Solving robot motion planning problem using Hopfield neural network in a fuzzified environment

N. Sadati; J. Taheri

doi:10.1109/FUZZ.2002.1006665

Solving robot motion planning problem using Hopfield neural network in a fuzzified environment

N. Sadati
, J. Taheri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Citations (Scopus)

Abstract

In this paper, a new approach based on Artificial Neural Networks to solve the robot motion planning problem is presented. For this purpose, a Hopfield Neural Network is used in a certain constraint satisfaction problem of the robot motion planning in conjunction with fuzzy modeling of the real robot's environment so that the energy of a state can be interpreted as the extent to which a hypothesis fit the underlying neural formulation model. Thus, low energy values indicate a good level of constraint satisfaction of the problem. Finally, since the obtained answer by the Hopfield Neural Network is not optimal, some algorithms are designed to optimize and generate the final answer.

Original language	English
Title of host publication	2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings
Publisher	IEEE
Pages	1144-1149
Number of pages	6
Volume	2
ISBN (Print)	0780372808
DOIs	https://doi.org/10.1109/FUZZ.2002.1006665
Publication status	Published - 07 Aug 2002
Externally published	Yes

Keywords

Fuzzy environment
Hopfield neural network
Optimization
Robot motion planning

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Condensed Matter Physics
Electrical and Electronic Engineering

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10.1109/FUZZ.2002.1006665Licence: Unspecified

Cite this

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title = "Solving robot motion planning problem using Hopfield neural network in a fuzzified environment",

abstract = "In this paper, a new approach based on Artificial Neural Networks to solve the robot motion planning problem is presented. For this purpose, a Hopfield Neural Network is used in a certain constraint satisfaction problem of the robot motion planning in conjunction with fuzzy modeling of the real robot's environment so that the energy of a state can be interpreted as the extent to which a hypothesis fit the underlying neural formulation model. Thus, low energy values indicate a good level of constraint satisfaction of the problem. Finally, since the obtained answer by the Hopfield Neural Network is not optimal, some algorithms are designed to optimize and generate the final answer.",

keywords = "Fuzzy environment, Hopfield neural network, Optimization, Robot motion planning",

author = "N. Sadati and J. Taheri",

year = "2002",

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doi = "10.1109/FUZZ.2002.1006665",

language = "English",

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Solving robot motion planning problem using Hopfield neural network in a fuzzified environment. / Sadati, N.; Taheri, J.
2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings . Vol. 2 IEEE, 2002. p. 1144-1149.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Solving robot motion planning problem using Hopfield neural network in a fuzzified environment

AU - Sadati, N.

AU - Taheri, J.

PY - 2002/8/7

Y1 - 2002/8/7

N2 - In this paper, a new approach based on Artificial Neural Networks to solve the robot motion planning problem is presented. For this purpose, a Hopfield Neural Network is used in a certain constraint satisfaction problem of the robot motion planning in conjunction with fuzzy modeling of the real robot's environment so that the energy of a state can be interpreted as the extent to which a hypothesis fit the underlying neural formulation model. Thus, low energy values indicate a good level of constraint satisfaction of the problem. Finally, since the obtained answer by the Hopfield Neural Network is not optimal, some algorithms are designed to optimize and generate the final answer.

AB - In this paper, a new approach based on Artificial Neural Networks to solve the robot motion planning problem is presented. For this purpose, a Hopfield Neural Network is used in a certain constraint satisfaction problem of the robot motion planning in conjunction with fuzzy modeling of the real robot's environment so that the energy of a state can be interpreted as the extent to which a hypothesis fit the underlying neural formulation model. Thus, low energy values indicate a good level of constraint satisfaction of the problem. Finally, since the obtained answer by the Hopfield Neural Network is not optimal, some algorithms are designed to optimize and generate the final answer.

KW - Fuzzy environment

KW - Hopfield neural network

KW - Optimization

KW - Robot motion planning

U2 - 10.1109/FUZZ.2002.1006665

DO - 10.1109/FUZZ.2002.1006665

M3 - Conference contribution

AN - SCOPUS:0036456051

SN - 0780372808

VL - 2

SP - 1144

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BT - 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings

PB - IEEE

ER -

Solving robot motion planning problem using Hopfield neural network in a fuzzified environment

Abstract

Keywords

ASJC Scopus subject areas

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