By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sadati, N. ; Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran ; Taheri, J.

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

Published in:

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on  (Volume:2 )

Date of Conference:

2002