section: Neuro-fuzzy Technique For Mobile Robot Navigationmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

176

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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“…The relationships for kinematic model of the autonomous ground vehicle can be given as follows Al-Mayyahi et al (2014):…”

Section: Kinematic Modelmentioning

confidence: 99%

Design of Fractional-Order Controller for Trajectory Tracking Control of a Non-holonomic Autonomous Ground Vehicle

Al-Mayyahi

Wang

Birch

2015

J Control Autom Electr Syst

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(2016) Design of fractional-order controller for trajectory tracking control of a non-holonomic autonomous ground vehicle. Journal of Control, Automation and Electrical Systems, 27 (1). pp. 29-42. ISSN 2195-3880 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/57057/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract A robust control technique is proposed to address the problem of trajectory tracking of an autonomous ground vehicle (AGV). This technique utilizes a fractional-order proportional integral derivative (FOPID) controller to control a non-holonomic autonomous ground vehicle to track the behaviour of the predefined reference path. Two FOPID controllers are designed to control the AGV's inputs. These inputs represent the torques that are used in order to manipulate the implemented model of the vehicle to obtain the actual path. The implemented model of the non-holonomic autonomous ground vehicle takes into consideration both of the kinematic and dynamic models. In additional, a particle swarm optimization (PSO) algorithm is used to optimize the FOPID controllers' parameters. These optimal tuned parameters of FOPID controllers minimize the cost function used in the algorithm. The effectiveness and validation of the proposed method have been verified through different patterns of reference paths using MATLAB-Simulink software package. The stability of fractional-order system is analysed. Also, the robustness of the system is conducted by adding disturbances due to friction of wheels during the vehicle motion.The obtained results of FOPID controller show the advantage and the performance of the technique in terms of minimizing path tracking error and the complement of the path following.B Auday Al-Mayyahi

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Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation

Cited by 51 publications

References 16 publications

Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Design of Fractional-Order Controller for Trajectory Tracking Control of a Non-holonomic Autonomous Ground Vehicle

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