Robot navigation in very cluttered environments by preference-based fuzzy behaviors

One of the key challenges in application of Autonomous Ground Vehicles (AGVs) is navigation in environments that are densely cluttered with obstacles. The control task becomes more complex when the configuration of obstacles is not known a priori. The most popular control methods for such systems are based on reactive local navigation schemes that tightly couple the robot actions to the sensor information. Because of the environmental uncertainties, fuzzy behavior systems have been proposed. The most difficult problem in applying fuzzy-reactive-behavior-based navigation control systems is that of arbitrating or fusing the reactions of the individual behaviors, which is addressed here by the use of preference logic. This paper presents the design of a preference-based fuzzy behavior system for navigation control of robotic vehicles using the multivalued logic framework. As shown in simulation and experimental results, the proposed method allows the robot to smoothly and effectively navigate through cluttered environments such as dense forests. Experimental comparisons with the vector field histogram method (VFH) show that the proposed method usually produces smoother albeit longer paths to the goal.