Multilayered fuzzy behavior fusion for real-time reactive control of systems with multiple sensors

Abstract

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, with large numbers of inputs, may also benefit from the use of qualitative linguistic rules if the control task is properly partitioned. This paper presents a modular fuzzy control architecture and inference engine that can be used to control complex systems. The control function is broken down into multiple local 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. A development tool is used to translate a fuzzy programming language offline for fast execution at run time. Using this system, a multilayered fuzzy behavior fusion based reactive control system has been implemented on an autonomous mobile robot, MARGE, with great success. MARGE won first place in Event III of the 1993 Robot Competition sponsored by the American Association for Artificial Intelligence.