Abstract
For a mobile robot movement, the position measurement using internal reference sensors are of low accuracy. Encoder measurements are affected by skid, accelerometer results are affected by noise and final calculation by additive errors. In this paper is presented a method for acquiring and processing data in order to be used for the correction to be performed in order to determine the position of a mobile robot with increased accuracy. The used sensors in the proposed system are accelerometer and encoders, while the perturbation filtering is made with Kalman method. The data obtained from sensors are processed and analyzed, in order to reduce the measurement error.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dudek, G., Jenkin, M.: Computational Principles of Mobile Robots, 2nd edn. Cambridge University Press, New York (2010)
Grewal, M.S., Andrews, A.P.: Kalman Filtering: Theory and Practice Using MATLAB, 4th edn. Wiley-IEEE Press, Hoboken (2015)
Henderson, T.: 1-D Kinematics. http://www.physicsclassroom.com/
Shim, H., Kochem, M., Tomizuka, M.: Use of Accelerometer for Precision Motion Control of Linear Motor Driven Positioning System. University of California at Berkeley, Berkeley (1998)
Zhu, W.H., Lamarche, T.: Velocity estimation by using imperfect accelerometer and encoder for rigid contact modeling and control. In: Proceedings 2006 IEEE International Conference on Robotics and Automation; ICRA; Orlando, FL, pp. 4234–4239 (2006). doi:10.1109/ROBOT.2006.1642353
Zhu, W.H., Lamarche, T.: Velocity estimation by using position and acceleration sensors. IEEE Trans. Ind. Electron. 54(5), 2706–2715 (2007). doi:10.1109/TIE.2007.899936
Nanu, S.: Educational aspects in control system design technology. Sci. Tech. Bull. 4(1), 584–589 (2007)
Welch, G.F.: History: the use of the Kalman filter for human motion tracking in virtual reality. Presence Teleoperators Virtual Environ. 18(1), 72–91 (2009)
Faragher, R.: Understanding the basis of the Kalman filter via a simple and intuitive derivation [Lecture Notes]. IEEE Signal Process. Mag. 29(5), 128–132 (2012). doi:10.1109/MSP.2012.2203621
Negrea, R.: Modelare statistica si stocastica. Aplicatii in inginerie si economie; Ed. Politehnica Timisoara (2006)
Schittkowski, K.: Data Fitting in Dynamical Systems. Kluwer, Boston (2002). ISBN 1402010796
Bethea, R.M., Duran, B.S., Boullion, T.L.: Statistical Methods for Engineers and Scientists. Marcel Dekker, New York (1985). ISBN 0-8247-7227-X
Ravishankar, N., Dey, D.K.: A First Course in Linear Model Theory. Chapman and Hall/CRC, Boca Raton (2002)
Fotheringham, A.S., Brunsdon, C., Charlton, M.: Geographically weighted regression: the analysis of spatially varying relationships; (Reprint ed.). Wiley, Chichester (2002). ISBN 978-0-471-49616-8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Florin, S., Sorin, N., Romeo, N., Anca, P., Sergiu, F. (2018). Data Acquisition and Processing with Fusion Sensors, Used in Algorithms for Increasing Accuracy of Position Measurement of Mobile Robots. In: Balas, V., Jain, L., Balas, M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-319-62524-9_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-62524-9_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62523-2
Online ISBN: 978-3-319-62524-9
eBook Packages: EngineeringEngineering (R0)