IMPROVING RANGE ESTIMATION ACCURACY OF AN ULTRASONIC SENSOR USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM, 200-208.
S. Adarsh,∗ K. I. Ramachandran,∗∗ and Binoy B. Nair∗
Keywords
Accuracy, neuro-fuzzy systems, membership functions, regression analysis, ultrasonic sensors
Abstract
Ultrasonic are widely used for obstacle detection and range estima-
tion. They offer a cost-effective solution compared with a camera,
infrared, and RADAR-based sensing systems over short ranges.
The sensor HC-SR04 is one of the most popular and commercially
available ultrasonic sensors, widely used to address the problems
in obstacle detection and ranging in robotics applications. In this
paper, we propose a neuro-fuzzy based system that can improve the
accuracy of the range estimated by the ultrasonic sensor across its
measurement range. The proposed system could reduce the root
mean square error associated with the range estimation of the sensor
by a factor of 4. A brief discussion on the observed improvement
in accuracy, sensitivity, and the calibration process undertaken is
also presented. A simplified regression model is proposed as an out-
come of this experiment. The simplified model is derived from the
neuro-fuzzy system and is observed to be capable of offering lower
error, compared with the conventional linear/non-linear regression
models. The designed neuro fuzzy system was compared with other
techniques such as Support Vector Regression and Artificial Neural
Network. The proposed algorithm can be used to improve the
accuracy of any range sensor used for mobile robot navigation.
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