Title :
Prune-Able Fuzzy ART Neural Architecture for Robot Map Learning and Navigation in Dynamic Environments
Author_Institution :
Dept. of Electr. & Comput. Eng., Coimbra Univ.
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 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
Keywords :
ART neural nets; fuzzy neural nets; mobile robots; neural net architecture; path planning; self-organising feature maps; Nomad 200 robot; dynamic environments; mobile robots; prune-able fuzzy ART neural architecture; robot map learning; robot navigation; self-organizing neural network; shortest path planning; Mobile robots; Navigation; Neural networks; Path planning; Resonance; Self organizing feature maps; Sensor phenomena and characterization; Sensor systems and applications; Subspace constraints; Trajectory; Dynamic worlds; mobile robot navigation; neural architecture; self-organizing maps learning; Algorithms; Artificial Intelligence; Cluster Analysis; Computing Methodologies; Fuzzy Logic; Motion; Neural Networks (Computer); Pattern Recognition, Automated; Robotics;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.877534