Prune-able fuzzy ART neural architecture for robot map learning and navigation in dynamic environments

Author(s): Araujo, R. |

Year: 2006

Citation: IEEE TRANSACTIONS ON NEURAL NETWORKS Volume: 17 Issue: 5 Pages: 1235-1249

Abstract: Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new online method for learning maps of unknown dynamic worlds. For this purpose the new Prune-able fuzzy adaptive resonance theory neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network with novel mechanisms that provide important dynamic adaptation capabilities. Relevant PAFARTNA properties are formulated and demonstrated. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. The proposed methods are integrated into a navigation architecture. With the new navigation architecture the mobile robot is able to navigate in changing worlds, and a degree of optimality is maintained, associated to a shortest path planning approach implemented in realtime over the underlying global world model. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods.

Topics: Robotics, Applications: Information Fusion, Models: ART 2 / Fuzzy ART,

PDF download

Cross References