Performance improvement of contactless distance sensors using neural network

  • Authors:

  • R. Abdubrani;S. S. N. Alhady

  • Affiliations:

  • School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia;School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia

  • Venue:
  • IMMURO'12 Proceedings of the 11th WSEAS international conference on Instrumentation, Measurement, Circuits and Systems, and Proceedings of the 12th WSEAS international conference on Robotics, Control and Manufacturing Technology, and Proceedings of the 12th WSEAS international conference on Multimedia Systems & Signal Processing
  • Year:
  • 2012

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Abstract

Sensor is used to detect an object and determines the distance between sensor and the object. The distance measured by the sensor is sometimes inaccurate, leading to distance errors. Two types of sensors used in this research project are Sharp GP2D12 and ultrasonic LV-Maxsonar EZ1. The output voltage will change based on the distance between the sensor and the object. The sensor's performance is measured by comparing the actual value with sensor's measurement value. The method is used to determine the sensor's performance by using a neural network approach. Neural network applications which help to improve the performance of the sensor as the output value from neural network shows near approximation to the actual value. Mean squared error (MSE) value produced in the learning process shows the distance errors. Next, the testing process is performed to determine the accuracy of the distance sensor indicating the percentage of accuracy is inversely proportional to MSE value. Experimental results demonstrate that Sharp GP2D12 distance sensor showing a higher percentage of accuracy with 95.57% compared to LV-Maxsonar EZ1 sensor which resulted only 90.91%. These results prove that the GP2D12 Sharp distance sensor is more accurate than ultrasonic LV-Maxsonar EZ1 sensor.