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Tytuł artykułu

Application Of Kalman Filter In Navigation Process Of Automated Guided Vehicles

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper an example of application of the Kalman filtering in the navigation process of automatically guided vehicles was presented. The basis for determining the position of automatically guided vehicles is odometry – the navigation calculation. This method of determining the position of a vehicle is affected by many errors. In order to eliminate these errors, in modern vehicles additional systems to increase accuracy in determining the position of a vehicle are used. In the latest navigation systems during route and position adjustments the probabilistic methods are used. The most frequently applied are Kalman filters.
Słowa kluczowe
Rocznik
Strony
443--454
Opis fizyczny
Bibliogr. 26 poz., rys., wykr., wzory
Twórcy
autor
  • Rzeszów University of Technology, Faculty of Management, Al. Powstańców Warszawy 10, 35-959 Rzeszów, Poland
  • Rzeszów University of Technology, Faculty of Management, Al. Powstańców Warszawy 10, 35-959 Rzeszów, Poland
Bibliografia
  • [1] Bong-Su, Ch., Woo-Sung, M., Woo-Jin, S., Kwang-Ryul, B. (2011). A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding. Journal of Mechanical Science and Technology, 25(11), 2907-2917.
  • [2] Castellanos, J.A., Martinez-Cantin, R., Tardós, J.D., Neira, J. (2007). Robocentric map joining: Improving the consistency of EKF-SLAM. Robotics and Autonomous Systems, 55, 21-29.
  • [3] Changbae, J., Chang-Bae, M., Daun, J., Jong-Suk, Ch., Woojin, Ch. (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.
  • [4] Diosi, A., Kleeman, L. (2004). Advanced sonar and laser range finder fusion for simultaneous localization and mapping. Proc. of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai Japan, 1854-1859.
  • [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-Journal of the International Societies for Precision Engineering and Nanotechnology, 34(3), 647-650.
  • [6] Dobrzanski, P., Pawlus, P. (2010). Digital filtering of surface topography: Part II. Applications of robust and valley suppression filters. Precision Engineering-Journal of the International Societies for Precision Engineering and Nanotechnology, 34(3), 651-658.
  • [7] Epton, T., Hoover, A. (2012). Improving odometry using a controlled point laser. Autonomous Robots, 32, 165-172.
  • [8] Grewal, M.S., Andrews, A.P. (2008). Kalman Filtering: Theory and Practice with MATLAB. Wiley.
  • [9] Joerger, M., Pervan, B. (2013). Kalman Filter-Based Integrity Monitoring Against Sensor Faults. Journal of Guidance Control and Dynamics, 36(2), 349-361.
  • [10] Joerger, M., Pervan, B. (2009). Measurement-level integration of carrier-phase GPS and laser-scanner for outdoor ground vehicle navigation. Journal of Dynamic Systems, Measurement, and Control, 131/021004-1-021004-11.
  • [11] Jung-Suk, L., Wan Kyun, Ch. (2010). Robust mobile robot localization in highly non-static environments. Autonomous Robots, 29, 1-16.
  • [12] Jungmin, K., Seungbeom, W., Jaeyong, K., Joocheol, D., Sungshin, K., Sunil, B. (2012). Inertial Navigation System for an Automatic Guided Vehicle with Mecanum Wheels. International Journal of Precision Engineering and Manufacturing, 13(3), 379-386.
  • [13] Kaplonek, W., Łukianowicz, Cz., Nadolny, K. (2012). Methodology of the assessment of the abrasive tool’s active surface using laser scatterometry. Transactions of the Canadian Society for Mechanical Engineering, 36(1), 49-66.
  • [14] Kasinski, A., Skrzypczynski, P. (2001). Perception network for the team of indoor mobile robots: concept, architecture, implementation. Engineering Applications of Artificial Intelligence, 14, 125-137.
  • [15] Kelly, A. (2004). Linearized error propagation in odometry. International Journal of Robotics Research, 23(2), 179-218.
  • [16] Knuth, J., Barooah, P. (2013). Error growth in position estimation from noisy relative pose measurements.Robotics and Autonomous Systems, 61, 229-244.
  • [17] Kooktae, L., Changbae, J., Woojin, Ch. (2011). Accurate calibration of kinematic parameters for two wheel differential mobile robots. Journal of Mechanical Science and Technology, 25(6), 1603-1611.
  • [18] Madhavan, R., Durrant-Whyte, H.F. (2004). Terrain-aided localization of autonomous ground vehicles.Automation in Construction, 13, 83-100.
  • [19] Martinelli, A., Tomatis, N., Siegwart, R. (2007). Simultaneous localization and odometry self-calibration for mobile robot. Autonomous Robots, 75-85.
  • [20] Pears, N.E. (2000). Feature extraction and tracking for scanning range sensors. Robotics and Autonomous Systems, 33, 43-58.
  • [21] Roberts, J.M., Duff, E.S., Corke, P.I. (2002). Reactive navigation and opportunistic localization for autonomous underground mining vehicles. Information Sciences, 145, 127-146.
  • [22] Shoval, S., Zeitoun, I., Lenz, E. (1997). Implementation of a Kalman Filter in positioning for autonomous vehicles, and its sensitivity to the process parameters. International Journal of Advanced Manufacturing Technology, 738-746.
  • [23] Smieszek, M., Dobrzanska, M., Dobrzanski, P. (2010). Errors in odometry navigation. ICMEM, HLOCH, Presov, 124-128.
  • [24] Tungadi, F., Kleeman, L. (2011). Discovering and restoring changes in object positions using an autonomous robot with laser rangefinders. Robotics and Autonomous Systems 59, 428-443.
  • [25] www.nivelco.pl (Dec. 2014).
  • [26] www.turck.pl (Dec. 2014).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3de51e9e-b133-4940-9416-3dd0251c06da
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