Fuzzy based obstacle avoidance system for autonomous mobile robot

Rusu, C.; Birou, Iulian; Szoke, E.

doi:10.1109/aqtr.2010.5520862

Cited by 19 publications

(9 citation statements)

References 2 publications

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“…Esta configuración es de fácil aplicación, pero al mismo tiempo puede ser difícil de manejar por la necesidad de controlar las ruedas de propulsión adecuadamente [10] y [14]. Otra de las configuraciones es la llamada Ackermann, que está basada en el movimiento de los automóviles comerciales, esto es, la parte trasera otorga la propulsión del sistema mientras que la parte delantera proporciona la dirección, el inconveniente de este sistema es el no poder el girar sobre su mismo eje.…”

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“…Otra de las configuraciones es la llamada Ackermann, que está basada en el movimiento de los automóviles comerciales, esto es, la parte trasera otorga la propulsión del sistema mientras que la parte delantera proporciona la dirección, el inconveniente de este sistema es el no poder el girar sobre su mismo eje. [14].…”

Section: B Trabajo Previounclassified

“…Una configuración omnidireccional tiene ruedas construidas con rodillos que se pueden desplazar libremente. Para utilizarlas en una plataforma de 3 puntos de apoyo, las ruedas deben de tener rodillos de 90 grados mientras una plataforma de 4 puntos de apoyo debe de tener rodillos colocados a 45 grados, al accionar cada rueda de manera independiente permite a la plataforma moverse en forma de dirección omnidireccional, el inconveniente que tiene este tipo de rueda es la dificulta de manejo en línea recta y una difícil implementación del sistema de control de desplazamiento [14]. Para el control de desplazamiento omnidireccional, existen varias técnicas, una de ellas es la lógica difusa, que pretende subsanar la imprecisión del razonamiento humano.…”

Section: B Trabajo Previounclassified

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Fuzzy Logic for Omni directional Mobile Platform Control Displacement using FPGA and Bluetooth

Peña

Gómez

Osorio-Comparán

et al. 2015

IEEE Latin Am. Trans.

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The article shows an omnidirectional mobile platform control using the artificial intelligence techinique of Fuzzy Logic; the control allows a practical and reliable driving control of 4 omnidirectional wheels. The control module is implemented in FPGA allowing having an independent and autonomous single chip system out of a central computer dependence to be used in different applications like service robots platforms. An additional feature is performed by using Bluetooth communication with a cellular phone as the handset control device. Driving movements for the mobile platform is limited for eight directions, a Fuzzy Logic module controls the travelling of the platform with independent movements for each wheel, physical feedback is implemented by using electronic decoders, and experimental results were achieved with an additional feature of a handset device control based on a smartphone OS Android with Bluetooth communication. Not using external software and having a system using an artificial intelligence technique implemented in fast hardware, gives the system robust and reliable control capabilities.

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Section: B Trabajo Previounclassified

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Fuzzy Logic for Omni directional Mobile Platform Control Displacement using FPGA and Bluetooth

Peña

Gómez

Osorio-Comparán

et al. 2015

IEEE Latin Am. Trans.

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“…In this study, the genetic algorithm is employed to adjust the fuzzy membership function and weight of the neural network. Fuzzy PWM (Pulse Width Modulation) controller has been presented in the article [41] for mobile robot navigation and obstacle avoidance in an unknown environment. Abdessemed et al [42] have designed an evolutionary algorithm to optimize the antecedent and consequent parameters of the fuzzy controller, and implemented it for mobile robot path planning.…”

Section: Hybridization Of Fuzzy and Nondeterministic Algorithmmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

186

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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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“…A collision avoidance mechanism for mobile robot navigating in unknown environments based on a dynamic recurrent neuro-fuzzy system (DRNFS), proposed by Chen and Richardson [9]. Multi-sensor fusion technology based collision avoidance approximation with ultrasonic and infrared sensors was used in [10], [11]. A novel sensor data methodology for mobile robots in unknown environments was conceived in [12].…”

Section: Related Workmentioning

confidence: 99%

Collision-Free Mobile Robot navigation using Fuzzy Logic Approach

Dirik¹

2018

IJCA

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Autonomous mobile robots' navigation has become a very popular and interesting topic of computer science and robotics in the last decade. Many algorithms have been developed for robot motion control in an unknown (indoor/outdoor) and in various environments (static/dynamic). Fuzzy logic control techniques are an important algorithm developed for robot navigation problems. The aim of this research is to design and develop a fuzzy logic controller that enables the mobile robot to navigate to a target in an unknown environment, using WEBOTS commercial mobile robot simulation and MATLAB software. The algorithm is divided into two stages; In the first stage, the mobile robot was made to go to the goal, and in the second stage, obstacle avoidance was realized. Robot position information (x, y, Ø) was used to move the robot to the target and six sensors data were used during the obstacle avoidance phase. The used mobile robot (E_PUCK) is equipped with 12 IR sensors to measure the distance to the obstacles. The fuzzy control system is composed of six inputs grouped in doubles which are left, front and right distance sensors two outputs which are the mobile robot's left and right wheel speeds. To check the simulation result for proposed methodology, WEBOTS simulator and MATLAB software were used. To modeling the environment in different complexity and design, this simulator was used. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for autonomous mobile robots and the objective of this research has been successfully achieved. This research also indicated that WEBOT and MATLAB are suitable tools that could be used to develop and simulate mobile robot navigation system.

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Fuzzy based obstacle avoidance system for autonomous mobile robot

Cited by 19 publications

References 2 publications

Fuzzy Logic for Omni directional Mobile Platform Control Displacement using FPGA and Bluetooth

Fuzzy Logic for Omni directional Mobile Platform Control Displacement using FPGA and Bluetooth

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Collision-Free Mobile Robot navigation using Fuzzy Logic Approach

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