Integrating laser and infrared sensors for learning dynamic self-organizing world maps

Mobile robots require the ability to build their own maps to operate in unknown environments. This article introduces a new online method for learning world maps in non-static worlds. The new approach expands the previously proposed method by Araujo et al. (1998, 1999), based on the fuzzy ART neural architecture (FARTNA) towards the application in non-static worlds. For this purpose we introduce the prune-able fuzzy ART neural architecture (PAFARTNA) which extends the FARTNA self-organizing neural network to include the ability to selectively remove recognition categories. A method is proposed for the perception of object removals, and integrated with the PAFARTNA. To study and experimentally validate the proposed methods we integrated sensor information from a laser range finder sensor and a ring of infrared range sensors. Results of experiments with a Nomad 200 mobile robot are presented, demonstrating the effectiveness of the proposed methods.