1. bookVolume 16 (2016): Issue 4 (August 2016)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

Selection of Filtration Methods in the Analysis of Motion of Automated Guided Vehicle

Published Online: 19 Aug 2016
Volume & Issue: Volume 16 (2016) - Issue 4 (August 2016)
Page range: 183 - 189
Received: 22 Feb 2016
Accepted: 08 Jul 2016
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

In this article the issues related to mapping the route and error correction in automated guided vehicle (AGV) movement have been discussed. The nature and size of disruption have been determined using the registered runs in experimental studies. On the basis of the analysis a number of numerical runs have been generated, which mapped possible to obtain runs in a real movement of the vehicle. The obtained data set has been used for further research. The aim of this paper was to test the selected methods of digital filtering on the same data set and determine their effectiveness. The results of simulation studies have been presented in the article. The effectiveness of various methods has been determined and on this basis the conclusions have been drawn.

Keywords

[1] Bojja, J., Kirkko-Jaakkola, M., Collin, J., Takala, J. (2014). Indoor localization methods using dead reckoning and 3D map matching. Journal of Signal Processing Systems, 76, 301-312.10.1007/s11265-013-0865-9Search in Google Scholar

[2] Borenstein, J., Feng, L. (1996). Measurement and correction of systematic odometry errors in mobile robots. IEEE Transactions on Robotics and Automation, 12 (6), 869-880.10.1109/70.544770Search in Google Scholar

[3] Brinkmann, S., Bodschwinna, H. (2003). Advanced Gaussian filters. In Advanced Techniques for Assessment Surface Topography. Butterworth-Heineman, 63-89.10.1016/B978-190399611-9/50004-9Search in Google Scholar

[4] Chen, X., Jia, Y. (2014). Indoor localization for mobile robots using lampshade corners as landmarks: Visual system calibration, feature extraction and experiments. International Journal of Control, Automation, and Systems, 12 (6), 1313-1322.10.1007/s12555-013-0076-ySearch in Google Scholar

[5] Dobrzanski, P., Pawlus, P. (2010). Digital filtering of surface topography: Part I. Separation of one-process surface roughness and waviness by Gaussian convolution, Gaussian regression and spline filters. Precision Engineering, 34 (3), 647-650.Search in Google Scholar

[6] Dobrzanski, P., Pawlus, P. (2010). Digital filtering of surface topography: Part II. Applications of robust and valley suppression filters. Precision Engineering, 34 (3), 651-658.Search in Google Scholar

[7] Epton, T., Hoover, A. (2012). Improving odometry using a controlled point laser. Autonomous Robots, 32, 165-172.10.1007/s10514-011-9272-xSearch in Google Scholar

[8] Jung, Ch., Moon, Ch., Jung, D., Choi, J., Chung, W. (2014). Design of test track for accurate calibration of two wheel differential mobile robots. International Journal of Precision Engineering and Manufacturing, 15 (1), 53-61.10.1007/s12541-013-0305-6Search in Google Scholar

[9] Kelly, A. (2004). Fast and easy systematic and stochastic odometry calibration. In International Conference on Intelligent Robots and Systems. IEEE, Vol. 4, 3188-3194.Search in Google Scholar

[10] Kelly, A. (2004). Linearized error propagation in odometry. International Journal of Robotics Research, 23 (2), 179-218.10.1177/0278364904041326Search in Google Scholar

[11] Knuth, J., Barooah, P. (2013). Error growth in position estimation from noisy relative pose measurements. Robotics and Autonomous Systems, 61, 229-244.10.1016/j.robot.2012.11.001Search in Google Scholar

[12] Krystek, M. (2000). Discrete linear profile filters. In X International Coloquium on Surfaces, Chemnitz, Germany, 145-152.Search in Google Scholar

[13] Martínez-Barbera, H., Herrero-Pérez, D. (2010). Autonomous navigation of an automated guided vehicle in industrial environments. Robotics and Computer-Integrated Manufacturing, 26, 296-311.10.1016/j.rcim.2009.10.003Search in Google Scholar

[14] Meng, Q., Bischoff, R. (2005). Odometry based pose determination and errors measurement for a mobile robot with two steerable drive wheels. Journal of Intelligent and Robotic Systems, 41 (4), 263-282.10.1007/s10846-005-3506-0Search in Google Scholar

[15] Muniandya, M., Muthusamyb, M. (2012). An innovative design to improve systematic odometry error in nonholonomic wheeled mobile robots. Procedia Engineering, 41, 436-442.10.1016/j.proeng.2012.07.195Search in Google Scholar

[16] Ojeda, L., Borenstein, J. (2004). Methods for the reduction of odometry errors in overconstrained mobile robots. Autonomous Robots, 16, 273-286.10.1023/B:AURO.0000025791.45313.01Search in Google Scholar

[17] Smieszek, M., Dobrzanska, M. (2015). Application of Kalman Filter in navigation process of automated guided vehicles. Metrology and Measurement Systems, 22 (3), 443-454.10.1515/mms-2015-0037Search in Google Scholar

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