Optical Computer Mouse Referenced Calibration of an Inertial Measurement Unit for Use in Unmanned Underwater Vehicles

Abstract:

In this study an inertial measurement unit (IMU) used in unmanned underwater vehicles has been taken into consideration.The main objective of this study is to improvethe measurements obtained from an IMU used in the position detection by minimizing the effect of its static and dynamic errors on the output. To enhance the IMU data optical computer mouse (OCM) is proposed as calibrator. The data received from the OCM is used to train an artificial neural network (ANN) which would improve the IMU outputs by trying to estimate the reference data from the actual sensor outputs. The ANN performance is compared with that of classic low pass filtering methods to provide a relative performance criterion. The ANN trained with OCM data has given satisfactory results. During the training of ANNs the effects of several parameters such as neural network architecture, activation functions, training algorithm, layer and cell number have been investigated. Thus, the results, findings and insights obtained in this study can be applied in research areas where this kind of nonlinear estimators are used.

• Periodical: Applied Mechanics and Materials (Volume 664)
• Pages: 274-278
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.664.274
• Online since: October 2014
• Authors: Serhat Ikizoglu*, Yaver Kamer
• Keywords: Artificial Neural Network (ANN), Calibration, Inertial Measurement Unit, Optical Computer Mouse

References

[1] M. Bennamoun, B. Boashash, F. Faruqi, M. Dunbar: The developmentof an integrated GPS/INS/sonar navigation system for autonomousunderwater vehicle navigation, Proceedings of the Symposium on Autonomous Underwater Vehicle Technology, 1996, p.256.

DOI: 10.1109/auv.1996.532423

[2] S. Ishibashi: The improvement of the precision of an inertial navigation system for AUV based on the neural network, IEEEOCEANS 2006-Asia Pacific, 2007, p.1–6.

DOI: 10.1109/oceansap.2006.4393828

[3] Y. Kamer, S. Ikizoglu: Effective accelerometer test beds for output enhancement of an inertial navigation system, Measurement 46 (2013), p.1641–1649.

DOI: 10.1016/j.measurement.2012.12.020

[4] MicroStrain. <http: /www. microstrain. com/pdf/3DMGX1%20Datasheet%20Rev%201. pdf>.

[5] T.W. Ng: The optical mouse as a two-dimensional displacement sensor, Sensors and Actuators A: Physical 107 (1) (2003) 21–25.

DOI: 10.1016/s0924-4247(03)00256-5

[6] F. A. Kanburoğlu, E. Kılıç, M. Dölen and B. Koku: A test setup for evaluating long-term measurment characteristics of optical mouse sensors, Journal of Automation, Mobile Robotics& Intelligent Systems. vol 1, (2007).

[7] N. Tunwattana, A.P. Roskilly, R. Norman: Investigations into the effects of illumination and acceleration on optical mouse sensors as contact-free 2D measurement devices, Sensors and Actuators A: Physical 149 (1) (2009) 87–92.

DOI: 10.1016/j.sna.2008.10.016

[8] Datasheet PixArt <http: /www. pixart. com. tw/upload/PAN3101_V10_20051121170653. pdf>.

[9] J. Cinkelj, M. Mihelj, M. Munih: Soft real-time acquisition in Windows XP, IEEE Third International Workshop on IntelligentSolutions in Embedded Systems, 2005, p.110–116.

DOI: 10.1109/wises.2005.1438719

[10] K. Kılıç, D. Baş, H.I. Boyacı: An Easy Approach for the Selection of Optimal Neural Network, GIDA,. vol 34, (2009).