An ART-based fuzzy controller for the adaptive navigation of a quadruped robot

Abstract

An adaptive-resonance theory (ART)-based fuzzy controller is presented for the adaptive navigation of a quadruped robot in cluttered environments, by incorporating the capability of ART in stable category recognition into fuzzy-logic control for selecting the adequate rule base. The environment category and navigation mechanism are first described for the quadruped robot. The ART-based fuzzy controller, including an ART-based environment recognizer, a comparer, combined rule bases, and a fuzzy inferring mechanism, is then introduced for the purpose of the adaptive navigation of the quadruped robot. Unlike classical/conventional adaptive-fuzzy controllers, the present adaptive-control scheme is implemented by the adaptive selection of fuzzy-rule base in response to changes of the robot environment, which can be categorized and recognized by the proposed environment recognizer. The results of simulation and experiment show that the adaptive-fuzzy controller is effective.