Multilayered fuzzy behavior fusion for reactive control of an autonomous mobile robot

Fuzzy linguistic rules provide an intuitive and powerful means for defining control behavior. Most applications that use fuzzy control feature a single layer of fuzzy inference, mapping a function from one or two inputs to equally few outputs. Highly complex systems, however, may benefit from qualitative rules as well if the control task is properly partitioned. This paper presents a modular fuzzy control architecture and inference engine. A control function is broken down into multiple agents, each of which samples a subset of a large sensor input space. Additional fuzzy agents are employed to fuse the recommendations of the local agents. Real-time implementation without special hardware is possible by using singleton output values during fuzzy rule evaluation. Using this system, a fuzzy behavior-based reactive control system has been implemented on an autonomous mobile robot MARGE, with great success.