DOI QR코드

DOI QR Code

손가락 인식을 기반으로 한 로봇청소기 제어기술

Control Technology Based on the Finger Recognition of Robot Cleaners

  • 유향준 (남서울대학교 전자공학과) ;
  • 목승수 (남서울대학교 전자공학과) ;
  • 김준서 (남서울대학교 전자공학과) ;
  • 백지아 (남서울대학교 전자공학과) ;
  • 고윤석 (남서울대학교 전자공학과)
  • 투고 : 2019.11.04
  • 심사 : 2020.02.15
  • 발행 : 2020.02.29

초록

일반 로봇 청소기의 단점은 정해진 루트에서만 동작하기 때문에 정해진 루트를 벗어난 장소에 대한 청소가 불가능하다. 따라서 본 연구에서는 기존 청소기의 단점을 보완하기 위해 손가락 인식 기술을 기반으로 정해진 루트 이외의 장소를 탐색하기 위한 방향제어 방법론을 연구하였다. 주제어장치로는 라즈베리파이를 사용하였으며 Open CV 프로그램을 이용하여 손가락 개수를 인식할 수 있도록 하였다. 제안된 방법론의 유효성을 검증하기 위해서 파이선 언어를 이용하여 손가락 인식 알고리즘을 구현하였으며, 로지텍 C922를 사용한 결과 90cm에서는 100%, 110cm에서는 70%의 성공률을 확인할 수 있었다.

The disadvantage of the general robot cleaner is that it works only on the designated route, so it is impossible to clean the place outside the designated route. Therefore, in this study, the direction control methodology for searching the place other than the designated route based on the finger recognition technology was studied to compensate for the shortcomings of the existing cleaner. Raspberry Pi was used as the main controller and Open CV program was used to recognize the number of fingers. To verify the validity of the proposed methodology, a finger recognition algorithm was implemented using Python language, and as a result of adopting the Logitech C922, the success rate was 100% at 90cm and 70% at 110cm, respectively.

키워드

참고문헌

  1. S. Kim "A Study on the Effect of Cleaning Condition on Cleaning," Master Thesis, Kangwon University, 2005.
  2. W. Jang, J. Lee, and G. Ryu, "Development of Navigation Sensor for Robot Cleaning," Robots and Humans, vol. 12, no. 2, 2015, pp. 26-32.
  3. Y. Kim, "Gesture Recognition System for Robot", Master Thesis, Daegu University, 2008.
  4. J. Byun, "A Study of The 4th Industrial Revolution's Impact on Cultural Industry," Culture Industry Research, vol. 17, no. 3, 2017, pp. 109-118.
  5. W. Jung, "A Study on Multi-Legged Robot Control Method and Real-Time Autonomous Walking using Hand Posture Recognition," Master Thesis, Dongmyung University, 2010.
  6. D. Lee, D. Shin, and D. Shin, "Research on the Finger Counting Method for Gesture Recognition," J. of Internet Computing and Services, vol. 17, no. 2, 2016, pp. 29-37. https://doi.org/10.7472/jksii.2016.17.2.29
  7. F. Gasparini and R. Schettini, "Skin Segmentation Using Multiple Thresholding," Proceedings of SPIE - The International Society for Optical Engineering, vol. 6061, Internet Imaging VII, San Jose, California, USA. Jan. 2006.
  8. S. Chae and K. Jun, "HSV Color Model based Hand Contour Detector Robust to Noise," Journal of Korea Multimedia Society, vol. 18, no. 10, 2015, pp. 1149-1156. https://doi.org/10.9717/kmms.2015.18.10.1149
  9. P. Nayana and K. Sanjeev "Implentation of Hand Gesture Recognition Technique for HCI Using Open CV," International Journal of Recent Development in Engineering and Technology, vol. 2, no. 5, 2014, pp. 17-21.
  10. K. Manikandan, A. Patidar, P. Walia, and A. Roy "Hand Gesture Detection and Conversion to Speech and Text," International Journal of Pure and Applied Mathematics, vol. 120, no. 6, 2018, pp 1347-1362.
  11. J. Park, J. Shin, S. Ahn, H. Lim, and Y. Ko "Design and Making of a Buck Converter For Smart Phone Wireless Charging," J. of the Korea Institute of Electronic Communication Sciences, vol. 12, no. 4, 2017, pp. 607-614. https://doi.org/10.13067/JKIECS.2017.12.4.607
  12. J. Choe, I. Choy, and W. Cho "Study on the Development of Multi-Agents Position Tracking System Using Ultrasonic Transducers," J. of the Korea Institute of Electronic Communication Sciences, vol. 8, no. 5, 2017, pp.725-731. https://doi.org/10.13067/JKIECS.2013.8.5.725