Adding memory processing behaviors to the fuzzy behaviorist approach (FBA): Resolving limit cycle problems in autonomous mobile robot navigation
Most fuzzy logic-based reasoning schemes developed for robot control are fully reflexive, i.e., the reasoning modules consist of fuzzy rule bases mat represent direct mappings from the stimuli provided by the perception systems to the responses implemented by the motion controllers. Due to their totally reflexive nature, such reasoning systems can encounter problems such as infinite loops and limit cycles. In this paper. we propose an approach to remedy these problems by adding a memory and memory-related behaviors to basic reflexive systems. Three major types of memory behaviors are addressed: memory feeding, memory management, and memory utilization. The concepts underlying these major classes of behaviors are first presented. An example of their implementation is then described for the recognition of limit cycles during the sensor-based navigation of mobile robots. Results of sample cases involving an autonomous robot navigating in a priori unknown environments are presented and discussed to illustrate me efficiency of the approach.
memory processing fuzzy behaviorist approach sensor-based navigation autonomous robot fuzzy control limit cycle avoidance