Implementation of Tracking and Capturing a Moving Object using a Mobile Robot

  • Kim Sang-joo (School of Electrical Engineering, Pusan National University) ;
  • Park Jin-woo (Institute of Information Technology Assessment(IITA)) ;
  • Lee Jang-Myung (School of Electrical Engineering, Pusan National University)
  • Published : 2005.09.01

Abstract

A new scheme for a mobile robot to track and capture a moving object using camera images is proposed. The moving object is assumed to be a point-object and is projected onto an image plane to form a geometrical constraint equation that provides the position data of the object based on the kinematics of the active camera. Uncertainties in position estimation caused by the point-object assumption are compensated for using the Kalman filter. To generate the shortest time path to capture the moving object, the linear and angular velocities are estimated and utilized. In this paper, the experimental results of the tracking and capturing of a target object with the mobile robot are presented.

Keywords

References

  1. K. Daniilidis and C. Krauss, 'Real-time tracking of moving objects with an active camera,' Real- Time Imaging, Academic Press Limited, 1998
  2. R. F. Berg, 'Estimation and prediction for maneuvering target trajectories,' IEEE Trans. on Automatic Control, vol. AC-28, no. 3, pp. 294-304, March 1983
  3. S. M. Lavalle and R. Sharma, 'On motion planning in changing partially predictable environments,' International Journal of Robotics Research, vol. 16, no. 6, pp. 705-805, December 1997
  4. H. W. Sorenson, 'Kalman filtering techniques,' Advances in Control Systems Theory and Applications, vol. 3, pp. 219-292, 1996
  5. J. W. Park and J. M. Lee, 'Robust map building and navigation for a mobile robot using active camera,' Proc. of ICMT, pp. 99-104, October 1999
  6. R. A. Brooks, 'A robust layered control system for a mobile robot,' IEEE Journal of Robotics and Automation, vol. RA-2, no. 1, pp. 14-23, April 1986
  7. J. J. Leonard and H. F. Durrant-Whyte, 'Mobile robot localization by tracking geometric beacons,' IEEE Trans. on Robotics and Automation, vol. 7, no. 3, pp. 376-382, June 1991 https://doi.org/10.1109/70.88147
  8. D. J. Kriegman, E. Triendl, and T. O. Binford, 'Stereo vision and navigation in buildings for mobile robots,' IEEE Trans. on Robotics and Automation, vol. 5, no. 6, pp. 792-803, December 1989 https://doi.org/10.1109/70.88100
  9. R. E. Kalman, 'A new approach to linear filtering and prediction problems,' Trans, ASME, J. Basic Eng, vol. 82D, pp. 35-45, March 1960
  10. M. Y. Han, B. K. Kim, K. H. Kim, and J. M. Lee, 'Active calibration of the robot/camera pose using the circular objects,' Trans. on Control, Automation and Systems Engineering, vol. 5, no. 3, pp. 314-323, April 1999
  11. D. Nair and J. K. Aggarwal, 'Moving obstacle detection from a navigation robot,' IEEE Trans. on Robotics and Automation, vol. 14, no. 3, pp. 404-416, 1989 https://doi.org/10.1109/70.678450
  12. A. Lallet and S. Lacroix, 'Toward real-time 2D localization in outdoor environments,' Proc. of the IEEE International Conference on Robotics & Automation, pp. 2827-2832, May 1998
  13. A. Adam, E. Rivlin, and I. Shimshoni, 'Computing the sensory uncertainty field of a vision-based localization sensor,' Proc. of the IEEE International Conference on Robotics & Automation, pp. 2993-2999, April 2000
  14. B. H. Kim, D. K. Roh, J. M. Lee, M. H. Lee, K. Son, M. C. Lee, J. W. Choi, and S. H. Han, 'Localization of a mobile robot using images of a moving target,' Proc. of the IEEE International Conference on Robotics & Automation, May 2001
  15. V. Caglioti, 'An entropic criterion for minimum uncertainty sensing in recognition and localization part II-A case study on directional distance measurements,' IEEE Trans. on Systems, Man, and Cybernetics, vol. 31, no. 2, pp. 197-214, April 2001 https://doi.org/10.1109/3477.915343
  16. C. F. Olson, 'Probabilistic self-localization for mobile robots,' IEEE Trans. on Robotics and Automation, vol. 16, no. 1, pp. 55-66, February 2000 https://doi.org/10.1109/70.833191
  17. H. Zhou and S. Sakane, 'Sensor planning for mobile robot localization based on probabilistic inference using bayesian network,' Proc. of the 4th IEEE International Symposium on Assembly and Task Planning, pp. 7-12, May 2001
  18. M. Selsis, C. Vieren, and F. Cabestaing, 'Automatic tracking and 3D localization of moving objects by active contour models,' Proc. of the IEEE International Symposium on Intelligent Vehicles, pp. 96-100, 1995
  19. H. Choset and K. Nagatani, 'Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization,' IEEE Trans. on Robotics and Automation, vol. 17, no. 2, pp. 125-137, April 2001 https://doi.org/10.1109/70.928558
  20. S. Segvic and S. Ribaric, 'Determining the absolute orientation in a corridor using projective geometry and active vision,' IEEE Trans. on Industrial Electronics, vol. 48, no. 3, pp. 696-710, June 2001 https://doi.org/10.1109/41.925597
  21. N. Strobel, S. Spors, and R. Rabenstein, 'Joint audio-video object localization and tracking,' IEEE Signal Processing Magazine, vol. 18, no. 1, pp. 22-31, January 2001
  22. R. G. Hutchins and J. P. C. Roque, 'Filtering and control of an autonomous underwater vehicle for both target intercept and docking,' Proc. of the 4th IEEE International Conference on Control Applications, pp. 1162-1163, 1995
  23. J. Jang, C. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall, 1997
  24. E. Grosso and M. Tistarelli, 'Active/dynamic stereo vision,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no. 9, pp. 868-879, December 1995 https://doi.org/10.1109/34.406652