Map building using fuzzy ART, and learning to navigate a mobile robot on an unknown world

This paper introduces a new approach, based on the application of the fuzzy ART neural architecture, for online map building from actual sensor data collected with a mobile robot. This method is then integrated as a complement, on the parti-game learning approach, allowing the system to make a more efficient use of collected sensor information. Also, a predictive online trajectory filtering method is introduced on the learning approach. Instead of having a mechanical device (robot) moving to search the world the idea is to have the system analysing trajectories in a predictive mode, by taking advantage of the improved world model. The real robot will only move to try trajectories that have been predicted to be successful, allowing lower exploration costs. This results on an overall new and powerful method for simultaneous and cooperative construction of a world model, and learning to navigate from an initial position to a goal region on an unknown world. It is assumed that the robot knows its own current world location. It is additionally assumed that the mobile robot is able to perform sensor-based obstacle detection (not avoidance), and straight-line motions. Results of experiments with a real Nomad 200 mobile robot will be presented, demonstrating the effectiveness of the proposed methods.