Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches

Hui, Nirmal Baran; Mahendar, V.; Pratihar, Dilip Kumar

doi:10.1016/j.fss.2006.04.004

Cited by 92 publications

(45 citation statements)

References 42 publications

(62 reference statements)

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“…In this study they have implemented neural integrated fuzzy controller to control the mobile robot motion in terms of steering angle, heading direction, and speed. To determine collision-free path of mobile robot navigating in a dynamic environment using Neuro-fuzzy technique presented by Hui et al [25]. In this paper the performances of Neuro-fuzzy approaches are compared with other approaches (GA, Mamdani) and it was found that Neuro-fuzzy approaches are found to perform better than the other approaches.…”

Section: Introductionmentioning

confidence: 94%

A New Intelligent Approach for Mobile Robot Navigation

Mohanty

Parhi

2013

Lecture Notes in Computer Science

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Abstract. In recent times computational intelligent techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable optimization methods for modeling an engineering system. In this paper an efficient hybrid technique has been applied for mobile robot navigation using multiple adaptive neuro-fuzzy inference system (MANFIS). ANFIS has taken the advantages of both fuzzy inference system and artificial neural network. First, we design an adaptive fuzzy controller with four input parameters, two types of output parameters and three parameters each. Next each adaptive fuzzy controller acts as a single Sugeno-Takagi type fuzzy inference system where inputs are the different sensor based information and output corresponds to the velocity of the mobile robot. The implementation of the proposed navigational controller is discussed via numerous simulation examples. It is found that such an adaptive neuro-fuzzy controller is successfully and quickly finding targets in an unknown or partially unknown environment.

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Section: Introductionmentioning

confidence: 94%

A New Intelligent Approach for Mobile Robot Navigation

Mohanty

Parhi

2013

Lecture Notes in Computer Science

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“…An attempt has been made by the second author to model Mamdani Approach [23] of FLC using the structure of a feedforward NN [24]. The schematic diagram of a Neuro-Fuzzy System (NFS) is shown in Fig.…”

Section: Neuro-fuzzy Model Constructionmentioning

confidence: 99%

A Neuro-Fuzzy System for Modeling the Depression Data

Chattopadhyay¹,

Hui²,

Dasari³

2012

IJCA

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Depression is a psychological disorder, which, if untreated, may deteriorate the quality of one's life. Therefore, to tackle it, its early screening and accurate grading are much needed. The success of soft computing largely stands on its effective ways of handling uncertainty, which is often encountered in a clinical diagnosis. This paper proposes application of soft computing techniques to automate depression diagnosis. In order to achieve our goal, an intelligent Neuro-Fuzzy model has been developed. It has been trained with a sample of realworld depression data. Experiments with test data reveal that the Mean Squared Error in prediction is nominal for most of the cases. Such a system could assist the doctors to take decisions in much needed situations.

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“…This paper considers navigation in unknown environments. Different navigation approaches in unknown environments have been proposed [14,[20][21][22][23][24][25][26]. Among them, one efficient navigation approach that avoids the dead-cycle problem was proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

Navigation of an Autonomous Wheeled Robot in Unknown Environments Based on Evolutionary Fuzzy Control

Chou

Juang

2018

Inventions

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Navigation of a wheeled robot in unknown environments is proposed in this paper. The approach may be applied to navigating an autonomous vehicle in unknown environments, such as parking lots. The navigation consists of three parts: obstacle avoidance behavior, target seeking behavior, and a behavior supervisor. The obstacle avoidance behavior is achieved by controlling the robot to move along an obstacle boundary through evolutionary fuzzy control. In the evolutionary fuzzy control approach, a Pareto set of fuzzy controllers (FCs) is found though a multi-objective continuous ant colony optimization algorithm. Target seeking behavior is achieved by controlling the robot through hybrid proportional-integral-derivative (PID) controllers. The behavior supervisor determines the switching between obstacle avoidance and target seeking behaviors, where the dead-cycle problem is considered. Simulations and experiments were performed to verify the effectiveness of the proposed navigation scheme.

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Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches

Cited by 92 publications

References 42 publications

A New Intelligent Approach for Mobile Robot Navigation

A New Intelligent Approach for Mobile Robot Navigation

A Neuro-Fuzzy System for Modeling the Depression Data

Navigation of an Autonomous Wheeled Robot in Unknown Environments Based on Evolutionary Fuzzy Control

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