DOI QR코드

DOI QR Code

Development of underwater 3D shape measurement system with improved radiation tolerance

  • Kim, Taewon (Nuclear Robot Division, Korea Atomic Energy Research Institute) ;
  • Choi, Youngsoo (Nuclear Robot Division, Korea Atomic Energy Research Institute) ;
  • Ko, Yun-ho (Mechatronics Engineering, Chungnam National University)
  • Received : 2020.06.09
  • Accepted : 2020.09.23
  • Published : 2021.04.25

Abstract

When performing remote tasks using robots in nuclear power plants, a 3D shape measurement system is advantageous in improving the efficiency of remote operations by easily identifying the current state of the target object for example, size, shape, and distance information. Nuclear power plants have high-radiation and underwater environments therefore the electronic parts that comprise 3D shape measurement systems are prone to degradation and thus cannot be used for a long period of time. Also, given the refraction caused by a medium change in the underwater environment, optical design constraints and calibration methods for them are required. The present study proposed a method for developing an underwater 3D shape measurement system with improved radiation tolerance, which is composed of commercial electric parts and a stereo camera while being capable of easily and readily correcting underwater refraction. In an effort to improve its radiation tolerance, the number of parts that are exposed to a radiation environment was minimized to include only necessary components, such as a line beam laser, a motor to rotate the line beam laser, and a stereo camera. Given that a signal processing circuit and control circuit of the camera is susceptible to radiation, an image sensor and lens of the camera were separated from its main body to improve radiation tolerance. The prototype developed in the present study was made of commercial electric parts, and thus it was possible to improve the overall radiation tolerance at a relatively low cost. Also, it was easy to manufacture because there are few constraints for optical design.

Keywords

Acknowledgement

This paper was supported by the MSIT(Ministry of Science and ICT), Korea.

References

  1. Keiji Nagatani, et al., Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots, J. Field Robot. 30 (1) (2013) 44-63. https://doi.org/10.1002/rob.21439
  2. Andreas Loeb, D. Stanke, L. Kemp, Decommissioning of the reactor pressure vessel and its peripheral facilities of the Nuclear Power Plant in Stade, Germanye11100, in: WM2011 Conference, 2011. Phoenix, Arizona, USA.
  3. M. Maimone, et al., A photo-realistic 3-D mapping system for extreme nuclear environments: Chernobyl, in: Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No. 98CH36190), vol. 3, IEEE, 1998.
  4. Houssay, P. Laurent, Robotics and Radiation Hardening in the Nuclear Industry, Diss. State University System of Florida, 2000.
  5. H.L. Hughes, J.M. Benedetto, Radiation effects and hardening of MOS technology: devices and circuits, IEEE Trans. Nucl. Sci. 50 (3) (2003) 500-521. https://doi.org/10.1109/TNS.2003.812928
  6. Fa-Xin Yu, et al., Overview of radiation hardening techniques for IC design, Inf. Technol. J. 9 (6) (2010) 1068-1080. https://doi.org/10.3923/itj.2010.1068.1080
  7. Alia, Ruben Garcia, et al., Simplified SEE sensitivity screening for COTS components in space, IEEE Trans. Nucl. Sci. 64 (2) (2017) 882-890. https://doi.org/10.1109/TNS.2017.2653863
  8. P.S. Winokur, et al., Use of COTS microelectronics in radiation environments, IEEE Trans. Nucl. Sci. 46 (6) (1999) 1494-1503. https://doi.org/10.1109/23.819113
  9. Wolfgang Boehler, Andreas Marbs, 3D scanning instruments, Proc. CIPA WG 6 (9) (2002).
  10. Jason Geng, Structured-light 3D surface imaging: a tutorial, Adv. Optic Photon 3 (2) (2011) 128-160. https://doi.org/10.1364/AOP.3.000128
  11. Digumarti, Sundara Tejaswi, et al., Underwater 3D capture using a low-cost commercial depth camera, in: IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, 2016, 2016.
  12. Marc Hildebrandt, et al., "A Practical Underwater 3D-Laserscanner." OCEANS 2008, IEEE, 2008.
  13. Miquel Massot-Campos, Gabriel Oliver-Codina, Optical sensors and methods for underwater 3D reconstruction, Sensors 15 (12) (2015) 31525-31557. https://doi.org/10.3390/s151229864
  14. Gianfranco Bianco, et al., A comparative analysis between active and passive techniques for underwater 3D reconstruction of close-range objects, Sensors 13 (8) (2013) 11007-11031. https://doi.org/10.3390/s130811007
  15. Kurt Konolige, Projected texture stereo, in: IEEE International Conference on Robotics and Automation, IEEE, 2010, 2010.
  16. Albert Palomer, et al., Underwater laser scanner: ray-based model and calibration, IEEE ASME Trans. Mechatron. 24 (5) (2019) 1986-1997. https://doi.org/10.1109/TMECH.2019.2929652
  17. Kyriakos Herakleous, Charalambos Poullis, 3dunderworld-sls: an Open-Source Structured-Light Scanning System for Rapid Geometry Acquisition, 2014 arXiv preprint arXiv:1406.6595.
  18. Van Luan Tran, Huei-Yung Lin, A structured light RGB-D camera system for accurate depth measurement, Int. J. Optics (2018) 2018.
  19. F. Bruno, et al., Experimentation of structured light and stereo vision for underwater 3D reconstruction, ISPRS J. Photogrammetry Remote Sens. 66 (4) (2011) 508-518. https://doi.org/10.1016/j.isprsjprs.2011.02.009
  20. Zhengyou Zhang, A flexible new technique for camera calibration, IEEE Trans. Pattern Anal. Mach. Intell. 22 (11) (2000) 1330-1334. https://doi.org/10.1109/34.888718
  21. Richard Hartley, Andrew Zisserman, Multiple View Geometry in Computer Vision, Cambridge university press, 2003.