Prune-able fuzzy ART neural architecture for robot map learning and navigation in dynamic environments.
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 real-time 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.
Araújo
Rui
R
Institute for Systems and Robotics (ISR), Department of Electrical and Computer Engineering, University of Coimbra, P-3030-290 Coimbra, Portugal. rui@isr.uc.pt
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003205:Computing Methodologies; D017143:Fuzzy Logic; D009038:Motion; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics