Learning self-organizing maps for navigation in dynamic worlds

摘要

Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method [Rui Araujo et al., April 1999], based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new on-line method for learning maps of dynamic worlds. For this purpose the Prune-Able fuzzy ART neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network to include the ability to selectively perform the following additional operation on recognition categories: remove, directly update spatial span, or forced create. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods.