Driving a car using reflexive fuzzy behaviors
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. Two types of computer boards including custom-designed VLSI chips have been developed to add a fuzzy inferencing 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 {mu}sec, and therefore, making control of ``reflex-type`` of motions envisionable. The use of these boards and the approach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation in a-prioii unknown environments are discussed. We describe how the human-like navigation scheme implemented on one of the qualitative inferencing boards was installed on a test-bed platform 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 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 charmers to perceive the environment. Simulation results as well as indoor and outdoor experiments are discussed to illustrate the feasibility and robustness of autonomous navigation and/or safety enhancing driver`s aid using the new fuzzy inferencing hardware system and some human-like reasoning schemes.
- Research Organization:
- Oak Ridge National Lab., TN (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 10179104
- Report Number(s):
- CONF-930339-1; ON: DE93001214
- Resource Relation:
- Conference: 2. IEEE international conference on fuzzy systems,San Francisco, CA (United States),28 Mar - 1 Apr 1993; Other Information: PBD: [1992]
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
VEHICLES
REMOTE CONTROL
ROBOTS
NAVIGATION
FUZZY LOGIC
LOGIC CIRCUITS
INTEGRATED CIRCUITS
DATA PROCESSING
DECISION MAKING
COMPUTERIZED CONTROL SYSTEMS
PARALLEL PROCESSING
SONAR
420200
990200
426000
FACILITIES, EQUIPMENT, AND TECHNIQUES
MATHEMATICS AND COMPUTERS
COMPONENTS
ELECTRON DEVICES AND CIRCUITS