Top > JRM > Autonomous Navigation of a Mobile Robot Based on GNSS/DR In ...

single-rb.php

JRM Vol.26 No.2 pp. 214-224
doi: 10.20965/jrm.2014.p0214
(2014)

Paper:

Views over last 60 days: 1,316

Autonomous Navigation of a Mobile Robot Based on GNSS/DR Integration in Outdoor Environments

Taro Suzuki*, Mitsunori Kitamura**, Yoshiharu Amano**,
and Nobuaki Kubo*

*Tokyo University of Marine Science and Technology, 2-1-6 Etchujima, Koto-ku, Tokyo 135-8533, Japan

**Waseda University, 17 Kikuicho, Shinjuku-ku, Tokyo 162-0044, Japan

Received:
December 5, 2013
Accepted:
February 12, 2014
Published:
April 20, 2014
Creative Commons License
Keywords:
mobile robot, GPS, GPS/DR, navigation
Abstract
This paper describes the development of a mobile robot system and an outdoor navigationmethod based on global navigation satellite system (GNSS) in an autonomous mobile robot navigation challenge, called the Tsukuba Challenge, held in Tsukuba, Japan, in 2011 and 2012. The Tsukuba Challenge promotes practical technologies for autonomous mobile robots working in ordinary pedestrian environments. Many teams taking part in the Tsukuba Challenge used laser scanners to determine robot positions. GNSS was not used in localization because its positioning has multipath errors and problems in availability. We propose a technique for realizing multipath mitigation that uses an omnidirectional IR camera to exclude “invisible” satellites, i.e., those entirely obstructed by a building and whose direct waves therefore are not received. We applied GPS / dead reckoning (DR) integrated based on observation data from visible satellites determined by the IR camera. Positioning was evaluated during Tsukuba Challenge 2011 and 2012. Our robot ran the 1.4 km course autonomously and evaluation results confirmed the effectiveness of our proposed technique and the feasibility of its highly accurate positioning.
Cite this article as:
T. Suzuki, M. Kitamura, Y. Amano, and N. Kubo, “Autonomous Navigation of a Mobile Robot Based on GNSS/DR Integration in Outdoor Environments,” J. Robot. Mechatron., Vol.26 No.2, pp. 214-224, 2014.
Data files:
References
  1. [1] S. Thrun et al., “Stanley, the robot that won the DARPA Grand Challenge,” J. of Field Robotics, Vol.23, No.9, pp. 661-692, 2006.
  2. [2] M.Montemerlo et al., “Junior: The Stanford entry in the urban challenge,” J. of Field Robotics, Vol.25, No.9, pp. 569-597, 2008.
  3. [3] C. Urmson et al., “Autonomous driving in urban environments: Boss and the urban challenge,” J. of Field Robotics, Vol.25, No.9, pp. 425-466, 2008.
  4. [4] J. Markoff, “Google cars drive themselves, in traffic,” The New York Times, October 10, p. A1, 2010.
  5. [5] S. Gleason and D. Gebre-Egziabher, “GNSS Applications and Methods,” Artech House, pp. 1-21, 2009.
  6. [6] S. Yuta et al., “Tsukuba Challenge 2009 – Towards Robots Working in the Real World: Records in 2009 –,” J. of Robotics and Mechatoronics, Vol.23, No.2, pp. 201-206, 2011.
  7. [7] H. Date and Y. Takita, “Real world experiments of an autonomous mobile robot in the pedestrian environment,” Proc. of the 5th Int. Conf. on Automation, Robotics and Applications, pp. 413-418, 2011.
  8. [8] T. Tomizawa et al., “Development of an Intelligent Senior-Car in a PedestrianWalkway,” Advanced Robotics, Vol.26, No.14, pp. 1577-1602, 2012.
  9. [9] S. A. Rahok et al., “Development of a Mobile Robot to Run in Tsukuba Challenge 2010,” Advanced Robotics, Vol.26, No.14, pp. 1555-1575, 2012.
  10. [10] Y.-W. Lee, Y. C. Suh, and R. Shibasaki, “A Simulation System for GNSS Multipath Mitigation Using Spatial Statistical Methods,” Computers and Geosciences, Vol.34, No.11, pp. 1597-1609, 2008.
  11. [11] D. Maier and A. Kleiner, “Improved GPS Sensor Model for Mobile Robots in Urban Terrain,” In Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 4385-4390, 2010.
  12. [12] P. D. Groves, “Shadow Matching: A New GNSS Positioning Technique for Urban Canyons,” J. of Navigation, Vol.64, No.3, pp. 417-30, 2011.
  13. [13] T. Suzuki et al., “6-DOF Localization for a Mobile Robot Using Outdoor 3D Point Clouds,” J. of Robotics and Mechatronics, Vol.22, No.2, pp. 158-166, 2010.
  14. [14] T. Suzuki et al., “High-Accuracy GPS and GLONASS Positioning by Multipath Mitigation using Omnidirectional Infrared Camera,” 2011 IEEE Int. Conf. on Robotics and Automation, pp. 311-316, 2011.
  15. [15] T. Suzuki et al., “Multipath Mitigation Using Omnidirectional Infrared Camera for Tightly Coupled GPS/INS Integration in Urban Environment,” Proc. of ION GNSS 2011, pp. 2914-2922, 2011.
  16. [16] E. Kaplan and C. Hegarty, “Understanding GPS: Principles and Applications 2nd Edition,” Artech House, Boston, 2005.
  17. [17] A. Takeya, T. Kuroda, K. Nishiguchi, and A. Ichikawa, “Omnidirectional vision system Using Two mirrors,” in Proc. of SPIE, Novel Optical Systems and Large-Aperture Imaging, Vol.3430, pp. 50-60, 1998.
  18. [18] R. Hirokawa et al., “An Efficient UKF Based GPS/INS Augmented by Local Landmark Update,” Proc. ION GNSS, pp. 127-134, 2007.

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. 18, 2024