JRM Vol.21 No.4 pp. 460-468
doi: 10.20965/jrm.2009.p0460


Rotation-Based Dynamic Localization at an Initial Dead-Zone Avoidance Stage on an RFID Tag Lattice

Kenri Kodaka, Haruhiko Niwa, and Shigeki Sugano

Waseda University, WABOT-HOUSE Laboratory
1-18 Techno-Plaza, Kakamigahara-shi, Gifu 509-0109, Japan

January 12, 2009
April 15, 2009
August 20, 2009
RFID, localization, particle filter, ambient intelligence, dynamic model
We have developed a novel way for robots to estimate their pose dynamically in an environment in which RFID tags have been arranged. We previously developed a method for localizing robots using a particle filter. Testing in a room equipped with an RFID tag lattice at 300-mm intervals revealed that the estimation fails when the robot’s RFID readers are near the center of the robot’s rotation since the reader could not detect enough tags depending on the robot’s rotating location. We overcame this problem by developing an active localization algorithm that generates an entropy map from RFID tag arrangement information, predicts the pose using a particle filter, and attracts the robot to the target using a dynamic model whose basic unit is rotation-based angular velocity. Testing demonstrated that a robot using this algorithm and an entropy map can estimate its pose robustly without falling into a dead zone by moving only about 200 mm.
Cite this article as:
K. Kodaka, H. Niwa, and S. Sugano, “Rotation-Based Dynamic Localization at an Initial Dead-Zone Avoidance Stage on an RFID Tag Lattice,” J. Robot. Mechatron., Vol.21 No.4, pp. 460-468, 2009.
Data files:
  1. [1] S. Sugano, Y. Shirai, and S. Chae, “Environment Design for Human-Robot Symbiosis — Introduction of WABOT-HOUSE Project,” Proc. of the 23rd Int. Symposium on Automation and Robotics in Construction, pp. 152-157, 2006.
  2. [2] D. Hähnel, W. Bugard, D. Fox, K. Fishkin, and M. Philipose, “Mapping and Localization with RFID Technology,” Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 1015-1020, 2004.
  3. [3] T. Yamano, K. Tanaka, M. Hirayama, E. Kondo, Y. Kimuro, and M. Matsumoto, “Self-localization of Mobile Robots with RFID System by using Support Vector Machine,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and System (IROS), pp. 3756-3761, 2004.
  4. [4] K. Kawashima, T. Kainuma, T. Iwao, and N. Fujino, “A position detection method using ActiveRFID and its experiment,” IEICE technical report. Information networks, Vol.103, No.691, pp. 49-54, 2004 (in Japanese).
  5. [5] T. Asakawa, K. Nishihara, and T. Yoshidome, “A Detection System of Location and Direction Angle by a RF Tag Reader Using a Rotary Antenna,” Journal of Robotics and Mechatronics, Vol.20, No.1, pp. 189-195, 2008.
  6. [6] S. Park, R. Saegusa, and S. Hashimoto, “Autonomous Navigation of a Mobile Robot Based on Passive RFID,” Proc. of the IEEE Int. Symposium on Robot & Human Interactive Communication (ROMAN), pp. 218-223, 2007.
  7. [7] K. Kodaka, H. Niwa, Y. Sakamoto, M. Otake, Y. Kanemori, and S. Sugano, “Pose Estimation of a Mobile Robot on a Lattice of RFID Tags,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and System (IROS), pp. 1385-1390, 2008.
  8. [8] V. Kulyukin, A. Kutiyanawala, and M. Jiang, “Surface-embedded Passive RF Exteroception: Kepler, Greed, and Buffon's Needle,” Proc. of the Ubiquitous Intelligence and Computing (UIC), pp. 33-42, 2007.
  9. [9] S. Thrun, D. Fox, W. Burgard, and F. Dellaert, “Robust Monte Carlo localization for mobile robots,” Artificial Intelligence, Vol.128, pp. 99-141, 2001.
  10. [10] H. Niwa, K. Kodaka, Y. Sakamoto, M. Otake, S. Kawaguchi, K. Fujii, Y. Kanemori, and S. Sugano, “GPS-based indoor positioning system with multi-channel pseudolite,” Proc. of the IEEE Int. Conf. of Robotics and Automation (ICRA), pp. 905-910, 2008.
  11. [11] M. Kourogi and T. Kurata, “Indoor/Outdoor Pedestrian Navigation with an Embedded GPS/RFID/Self-contained Sensor System,” Proc. of the 16th Int. Conf. on Artificial Reality and Telexistence (ICAT), pp. 1310-1321, 2006.
  12. [12] Y. Matsumoto, T. Wada, S. Nishio, T. Miyashita, and N. Hagita, “Scalable Multi-People Head Tracking for Robotic Services Combining Multiple Sensors,” Proc. of the 5th Int. Conf. on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 581-586, 2008.

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