Using fuzzy behaviors for the outdoor navigation of a car with low-resolution sensors

摘要

Vehicle control in a priori unknown, unpredictable, and dynamic environments requires many calculational and reasoning schemes to operate on the basis of very imprecise, incomplete, or unreliable data. For such systems, in which all the uncertainties can not be engineered away, approximate reasoning may provide an alternative to the complexity and computational requirements of conventional uncertainty analysis and propagation techniques. A proposed approach using superposition of elemental fuzzy behaviors to emulate human-like qualitative reasoning schemes is first discussed. We then describe how a previously developed navigation scheme implemented on custom-designed VLSI fuzzy inferencing boards for indoor navigation of a small laboratory-type robot was progressively enhanced to investigate two control modes for driving a car in a priori unknown environments on the basis of sparse and imprecise sensor data. In the first mode, the car navigates fully autonomously, while in the second mode, the system acts as a driver's aid providing the driver with linguistic (fuzzy) commands to turn left or right and speed up or slow down depending on the obstacles perceived by the sensors. Experiments with both modes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulation results as well as indoors and outdoors experiments are presented and discussed to illustrate the feasibility of outdoor navigation using fuzzy behaviors operating on possibly very inaccurate sensor data.