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Steps Toward Sensor-Based Vehicle Navigation in Outdoor Environments Using a Fuzzy Behaviorist Approach

Because outdoor environments are typically dynamic and not fully predictable, real-time vehicle control in such environments requires many calculational and reasoning schemes to operate on the basis of incomplete, unreliable, and/or imprecise information. For such systems, in which all the uncertainties cannot be engineered away, approximate reasoning may provide an alternative to the complexity and computational requirements of conventional uncertainty analysis and propagation techniques. Two types of computer boards including custom-designed VLSI fuzzy inferencing chips have been developed to add an approximate reasoning capability to real-time control systems. All inferencing rules on a chip are processed in parallel, allowing execution of the entire rule base in about 30 μs i.e., at rates much faster than typical sensor sampling rates and therefore making control of “reflex-type” motions envisionable. The use of these boards and a proposed approach using superposition of sensor-based fuzzy behaviors for the development of qualitative reasoning schemes are first discussed. We then describe how a fuzzy behavior-based navigation scheme emulating human-like navigation in a priori unknown environments was implemented on one of the fuzzy inferencing boards and installed on a test-bed platform to investigate two control modes for driving a car 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-type fuzzy commands to turn left or right and speed up, slow down, or back up depending upon 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 indoor and outdoor experiments are presented and discussed to illustrate the feasibility and robustness of the proposed approach for sensor-based functions such as autonomous navigation and/or safety-enhancing driver's aid.