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Title: Mobile robot navigation using qualitative reasoning

Abstract

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. Forsuch 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. The use of these boards and anapproach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation are first discussed. We then describe how the human-like navigation schemes were implemented 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 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 nodes of control are described inmore » which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulationresults as well as indoors and outdoors experiments are presented and 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 methodologies.« less

Authors:
;
Publication Date:
Research Org.:
Oak Ridge National Lab., TN (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
10132790
Report Number(s):
CONF-930403-26
ON: DE93008277
DOE Contract Number:  
AC05-84OR21400
Resource Type:
Conference
Resource Relation:
Conference: 5. topical meeting on robotics and remote systems,Knoxville, TN (United States),26-29 Apr 1993; Other Information: PBD: [1993]
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ROBOTS; NAVIGATION; ARTIFICIAL INTELLIGENCE; FUZZY LOGIC; COMPUTERIZED CONTROL SYSTEMS; REAL TIME SYSTEMS; 420200; 990200; FACILITIES, EQUIPMENT, AND TECHNIQUES; MATHEMATICS AND COMPUTERS

Citation Formats

Pin, F G, and Watanabe, Yutaka. Mobile robot navigation using qualitative reasoning. United States: N. p., 1993. Web.
Pin, F G, & Watanabe, Yutaka. Mobile robot navigation using qualitative reasoning. United States.
Pin, F G, and Watanabe, Yutaka. Mon . "Mobile robot navigation using qualitative reasoning". United States.
@article{osti_10132790,
title = {Mobile robot navigation using qualitative reasoning},
author = {Pin, F G and Watanabe, Yutaka},
abstractNote = {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. Forsuch 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. The use of these boards and anapproach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation are first discussed. We then describe how the human-like navigation schemes were implemented 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 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 nodes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulationresults as well as indoors and outdoors experiments are presented and 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 methodologies.},
doi = {},
url = {https://www.osti.gov/biblio/10132790}, journal = {},
number = ,
volume = ,
place = {United States},
year = {1993},
month = {3}
}

Conference:
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