Evolving a modular neural network-based behavioral fusion using extended VFF and environment classification for mobile robot navigation

• Published in: IEEE transactions on evolutionary computation Vol. 6; no. 4; pp. 413 - 419
• Main Authors: IM, Kwang-Young, OH, Se-Young, HAN, Seong-Joo
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Artificial intelligence; Classification; Computer architecture; Computer science; control theory; systems; Computer simulation; Connectionism. Neural networks; Control theory. Systems; Educational technology; Evolutionary algorithms; Exact sciences and technology; Functional programming; Fusion power generation; Genetic programming; Intelligent robots; Mathematical analysis; Mobile robots; Navigation; Neural networks; Potential fields; Robotics; Robots; Navigation; Moving robot; Computer simulation; Capability; Evolutionary algorithm; Expert system; Modular system; Knowledge base; Neural network; Behavioral analysis; Learning (artificial intelligence); Classification; Numerical simulation; Objective function
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2002.802440

A local navigation algorithm for mobile robots is proposed that combines rule-based and neural network approaches. First, the extended virtual force field (EVFF), an extension of the conventional virtual force field (VFF), implements a rule base under the potential field concept. Second, the neural network performs fusion of the three primitive behaviors generated by EVFF. Finally, evolutionary programming is used to optimize the weights of the neural network with an arbitrary form of objective function. Furthermore, a multinetwork version of the fusion neural network has been proposed that lends itself to not only an efficient architecture but also a greatly enhanced generalization capability. Herein, the global path environment has been classified into a number of basic local path environments to which each module has been optimized with higher resolution and better generalization. These techniques have been verified through computer simulation under a collection of complex and varying environments.