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Line following robot with real–time viterbi Track–Before–Detect algorithm

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Warianty tytułu
PL
Robot line following z algorytmem czasu rzeczywistego TBD
Języki publikacji
EN
Abstrakty
EN
Line following robots are applied in numerous application areas. High reliability of the line estimation could be obtained by the application of Track–Before–Detect algorithms, like Viterbi algorithm. The hardware and software of the robot are shown in the paper. Real–time constraint are discussed in this paper, related to the constructed robot. The obtained results shows the possibilities of tracking the single line using Raspberry Pi v.2 and Linux operating system.
PL
Roboty śledzące linie znajdują zastoswania w wielu miejscach. Dużą niezawodność estymacji można osiągnąć stosując algorytmy TBD w tym Algorytm Viterbiego. W pracy pokazana część sprzę tową i programową robota. Ograniczenia czasu rzeczywistego są poruszane w odniesieniu do robota. Pokazano, że można śledzić linię dzięki układowi Raspberry Pi v.2 i systemowi operacyjnemu Linux.
Rocznik
Strony
69--72
Opis fizyczny
Bibliogr. 18 poz., fot., rys.
Twórcy
autor
  • West–Pomeranian University of Technology, Szczecin, Department of Signal Processing and Multimedia Engineering, 26. Kwietnia 10 St., 71126 Szczecin, Poland
autor
  • West–Pomeranian University of Technology, Szczecin, Department of Signal Processing and Multimedia Engineering, 26. Kwietnia 10 St., 71126 Szczecin, Poland
Bibliografia
  • [1] Astrand, B. and Baerveldt, A.: A vision–based row–following system for agricultural field machinery, Mechatronics, 15 (2), pp. 251–269, 2005.
  • [2] Colak, I. and Yildirim, D.: Evolving a Line Following Robot to Use in Shopping Centers for Entertainment, 35’th Annual Conference of IEEE Industrial Electronics, 2009. IECON ’09, 34(5), pp. 3803–3807, 2009.
  • [3] Haykin, S. and Moher, M.: Communication Systems, John Wiley & Sons, pp. 251–269, 2009.
  • [4] Horan, B. and Najdovski, Z. and Black, T. and Nahavandi, S. and Crothers, P.: OzTug Mobile Robot for Manufacturing Transportation, IEEE International Conference on Systems, Man and Cybernetics (SMC 2011), pp. 3554–3560, 2011.
  • [5] Ismail, A.H. and Ramli, H.R. and Ahmad, M.H. and Marhaban, M.H.: Vision-based System for Line Following Mobile Robot, 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), October 4-6, 2009, Kuala Lumpur, Malaysia, pp. 642–645, 2009.
  • [6] Grzegorz, M. and Mazurek, P.: Tracklet–Based Viterbi Track- Before-Detect Algorithm for Line Following Robots, Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015 Springer International Publishing, pp. 649–658, 2016.
  • [7] Mazurek, P.: Optimization of Bayesian Track-Before-Detect Algorithms for GPGPUs Implementations, Electrical Review, 86 (7), pp. 187–189, 2010.
  • [8] Mazurek, P.: Code reordering using local random extraction and insertion (LREI) operator for GPGPU-based track-beforedetect systems, Electrical Review, 18 (6), pp. 1095–1106, 2013.
  • [9] Mazurek, P.: Directional Filter and the Viterbi Algorithm for Line Following Robots, Computer Vision and Graphics, Lecture Notes in Computer Science, Springer, 8671, pp. 428–435, 2014.
  • [10] Mazurek, P.: Line Estimation using the Viterbi Algorithm and Track–Before–Detect Approach for Line Following Mobile Robots, 19’th International Conference on Methods and Models in Automation and Robotics, pp. 788–793, 2014.
  • [11] Mazurek, P.: Viterbi Algorithm for Noise Line Following Robots, 19’th International Conference on Methods and Models in Automation and Robotics, Advances in Intelligent Systems and Computing, Springer, 313, pp. 111-118, 2015.
  • [12] Okarma, K. and Lech, P.: A fast image analysis for the line tracking robots, Artifical Intelligence and Soft Computing (ICAISC 2010), LNCS, 6114, pp. 329–336, 2010.
  • [13] Ollis, M.: Perception Algorithms for a Harvesting Robot, Carnegie Mellon University, CMU-RI-TR-97-43, 1997.
  • [14] Schmidt, R.A. Jr.: A study of the real–time control of a computer–driven vehicle, Stanford University, 1971.
  • [15] Scott, T. A. and Nilanjan, R.: Biomedical Image Analysis: Tracking, Morgan & Claypool, 2005.
  • [16] Stone, L.D. and Barlow, C.A. and Corwin, T.L.: Bayesian Multiple Target Tracking, Artech House, 1999.
  • [17] Taubel, G. and Yang, J.-S.: A Lane DepartureWarning System Based on the Integration of the Optical Flow and Hough Transform Methods, 2013 10th IEEE International Conference on Control and Automation (ICCA) Hangzhou, China, June 12– 14, 2013, pp. 1352–1357, 2013.
  • [18] Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory, 13 (2), pp. 260–269, 1967.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5b6e3e9b-3511-481a-95ba-6b7bef89e43a
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