Top > JRM > Development of Intelligent Mobile Cart in a Crowded Environm ...

single-rb.php

JRM Vol.26 No.2 pp. 204-213
doi: 10.20965/jrm.2014.p0204
(2014)

Paper:

Views over last 60 days: 895

Development of Intelligent Mobile Cart in a Crowded Environment – Robust Localization Technique with Unknown Objects –

Satoshi Muramatsu*,**, Tetsuo Tomizawa**, Shunsuke Kudoh**,
and Takashi Suehiro**

*Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo 669-1337, Japan

**The University of Electro Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

Received:
December 4, 2013
Accepted:
February 5, 2014
Published:
April 20, 2014
Creative Commons License
Keywords:
mobile robot, localization
Abstract
This paper describes a localization method for mobile robots. The proposed method is based on a three dimensional space observation model that provides stochastic information for robot location assumption. We previously developed a localization method based on the two dimensional space observation model to be used in an environment with many unknown obstacles such an pedestrians. The previous method used a twodimensional laser scanner and particle filtering to realize robust effective localization of the mobile robot in such environments. It sometimes failed to estimate the robots’ location due to a lack of information about its environment, so we extended the previous method based on a three-dimensional space observationmodel that is expected to use richer information about its environment. Experimental results showed that our proposed method localizes robots successfully in environments where the previous method could not localize robots accurately.
Cite this article as:
S. Muramatsu, T. Tomizawa, S. Kudoh, and T. Suehiro, “Development of Intelligent Mobile Cart in a Crowded Environment – Robust Localization Technique with Unknown Objects –,” J. Robot. Mechatron., Vol.26 No.2, pp. 204-213, 2014.
Data files:
References
  1. [1] S. Cooper and H. Durrant-Whyte, “A Kalman filter model for GPS navigation of land vehicles,” Proc. of the 1994 IEEE/RSJ/GI Int. Conf. on Intelligent Robots Systems, pp. 157-163, 1994.
  2. [2] M. Betke and L. Gurvits, “Mobile Robot Localization Using Landmark,” IEEE Trans. on Robotics and Automation Vol.13, No.2, pp. 257-261, 1997.
  3. [3] M. Tomono, “A Scan Matching Method using Euclidean Invariant Signature for Global Localization and Map Building,” Proc. of the 2004 IEEE Int. Conf. on Robotics and Automation, pp. 866-871, 2004.
  4. [4] H. Koyasu, J. Miura, and Y. Shirai, “Integrating Multiple Scan Matching Results for Ego-Motion Estimation with Uncertainty,” Proc. of 2004 1EEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3104-3109, 2004.
  5. [5] Y. Negishi, J. Miura, and Y. Shirai, “Adaptive Robot Speed Control by Considering Map and Localization Uncertainty,” Proc. of the 8th Int. Conf. on Intelligent Autonomous Systems, pp. 873-880, 2004.
  6. [6] E. Takeuchi, K. Ohono, and S. Tadokoro, “A robust localization method based in free-space observation model,” Proc. of the 2009 JSME Conf. on Robotics and Mechatronics, 1A1-E20, 2009.
  7. [7] S. Olufs and M. Vincze, “An Efficient Area-based Observation Model for Monte-Carlo Robot Localization,” Proc. of the 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 13-20, 2009.
  8. [8] T. Tomizawa, S. Muramatsu, M. Hirai, M. Sato, S. Kudoh, and T. Suehiro, “A Robust Localization for Unknown Obstacle Based on the Gridmap Matching,” J. of Robotics Society of Japan, Vol.30, No.3, pp. 48-54, 2012 (in Japanese).
  9. [9] T. Tomizawa, S. Muramatsu, M. Sato, M. Hirai, S. Kudoh, and T. Suehiro, “Development of an Intelligent Senior-Car in a Pedestrian Walkway,” Int. J. of the Robotics Society of Japan, Advanced Robotics, Vol.26, No.14, pp. 1577-1602, 2012.
  10. [10] R. Triebel, P. Pfaff, and W. Burgard, “Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing,” Proc. of the 2006 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2276-2282, 2006.
  11. [11] T. Yoshida, K. Irie, E. Koyanagi, and M. Tomono, “A Sensor Platform for Outdoor Navigation Using Gyro-assisted Odometry and Roundly-swinging 3D Laser Scanner,” Proc. of the 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1414-1420, 2010.
  12. [12] E. Takeuchi, K. Ohno, and S. Tadokoro, “A robust localization method based on free-space observation model using 3D-Map,” Proc. of the 2010 IEEE Int. Conf. on Robotics and Biomimetics, pp. 973-979, 2010.
  13. [13] R. Kummerle, R. Trieble, P. Pfaff, and W. Burgard, “Monte Carlo Localization in Outdoor Terrains using Multi-Level Surface Maps,” J. of Field Robotics – Special Issue on Field and Service Robotics archive, Vol.25, Issue 6-7, pp. 346-359, 2008.
  14. [14] S. Takahashi, K. Okawa, H. Kato, and S. Higuchi, “Position Estimation based on Feature Data Extracted from Environmental Data,” The 12th SICE System Integration Division Annual Conference, 1O3-1, 2011 (in Japanese).
  15. [15] T. Shikina, T. Yamada, K. Kishita, and S. Yuta, “Approach for Localization and Motion Planning in Tsukuba Challenge 2011,” The 12th SICE System Integration Division Annual Conference, 2O1-4, 2011 (in Japanese).
  16. [16] T. Shioya, K. Kogure, and N. Ohta, “Minimal Autonomous Mover,” The 12th SICE System Integration Division Annual Conference, 2O2-1, 2011 (in Japanese).
  17. [17] H. Matsuda, S. Muramatsu, N. Hayashi, K. Takahashi, M. Ogawa, Y. Sato, T. Tomizawa, S. Kudoh, and T. Suehiro, “Robust Localization Based on a Free Space AreaModel and Image,” The 12th SICE System Integration Division Annual Conference, 2O2-6, 2011 (in Japanese).
  18. [18] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics,” Mit Press, pp. 139-168, 2005.
  19. [19]
    Supporting Online Materials:[a] “Real World Robotics Challenge Tsukuba Challenge”
    http://www.tsukubachallenge.jp/
    [Accessed April 1, 2014]

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024