ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Optimization-based humanoid robot navigation using monocular camera within indoor environment

  • Received : 2018.02.28
  • Accepted : 2018.05.27
  • Published : 2018.08.07
Download PDF
⟨ Previous Next ⟩

Abstract

Robot navigation allows robot mobility. Therefore, mobility is an area of robotics that has been actively investigated since robots were first developed. In recent years, interest in personal service robots for homes and public facilities has increased. As a result, robot navigation within the home environment, which is an indoor environment, is being actively investigated. However, the problem with conventional navigation algorithms is that they require a large computation time for their building mapping and path planning processes. This problem makes it difficult to cope with an environment that changes in real-time. Therefore, we propose a humanoid robot navigation algorithm consisting of an image processing and optimization algorithm. This algorithm realizes navigation with less computation time than conventional navigation algorithms using map building and path planning processes, and can cope with an environment that changes in real-time.

Keywords

References

  1. A. Nagariya et al., Mobile robot navigation amidst humans with intents and uncertainties: A time scaled collision cone approach, Proc. IEEE Annu. Conf. Decision Contr., Osaka, Japan, Dec. 15-18, 2015, pp. 2773-2779.
  2. K. Qian et al., Mobile robot navigation in unknown corridors using line and dense features of point clouds, Proc. Annu. Conf. IEEE Ind. Electron. Soc., Yokohama, Japan, Nov. 9-12, 2015, pp. 1831-1836.
  3. S. Mehmood et al., Stereo-vision based autonomous underwater navigation - The platform SARSTION, Proc. Int. Bhurban Conf. Appl. Sci. Technol., Islamabad, Pakistan, Jan. 12-16, 2016, pp. 554-559.
  4. J. Garimort, A. Hornung, and M. Bennewitz, Humanoid navigation with dynamic footstep plans, Proc. IEEE Int. Conf. Robotics Autom., Shanghai, China, May 9-13, 2011, pp. 3982-3987.
  5. P. Karkowski and M. Bennewitz, Real-time footstep planning using a geometric approach, Proc. IEEE Int. Conf. Robotics Autom., Stockholm, Sweden, May 16-21, 2016, pp. 1782-1787.
  6. H. Li et al., A humanoid robot localization method for biped navigation in human-living environments, Proc. IEEE Int. Conf. Cyber Technol. Autom., Contr., Intell. Syst., Sheyang, China, June 8-12, 2015, pp. 540-544.
  7. A. Amanatiadis, A multisensor indoor localization system for biped robots operating in industrial environments, IEEE Trans. Ind. Electron. 63 (2016), no. 12, 7597-7606. https://doi.org/10.1109/TIE.2016.2590380
  8. J. Delfin, H. M. Becerra, and G. Arechavaleta, Humanoid localization and navigation using a visual memory, Proc. IEEE-RAS Int. Conf. Humanoid Robots, Cancun, Mexico, Nov. 15-17, 2016, pp. 725-731.
  9. S. Wen et al., Camera recognition and laser detection based on EKF-SLAM in the autonomous navigation of humanoid robot, in Journal of Intelligent & Robotic Systems, Springer, Netherlands, 2017, pp. 1-13.
  10. D. Maier, M. Bennewitz, and C. Stachniss, Self-supervised obstacle detection for humanoid navigation using monocular vision and sparse laser data, Proc. IEEE Int. Conf. Robotics Autom., Shanghai, China, May 9-13, 2011, pp. 1263-1269.
  11. G. Brooks, P. Krishnamurthy, and F. Khorrami, Humanoid robot navigation and obstacle avoidance in unknown environments, Proc. Asian Contr. Conf., Istanbul, Turkey, June 23-26, 2013, pp. 1-6.
  12. Y. Furuta et al., Transformable semantic map based navigation using autonomous deep learning object segmentation, Proc. IEEE-RAS Int. Conf. Humanoid Robots, Cancun, Mexico, Nov. 15-17, 2016, pp. 614-620.
  13. A. Faragasso et al., Vision-based corridor navigation for humanoid robots, Proc. IEEE Int. Conf. Robotics Autom., Karlsruhe, Germany, May 6-10, 2013, pp. 3190-3195.
  14. M. Ferro et al., Omnidirectional humanoid navigation in cluttered environments based on optical flow information, Proc. IEEE-RAS Int. Conf. Humanoid Robots, Cancun, Mexico, Nov. 15-17, 2016, pp. 75-80.
  15. L. George and A. Mazel, Humanoid robot indoor navigation based on 2D bar codes: Application to the NAO robot, Proc. IEEE-RAS Int. Conf. Humanoid Robots, Atlanta, GA, USA, Oct. 15-17, 2013, pp. 329-335.
  16. S. Kajita et al., A realtime pattern generator for biped walking, Proc. IEEE Int. Conf. Robotics Autom., Washington, DC, USA, May 11-15, 2002, pp. 31-37.
  17. C. C. Hsu et al., Distance measurement based on pixel variation of CCD images, ISA Trans. 48 (2009), no. 4, 389-395. https://doi.org/10.1016/j.isatra.2009.05.005
  18. H. Kim et al., Distance measurement using a single camera with a rotating mirror, Int. J. Contr., Autom. Syst. 3 (2005), no. 4, 542-551.
  19. Z. Zhang et al., A novel absolute localization estimation of a target with monocular vision, Int. J. Light Electron Opt. 124 (2013), no. 12, 1218-1223. https://doi.org/10.1016/j.ijleo.2012.03.032
  20. L. F. Posada et al., Floor segmentation of omnidirectional images for mobile robot visual navigation, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Taipei, Taiwan, Oct. 18-22, 2010, pp. 804-809.
  21. Y. G. Kim and H. Kim, Layered ground floor detection for vision-based mobile robot navigation, Proc. IEEE Int. Conf. Robotics Autom., New Orleans, LA, USA, Apr. 26-May 1, 2004, pp. 13-18.
  22. Y. Li and S. T. Birchfield, Image-based segmentation of indoor corridor floors for a mobile robot, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Taipei, Taiwan, October 18-22, 2010, pp. 837-843.
  23. J. L. Barron, D. J. Fleet, and S. S. Beauchemin, Performance of optical flow techniques, Int. J. Comput. Vision 12 (1994), no. 1, 43-77. https://doi.org/10.1007/BF01420984
  24. J. Kennedy and R. Eberhart, Particle swarm optimization, Proc. IEEE Int. Conf. Neural Netw., Perth, Australia, Nov. 27-Dec. 1, 1995, pp. 1942-1948.
  25. O. Michel, Cyberbotics Ltd. $Webots^{TM}$: Professional mobile robot simulation, Int. J. Adv. Robotic Syst. 1 (2004), 39-42.
  26. J. K. Yoo and J. H. Kim, Gaze control-based navigation architecture with a situation-specific preference approach for humanoid robots, IEEE/ASME Trans. Mechatron. 20 (2004), no. 5, 2425-2436. https://doi.org/10.1109/TMECH.2014.2382633