Design of a two-level fuzzy controller for a reactive miniature mobile robot

Skubic, M.; Graves, S.; Mollenhauer, J.

doi:10.1109/ifis.1993.324183

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…The first approach has been used for navigating to a target while avoiding obstacles by Skubic et al [110], who used a miniature infrared-based robot; and by Li [66], who used a simulated sonar-based robot. It was also used by Altrok et al [125] on a model car for following a wall-bounded race track while compensating for the skidding and sliding due to the high speed.…”

Section: Complex Behaviorsmentioning

confidence: 99%

The uses of fuzzy logic in autonomous robot navigation

Saffiotti¹

1997

Soft Computing - A Fusion of Foundations, Methodologies and App

312

140

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The development of techniques for autonomous navigation in real-world environments constitutes one of the major trends in the current research on robotics. An important problem in autonomous navigation is the need to cope with the large amount of uncertainty that is inherent of natural environments. Fuzzy logic has features that make it an adequate tool to address this problem. In this paper, we review some of the possible uses of fuzzy logic in the field of autonomous navigation. We focus on four issues: how to design robust behavior-producing modules; how to coordinate the activity of several such modules; how to use data from the sensors; and how to integrate high-level reasoning and low-level execution. For each issue, we review some of the proposals in the literature, and discuss the pros and cons of fuzzy logic solutions.

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Section: Complex Behaviorsmentioning

confidence: 99%

The uses of fuzzy logic in autonomous robot navigation

Saffiotti¹

1997

Soft Computing - A Fusion of Foundations, Methodologies and App

312

140

View full text Add to dashboard Cite

show abstract

“…Many researchers have been working on this field for a long period, they have proposed control techniques for target tracking problems, which consist of state feedback control, fuzzy logic control [9,16,33,39], potential field [10], neural network [11], etc. Many WMR models and control schemes have been presented for trajectory tracking purposes.…”

Section: Introductionmentioning

confidence: 99%

Interpolation Based Controller for Trajectory Tracking in Mobile Robots

Serrano

Godoy

Quintero

et al. 2016

J Intell Robot Syst

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In this work, a novel algorithm for trajectory tracking in mobile robots is presented. For the purpose of tracking trajectory, a methodology based on the interpolation of trigonometric functions of the wheeled mobile robot kinematics is proposed. In addition, the convergence of the interpolation-based control systems is analysed. Furthermore, the optimal controller parameters are selected through Monte Carlo Experiments (MCE) in order to minimize a cost index. The MCE is able to find, the best set of gains that minimizes the tracking error. Experimental results over a mobile robot Pionner 3AT are conclusive and satisfactory. In addition, a comparative study of control performance is carried out against another controllers.

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Mamontov

Willander

1997

Nonlinear Dynamics

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Design of a two-level fuzzy controller for a reactive miniature mobile robot

Cited by 8 publications

References 6 publications

The uses of fuzzy logic in autonomous robot navigation

The uses of fuzzy logic in autonomous robot navigation

Interpolation Based Controller for Trajectory Tracking in Mobile Robots

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