International Journal of Engineering and Manufacturing(IJEM)
ISSN: 2305-3631 (Print), ISSN: 2306-5982 (Online)
Published By: MECS Press
IJEM Vol.8, No.6, Nov. 2018
Angle Measurement on a flat Surface using High Frequency Ultrasonic Pulse
Full Text (PDF, 759KB), PP.26-41
Ultrasonic waves are most commonly used to measure the presence of an object and its distance from the source using time of flight concept. These are pulses of sound waves that have frequency range higher than the human hearing range. In this paper, we will discuss the measurement of the tilt angle of a robot with respect to a flat base using ultrasonic waves and time of flight concept . An Arduino platform was used with Atmel328P as the processing microcontroller chipset which will then compute the angle of tilt using the distance calculated from an ultrasonic sound transmitter and a receiver using coordinate geometry and trigonometric functions. A combination of gyroscope and accelerometer is also used to find the true tilt angle of the apparatus and then it is compared with the angle readings obtained from the ultrasonic sensor system. A high response time and low delay is necessary for instantaneous angle measurement. Hence, gyroscope-based angles have been used as a reference to adjust filter parameters to decrease error and noise at every iteration.
Cite This Paper
Shashi Suman,"Angle Measurement on a flat Surface using High Frequency Ultrasonic Pulse", International Journal of Engineering and Manufacturing(IJEM), Vol.8, No.6, pp.26-41, 2018.DOI: 10.5815/ijem.2018.06.03
Barrett, S. F., & Pack, D. J. (2007). Atmel AVR Microcontroller Primer: Programming and Interfacing. Synthesis Lectures on Digital Circuits and Systems, 2(1), 1-194. DOI:10.2200/s00100ed1v01y200712dcs015
Shrivastava, A. K., Verma, A., & Singh, S. P. (2009). Distance Measurement of an Object or Obstacle by Ultrasound Sensors using P89C51RD2. International Journal of Computer Theory and Engineering, 64-68. doi:10.7763/ijcte.2010.v2.118
Badamasi, Y. A. (2014). The working principle of an Arduino. 2014 11th International Conference on Electronics, Computer, and Computation (ICECCO). doi:10.1109/icecco.2014.6997578
Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35. doi:10.1115/1.3662552
“MPU-6050 Product Specification”, Retrieved from: http://invensense.com/mems/gyro/mpu6050.html. [accessed August 19, 2013]
“Intro. to Signal Processing: Smoothing.” Cell Differentiation by Gram's Stain, terponnect.umd.edu/~toh/spectrum/Smoothing.html.
Martinsen, Paul. “Three Methods to Filter Noisy Arduino Measurements.” MegunoLink, MegunoLink, 4 June 2017, www.megunolink.com/articles/3-methods-filter-noisy-arduino-measurements/?nabe=6011995085340672:0.
Denyssene. “Denyssene/SimpleKalmanFilter.” GitHub, github.com/denyssene/SimpleKalmanFilter.
“Plot Function.” Neural Network - Multi Step Ahead Prediction - MATLAB Answers - MATLAB Central, in.mathworks.com/help/MATLAB/ref/plot.html.
Mercer, C. “Data Smoothing: RC Filtering and Exponential Averaging” Retrieved from: http://blog.prosig.com/2003/04/28/data-smoothing-rc-filtering-and-exponential-averaging/
“Arduino and Python” Retrieved from https://playground.arduino.cc/Interfacing/Python
J.R. Guerci, R.A. Goetz, J.Di Modica, “A method for improving extended Kalman filter performance for angle for angle-only passive ranging”, IEEE Transactions on Aerospace and Electronic Systems” Vol 30, pp 1090-1093, 1994.