A Fuzzy Behavior-Based Architecture for Mobile Robot Navigation in Unknown Environments

Qing-yong, Bao; Li, Shunming; Weiyan, Shang; An, Mujin

doi:10.1109/aici.2009.125

Cited by 34 publications

(12 citation statements)

References 9 publications

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“…Objects from 0-inches to 6-inches range as 6-inches. The interface output formats included are pulse width output, analog voltage output, and serial digital output [4].…”

Section: B Ultrasonic Sensor (Maxsonar-ez1)mentioning

confidence: 99%

Development of Autonomous Intelligent Vehicle (AIV) Based on Voting Technique

Ghazali¹,

Isa²,

Ibrahim³

et al. 2014

IJMMM

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Abstract-This project is to develop an autonomous intelligent vehicle using voting technique. The technique is depending on goal-seeking behavior and obstacles avoidance behavior. An intelligent robot usually achieves the goal point safely without hitting any obstacles. The robot needs to consider about safety and goal directedness. Therefore, the voting technique becomes one of the approaches to overcome this problem. The voting technique takes an approach where multiple modules concurrently share control of the robot. It uses a scheme where each behavior votes for a discrete set of motor command, with vote zero is the least desired and vote one is the most desired action. This will solve the action of selection problem.

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“…Objects from 0-inches to 6-inches range as 6-inches. The interface output formats included are pulse width output, analog voltage output, and serial digital output [4].…”

Section: B Ultrasonic Sensor (Maxsonar-ez1)mentioning

confidence: 99%

Development of Autonomous Intelligent Vehicle (AIV) Based on Voting Technique

Ghazali¹,

Isa²,

Ibrahim³

et al. 2014

IJMMM

View full text Add to dashboard Cite

show abstract

“…where and are coordinates of the mass center of the mobile robot, is the angle that represents the current orientation of the robot, V and are linear and angular velocities of the robot, and are angular velocities of right and left wheels, is the wheels radius, and is the distance between the two wheels centers. Combining (5) and (6)…”

Section: Kinematic Model Of Mobile Robotmentioning

confidence: 99%

“…The robot now is moving in the environment with obstacles. The initial position is also ( , ) = (5,5) and the final position is (25, 25).…”

Section: Simulation In the Environment With Obstaclesmentioning

confidence: 99%

“…Under the standard behavior paradigm, each behavior triggers its own specific control command according to the behavior. The behavior control architecture can handle the navigation problem in an online manner and it does not require environment model; thus, it has been widely used for mobile robot navigation [3][4][5]. Fuzzy logic control (FLC) [6] is the most important method for robot behaviorbased control and has been investigated for mobile robot navigation and obstacle avoidance by many researchers, see [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Behavior-Based Fuzzy Control for Mobile Robot Navigation

Tang

Meng

2013

Mathematical Problems in Engineering

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A new behavior-based fuzzy control method for mobile robot navigation is presented. It is based on behavioral architecture which can deal with uncertainties in unknown environments and has the ability to accommodate different behaviors. Basic behaviors are controlled by specific fuzzy logic controllers, respectively. The proposed approach qualifies for driving a robot to reach a target while avoiding obstacles in the environment. Simulation and experiments are performed to verify the correctness and feasibility of the proposed method.

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“…They developed the Gradient method to optimize the variables of the membership functions of the Sugeno fuzzy controller. In [19], authors presented a behavior-based control using fuzzy logic controller for mobile robot navigation in an obscure environment. They designed four basic tasks: go to goal behavior, obstacle avoidance behavior, tracking behaviour, and deadlock disarming behavior.…”

mentioning

confidence: 99%

Same Fuzzy Logic Controller for Two-Wheeled Mobile Robot Navigation in Strange Environments

Aouf

Boussaid

Sakly

2019

Journal of Robotics

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For any mobile device, the ability to navigate smoothly in its environment is of paramount importance, which justifies researchers’ continuous work on designing new techniques to reach this goal. In this work, we briefly present a description of a hard work on designing a Same Fuzzy Logic Controller (S.F.L.C.) of the two reactive behaviors of the mobile robot, namely, “go to goal obstacle avoidance” and “wall following,” in order to solve its navigation problems. This new technique allows an optimal motion planning in terms of path length and travelling time; it is meant to avoid collisions with convex and concave obstacles and to achieve the shortest path followed by the mobile robot. The efficiency of employing the proposed navigational controller is validated when compared to the results from other intelligent approaches; its qualities make of it an efficient alternative method for solving the path planning problem of the mobile robot.

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A Fuzzy Behavior-Based Architecture for Mobile Robot Navigation in Unknown Environments

Cited by 34 publications

References 9 publications

Development of Autonomous Intelligent Vehicle (AIV) Based on Voting Technique

Development of Autonomous Intelligent Vehicle (AIV) Based on Voting Technique

Behavior-Based Fuzzy Control for Mobile Robot Navigation

Same Fuzzy Logic Controller for Two-Wheeled Mobile Robot Navigation in Strange Environments

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