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The use of machine vision to control the basic functions of a CNC machine tool using gestures

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Warianty tytułu
PL
Zastosowanie systemu wizyjnego do sterowania podstawowymi funkcjami obrabiarki CNC za pomocą gestów
Języki publikacji
EN
Abstrakty
EN
This paper presents a concept of a vision system which can simplify the way in which some basic functions of CNC machines can be controlled. The proposed system enables the operator to control a machine tool using gestures. The developed solution is based on Microsoft Kinect for a Windows v2 sensor with a time-offlight camera. A gesture recognition module was implemented in the VC 760 milling machine with an open control system (O.C.E.A.N.). To conduct tests of the proposed interface, a set of gestures used to control a CNC machine was developed. Furthermore, the concept, the structure of the system and the test results are discussed. In summary, the advantages and potential problems of the proposed control system and plans for future development are discussed.
PL
W artykule przedstawiona została koncepcja systemu wizyjnego umożliwiającego kontrolowanie i programowanie obrabiarki CNC za pomocą gestów. Opracowane rozwiązanie ułatwia obsługę obrabiarki CNC poprzez rozpoznawanie gestów wykonywanych przez operatora. Do realizacji sytemu wykorzystany został kontroler ruchu Microsoft Kinect for Windows v2. System rozpoznawania gestów zastosowano w otwartym systemie sterowania obrabiarki VC 760 (O.C.E.A.N.). W ramach badań opracowane zostały zestawy gestów pozwalających na sterowanie obrabiarką CNC. W artykule omówiono koncepcję i budowę systemu oraz wyniki przeprowadzonych testów. W podsumowaniu wskazano zalety oraz potencjalne problemy związane ze strukturą i zastosowaniem systemu, a także zarysowano plany jego dalszego rozwoju.
Rocznik
Strony
213--229
Opis fizyczny
Bibliogr. 39 poz., il., tab.
Twórcy
  • West Pomeranian University of Technology
autor
  • West Pomeranian University of Technology
Bibliografia
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  • [9] Almetwally I., Mallem M., Real-time tele-operation and tele-walking of humanoid Robot Nao using Kinect Depth Camera, 10th IEEE International Conference on Networking, Sensing and Control (ICNSC), Evry, France 2013, 463–466.
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  • [14] Majewski M., Kacalak W., Budniak Z., Pajor M., Interactive Control Systems for Mobile Cranes, [in:] Advances in Intelligent Systems and Computing, Vol. 661, ed, 2018, 10–19.
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  • [17] Gallo L., Placitelli A., Ciampi M., Controller-free exploration of medical image data: Experiencing the Kinect, 24th International Symposium on Computer-Based Medical Systems (CBMS), Bristol, United Kingdom 2011, 1–6.
  • [18] Stateczny K., Pajor M., Project of a manipulation system for manual movement of CNCmachine tool body units, Advances in Manufacturing Science, Vol. 35, 33–41.
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  • [20] Kalgaonkar K., Raj B., One-handed gesture recognition using ultrasonic Doppler sonar, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan 2009, 1889–1892.
  • [21] Ketabdar H., Ali K., Roshandel M., MagiTact: interaction with mobile devices based on compass (magnetic) sensor, 15th international conference on Intelligent user interfaces, Hong Kong, China 2010, 413–414.
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  • [23] Miądlicki K., Pajor M., Saków M., Ground plane estimation from sparse LIDAR data for loader crane sensor fusion system, Methods and Models in Automation and Robotics (MMAR), Międzyzdroje 2017, 717–722.
  • [24] Miądlicki K., Pajor M., Saków M., Real-time ground filtration method for a loader crane environment monitoring system using sparse LIDAR data, Innovations in Intelligent SysTems and Applications (INISTA), Gdynia 2017, 207–212.
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  • [26] Mohan P., Srivastava S., Tiwari G., Kala R., Background and skin colour independent hand region extraction and static gesture recognition, Eighth International Conference on Contemporary Computing (IC), Noida, India 2015, 144–149.
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  • [31] Liu K., Chen C., Jafari R., Kehtarnavaz N., Multi-HMM classification for hand gesture recognition using two differing modality sensors, Circuits and Systems Conference (DCAS), Dallas, USA 2014, 1–4.
  • [32] Fu X., Lu J., Zhang T., Bonair C., Coats M. L., Wavelet Enhanced Image Preprocessing and Neural Networks for Hand Gesture Recognition, IEEE International Conference on Smart City/SocialCom/SustainCom, Chengdu, China 2015, 838–843.
  • [33] Watanabe T., Yachida M., Real time gesture recognition using eigenspace from multi-inputimage sequences, Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan 1998, 428–433.
  • [34] Shehu V., Dika A., Curve similarity measurement algorithms for automatic gesture detection systems, 35th International Convention on Information and Communication Technology, Electronics and Microelectronics, Opatija, Croatia 2012, 973–976.
  • [35] Chen M., AlRegib G., Juang B.H., A new 6D motion gesture database and the benchmark results of feature-based statistical recognition, IEEE International Conference on Emerging Signal Processing Applications (ESPA), Las Vegas, USA 2012, 131–134.
  • [36] Van Nieuwenhove D., Van der Tempel W., Grootjans R., Kuijk M., Time-of-flight Optical Ranging Sensor Based on a Current Assisted Photonic Demodulator, Annual Symposium of the IEEE Photonics Benelux Chapter, Ghent, Belgium 2006, 209–212.
  • [37] Domek S., Pajor M., Pietrusewicz K., Urbański Ł., Experimental open control system OCEAN for linear drives, Inżynieria Maszyn, Vol. 16, 40–49.
  • [38] Erol A., Bebis G., Nicolescu M., Boyle R.D., Twombly X., Vision-based hand pose estimation: A review, Computer Vision and Image Understanding, Vol. 108, 52–73.
  • [39] Wang Q., Kurillo G., Ofli F., Bajcsy R., Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect, International Conference on Healthcare Informatics (ICHI), Dallas, USA 2015, 380–389.
Uwagi
EN
Section "Mechanics Engineering"
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7b8c51f8-4a4b-4cb0-aacb-107f5a268f26
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