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Journal ArticleDOI

Fuzzy logic techniques for navigation of several mobile robots

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TLDR
In this paper, a fuzzy logic controller with different membership functions is developed and used to navigate mobile robots in a totally unknown environment, which depicts that the robots are able to avoid obstacles as well as negotiate the dead ends and reach the targets efficiently.
Abstract
In this paper, navigation techniques for several mobile robots as many as one thousand robots using fuzzy logic are investigated in a totally unknown environment. Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of ultrasonic sensors for measuring the distances of obstacles around it and an infrared sensor for detecting the bearing of the target. These techniques have been demonstrated in various exercises, which depicts that the robots are able to avoid obstacles as well as negotiate the dead ends and reach the targets efficiently. Amongst the techniques developed, FLC having Gaussian membership function is found to be most efficient for mobile robots navigation.

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Citations
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Journal ArticleDOI

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

TL;DR: 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.
Journal ArticleDOI

Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment

TL;DR: An on-line navigation technique for a wheeled mobile robot (WMR) in an unknown dynamic environment using fuzzy logic techniques is investigated and experimental results show the effectiveness of the proposed algorithm.
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Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

TL;DR: This paper solves the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference and the resulting FIS was easily able to plan the path of the robot.
Proceedings ArticleDOI

Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller

TL;DR: The simulation of path planning technique for an autonomous mobile robot is presented and the basic path planning strategies have been used.
Journal ArticleDOI

Comparative study of soft computing techniques for mobile robot navigation in an unknown environment

TL;DR: Hybrid fuzzy logic controls with soft computing techniques are found to be most efficient for mobile robot navigation and obstacle avoidance.
References
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Book

Robot Motion Planning

TL;DR: This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Journal ArticleDOI

Gross motion planning—a survey

TL;DR: This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments.
Journal ArticleDOI

Behavior-based robot navigation on challenging terrain: A fuzzy logic approach

TL;DR: A new traverse-terrain behavior is introduced that uses the regional traversability index to guide the robot to the safest and the most traversable terrain region, which succinctly quantifies the ease of traversal of the regional terrain by the mobile robot.
Journal ArticleDOI

Fuzzy control to drive car-like vehicles

TL;DR: The capability of EM to actually control a real vehicle and to perform trajectory following and obstacle avoidance in real outdoor environments by using simple fuzzy behaviours relying upon low-resolution sensor data is demonstrated.
Journal ArticleDOI

Fuzzy behavior-based control of mobile robots

TL;DR: An extensive fuzzy behavior-based architecture is proposed for the control of mobile robots in a multiagent environment that decomposes the complex multirobotic system into smaller modules of roles, behaviors and actions.
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