A novel behavior fusion method for the navigation of mobile robots

This paper presents a novel Behavior Fusion method for the navigation of Autonomous Mobile Vehicle in unknown environments. The proposed navigator consists of an Obstacle Avoider (OA), a Goal Seeker (GS) and a Navigation Supervisor (NS). The fuzzy actions inferred by the OA and the GS are weighted by the NS using the local and global environmental information and fused through fuzzy set operation to produce a command action, from which the final crisp action is determined by defuzzification. Simulation shows that the navigator is able to perform successful navigation task in various unknown environments, and it has smooth action and exceptionally good robustness to sensor noise.