Evolutionary algorithm based neural network controller optimization for autonomous mobile robot navigation

Author

Han, Seong-Joo ; Oh, Se-young

Author_Institution

Dept. of Electr. Eng., Pohang Univ. of Sci. & Technol., South Korea

Volume

1

fYear

2001

fDate

2001

Firstpage

121

Abstract

A neural network based navigation algorithm is proposed for mobile robots using ultrasonic sensors. The neural network has a dynamically reconfigurable structure which can not only optimize the weights but also the input sensory connectivity in order to meet any user-defined objective. Further, in order to enhance generalization capability of a single network, a modular network is used in which each network module is optimized for a specific local environment based on environment classification. Both computer simulation and real experiments show the effective performance of the algorithm

Keywords

digital simulation; evolutionary computation; mobile robots; navigation; neurocontrollers; optimisation; path planning; autonomous mobile robot navigation; computer simulation; dynamically reconfigurable structure; environment classification; evolutionary algorithm based neural network controller optimization; generalization; input sensory connectivity; local environment; modular network; ultrasonic sensors; user-defined objective; Biological cells; Computer simulation; Design optimization; Encoding; Evolutionary computation; Humans; Mobile robots; Navigation; Neural networks; Path planning;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on

Conference_Location

Seoul

Print_ISBN

0-7803-6657-3

Type

conf

DOI

10.1109/CEC.2001.934380

Filename

934380