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

Abstract

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.