Design and low cost implementation of a fuzzy logic controller for wall following behavior of a mobile robot

Farooq, Umar; Khalid, Ammara; Amar, Moty; Habiba, Aleena; Shafique, Shareen; Noor, Rabia

doi:10.1109/icsps.2010.5555781

Cited by 14 publications

(14 citation statements)

References 8 publications

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“…In order to improve the performance of WFR, the closed-loop controller, FLC is used. Essentially, FLC processes the input membership function and makes decision with respect to the proper output based on the rulebased system [3,4,15,16,17,19,26,27]. There have been many products applying the principle of FLC, such as in the case of adjusting the air conditioner based on the actual temperature, pressing the pedal of an automatic car based on the crowds around, and adjusting the heat based on the food maturity placed on the barbeque cooker.…”

Section: Methodsmentioning

confidence: 99%

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An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

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The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.

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Section: Methodsmentioning

confidence: 99%

“…The main task of WFR is to maintain the distance into the setpoint position safely, commonly-termed as the expected track, when it moves [1,2,3,4,5,6]. It is expected to have the ability to make decisions in the form of movements that match the contours of the ever-changing walls.…”

Section: Introductionmentioning

confidence: 99%

An FLC-Pso Algorithm-Controlled Mobile Robot

Suwoyo

Tian

Hajar

2020

Sinergi

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“…constante Errores 0; creciente Derivada +; deS4 (5) Para éste comportamiento las variables lingüísticas fueron particionadas en tres conjuntos difusos por variables, las cuales se expresan como {eS4_(Nt, Zr, Pt) y deS4_(Nt, Zr, Pt)} donde e: error; de: derivada; Nt: Negativo, Zr: Zero, Pt: Positivo. Este comportamiento fue diseñado para controlar solo el motor derecho (ver Fig.…”

Section: B Seguimiento De Pared Derechaunclassified

Coordination Scheme and Behavior Fusion Based on Fuzzy Weighting Factors for a Mobile Robotic Navigation

Zapata-Cortes¹,

Amaya²,

Builes³

2015

IEEE Latin Am. Trans.

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This paper presents a behavior fuzzy coordination scheme for a mobile robot platform. The control architecture is composed of three basic behaviors: obstacle avoidance, left wall following and right wall following. The mechanism of fusion behavior was performed on a weighting system that acquires information from the environment and calculates fuzzy dynamic weighting factors adjusted to the region in which the robot travels. These factors influence the output of each behavior giving strength to the most appropriate action according to the current situation of the environment. Subsequently, the weighted outputs of each behavior are normalized by the command fusion, achieving a cooperative solution to meeting the robot navigation target.Keywords-fuzzy control, behavior based fuzzy control architecture, mobile robotic, obstacle avoidance, tracking behavior, behavior coordination, artificial intelligence. I. INTRODUCCIÓNNA de las estrategias de solución para el problema de la navegación local en robots móviles ha sido la implementación de esquemas de control basado en comportamientos. En estos se han implementado comportamientos tales como la evasión de obstáculos [1]-[4], seguimiento de paredes [5]-[7] y búsqueda de metas [8], [9] los cuales ayudan a cumplir la tarea de navegación del robot.Cuando se implementan varios de estos comportamientos en un robot móvil surge el problema de la selección, el cual fue planteado por Pattie Maes [10], [11] y se define en cómo un agente robótico debe seleccionar la acción más relevante cuando se encuentra en un estado particular del entorno.Una estrategia inicial a la solución de este problema fue propuesta por Brooks [12],[13] mediante un esquema de jerarquización de los comportamientos en el cual, para una percepción en particular del entorno, el comportamiento de mayor jerarquía subsume o anula aquellos de menor jerarquía. La arquitectura propuesta por Brooks ha sido ampliamente implementada en sistemas robóticos y se conoce como la arquitectura de subsunción. Esta arquitectura se clasifica como un método competitivo y se constituye como el método más simple para la coordinación de comportamientos, dando una rápida respuesta; sin embargo presenta desventajas tales como

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“…A great deal of research has focused on the navigation problem, obstacle avoidance, wall following, and intelligent parking [1][2][3][4][5][6]. To reach the desired goals, mobile robots use different kinds of sensors to collect environmental information and a set of actuators for their motion and reaction.…”

Section: Introductionmentioning

confidence: 99%

Design and test of a new development FPGA board for mobile robot research

Naji¹,

Abdelmoula²,

Abbes³

et al. 2017

Turk J Elec Eng & Comp Sci

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Abstract:This paper presents the design and testing of a newly developed field programmable gate array (FPGA) board for mobile robot research. The idea consists of interfacing the FPGA to a 16-bit digital signal controller (dsPIC). This idea enables the FPGA designs to communicate with the dsPIC through the integration of per-written driver modules to be used in a wide variety of digital and analog peripheral modules. It also allows the use of a high-performance digital signal processing core in many signal processing-based mobile robot applications. The proposed FPGA board allows users to develop, test, and deploy quick and cost-effective complete design solutions to control the entire robot system in various applications without the need for external components.

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Design and low cost implementation of a fuzzy logic controller for wall following behavior of a mobile robot

Cited by 14 publications

References 8 publications

An FLC-Pso Algorithm-Controlled Mobile Robot

An FLC-Pso Algorithm-Controlled Mobile Robot

Coordination Scheme and Behavior Fusion Based on Fuzzy Weighting Factors for a Mobile Robotic Navigation

Design and test of a new development FPGA board for mobile robot research

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