Position Control of Mobile Robot for Human-Following in Intelligent Space with Distributed Sensors

  • Jin Tae-Seok (Dept. of Mechatronics Engineering, DongSeo University) ;
  • Lee Jang-Myung (School of Electronics Engineering, Pusan National University) ;
  • Hashimoto Hideki (Institute of Industrial Science, the University of Tokyo)
  • Published : 2006.04.01

Abstract

Latest advances in hardware technology and state of the art of mobile robot and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. And mobile service robot requires the perception of its present position to coexist with humans and support humans effectively in populated environments. To realize these abilities, robot needs to keep track of relevant changes in the environment. This paper proposes a localization of mobile robot using the images by distributed intelligent networked devices (DINDs) in intelligent space (ISpace) is used in order to achieve these goals. This scheme combines data from the observed position using dead-reckoning sensors and the estimated position using images of moving object, such as those of a walking human, used to determine the moving location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates of a moving object and the estimated position of the robot are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used to estimate the location of moving robot. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in the determining of the location of the mobile robot. Its performance is verified by computer simulation and experiment.

Keywords

References

  1. L. Moreno and E. Dapena, 'Path quality measure for sensor-based motion planning,' Robotics and Autonomous Systems, vol. 44, pp. 131-150, 2003 https://doi.org/10.1016/S0921-8890(03)00041-1
  2. B. Bouilly and T. Simeon, 'A sensor-based motion planner for mobile robot navigation with uncertainty,' Proc. of the International Workshop on Reasoning with Uncertainty in Robotics (RUR), Springer, pp. 235-247, 1996 https://doi.org/10.1007/BFb0013964
  3. M. Betke and L. Gurvits, 'Mobile robot localization using landmarks,' IEEE Trans. on Robotics and Automation, vol. 13, no. 2, pp. 251-263, April 1997 https://doi.org/10.1109/70.563647
  4. 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, 1989 https://doi.org/10.1109/70.88100
  5. J. H. Lee, G. Appenzeller, and H. Hashimoto, 'An agent for intelligent spaces: Functions and roles of mobile robots in sensored, networked, thinking spaces,' Proc. IEEE Conference Intelligent Transportation Systems, Boston, pp. 983-988, 1997
  6. Y. Nakamura, Advanced Robotics: Redundancy and Optimization, Addison-Wesley, 1991
  7. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision, Addison-Wesley, 1993
  8. N. Ayache and O. D. Faugeras, 'Maintaining representations of the environment of a mobile robot,' IEEE Trans. on Robotics and Automation, vol. 5, no. 6, pp. 804-819, 1989 https://doi.org/10.1109/70.88101
  9. R. E. Kalman, 'New approach to linear filtering and prediction problems,' Trans, ASME, J. Basic Eng, Series 82D, pp. 35-45, March 1960
  10. H. W. Sorenson, 'Kalman filtering techniques,' Advances in Control Systems Theory and Applications, vol. 3, pp. 219-292, 1966
  11. 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 (in Korean), vol. 5, no. 3, pp. 314-323, April 1999
  12. T. Akiyama, J. H. Lee, and H. Hashimoto, 'Evaluation of CCD camera arrangement for positioning system in intelligent space,' Proc. of Seventh International Symposium Artificial Life and Robotics, pp. 310-315, 2002
  13. D. Nair and J. K. Aggarwal, 'Moving obstacle detection from a navigation robot,' IEEE Trans. Robotics and Automation, vol. 14, no. 3, pp. 404-416, 1989 https://doi.org/10.1109/70.678450
  14. 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
  15. 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
  16. R. Sim and G. Dudek, 'Learning visual landmarks for pose estimation,' Proc. of the IEEE International Conference on Robotics & Automation, pp. 1972-1978, May 1999
  17. 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, pp. 253-258, May 2001
  18. 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
  19. C. F. Olson, 'Probabilistic self-localization for mobile robots,' IEEE Trans. on Robotics and Automation, vol. 16, no. 1, pp. 55-66, Feb. 2000 https://doi.org/10.1109/70.833191
  20. 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
  21. 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
  22. 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
  23. 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
  24. P. Hoppernot and E. Colle, 'Localization and control of a rehabilitation mobile robot by close human-machine cooperation,' IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 9, no. 2, pp. 181-190, June 2001 https://doi.org/10.1109/7333.928578
  25. J. H. Lee and H. Hashimoto, 'Intelligent space - concept and contents,' Advanced Robotics, vol. 16, no. 3, pp. 265-280, 2002 https://doi.org/10.1163/156855302760121936
  26. J. H. Lee and H. Hashimoto, 'Mobile robot control by distributed sensors,' Proc. of IFAC Workshop Mobile Robot Technology, pp. 85-90, 2001