Fuzzy ArtMap Neural Network (FAMNN) based collision avoidance approach for Autonomous Robotic Systems (ARS)

Autonomous Robotic Systems (ARS) have to achieve a high level of flexibility, adaptability and efficiency in real environments. To achieve this goal, they must particularly have the capability to avoid collisions with obstacles. In this paper, a Fuzzy ArtMap Neural Network (FAMNN) based collision avoidance approach for ARS is suggested. Indeed, each robot must learn, using LOcally Communicable Infrared Sensory System (LOCISS), for each positional relation (i.e., for each obstacle position with regard to the considered robot) to deduce the appropriate rule to avoid collisions with an obstacle. Thus, this approach must make robots able, after learning based on the supervised fast stable learning: Simplified Fuzzy ArtMap (SFAM), to determine and use the rule allowing collision avoidance. Simulation results display the ability of the FAMNN based approach providing ARS with capability to intelligently avoid collisions with obstacles. Such an Hybrid Intelligent System (HIS) enhances the abilities of these robots with real-time processing, more autonomy and intelligence.