Autonomous Navigation Based on   Magnetic and Geometric Landmarks on Environmental Structure in Real   World

Naoki Akai; Kazumichi Inoue; Koichi Ozaki

doi:10.20965/jrm.2014.p0158

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JRM Vol.26 No.2 pp. 158-165

doi: 10.20965/jrm.2014.p0158

(2014)

Paper:

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Autonomous Navigation Based on Magnetic and Geometric Landmarks on Environmental Structure in Real World

Naoki Akai, Kazumichi Inoue, and Koichi Ozaki

Graduate School of Engineering, Utsunomiya University, 7-1-2 Yoto, Utsunomiya-shi, Tochigi 321-8585, Japan

Received:

December 2, 2013

Accepted:

February 24, 2014

Published:

April 20, 2014

Keywords:

autonomous mobile robot, magnetic navigation method,Monte Carlo localization, distributed control system, Real World Robot Challenge

Abstract

For the Real World Robot Challenge (RWRC) 2013, a new task was established: every robot was required to search for designated persons. In this paper, therefore, we consider the difficulty of the task and construct a navigation strategy to achieve the task. To navigate a robot on the basis of the strategy, long distance navigation is necessary. We have developed a unique navigation method based on magnetic and geometric landmarks on environmental structures in various locations. This method allows a robot to robustly localize by evaluating the reliability of magnetic and geometric landmarks. By using this method, a robot can navigate stably, even if there are no existing landmarks to serve as objects. We achieved autonomous navigation over long distances and successfully searched out designated persons as the challenge of the RWRC2013. This paper presents our navigation method and discusses long distance navigation using the method.

Cite this article as:

N. Akai, K. Inoue, and K. Ozaki, “Autonomous Navigation Based on Magnetic and Geometric Landmarks on Environmental Structure in Real World,” J. Robot. Mechatron., Vol.26 No.2, pp. 158-165, 2014.

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References

[1] K. Nagatani et al., “Sensor Information Processing in Robot Competitions and Real World Robot Challenges,” Advanced Robotics, Vol.26, No.14, pp. 1539-1554, 2012.
[2] T. Yoshida et al., “A Sensor Platform for Outdoor Navigation Using Gyro-assisted Odometry and Roundly-swinging 3D Laser Scanner,” Int. Conf. on Intelligent Robots and Systems, pp. 1414-1420, 2010.
[3] T. Suzuki et al., “High-Accuracy GPS and GLONASS Positioning by Multipath Mitigation using Omni-directional Infrared Camera,” Int. Conf. on Robotics and Automation, pp. 311-316, 2011.
[4] T. Tomizawa et al., “Development of an Intelligent Senior-Car in a PedestrianWalkway,” Advanced Robotics, Vol.26, No.14, pp. 1577-1602, 2012.
[5] S. A. Rahok et al., “Navigation Using an Environmental Magnetic Field for Outdoor Autonomous Mobile Robots,” Advanced Robotics, Vol.26, Nos.3-4, pp. 1751-1771, 2011.
[6] N. Akai et al., “Monte Carlo Localization Using Magnetic Sensor and LIDAR for Real World Navigation,” Int. Symp. on System Integration, pp. 682-687, 2013.
[7] K. Yamauchi et al., “Person Detection Method Based on Color Layout in Real World Robot Challenge 2013,” J. of Robotics and Mechatronics, Vol.26, No.2, 2014 (in press).
[8] 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.
[9] S. Thrun et al., “Probabilistic Robotics,” MIT Press, 2005.
[10] A. Doucet et al., “Sequential Monte Carlo Methods in Practice,” Springer, 2001.
[11] S. A. Rahok et al., “Play-back Navigation for Outdoor Mobile Robot Using Trajectory Tracking Based on Environmental Magnetic Field,” Int. Conf. on Robotics and Automation, pp. 625-630, 2011.

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

[1] [1] K. Nagatani et al., “Sensor Information Processing in Robot Competitions and Real World Robot Challenges,” Advanced Robotics, Vol.26, No.14, pp. 1539-1554, 2012.

[2] [2] T. Yoshida et al., “A Sensor Platform for Outdoor Navigation Using Gyro-assisted Odometry and Roundly-swinging 3D Laser Scanner,” Int. Conf. on Intelligent Robots and Systems, pp. 1414-1420, 2010.

[3] [3] T. Suzuki et al., “High-Accuracy GPS and GLONASS Positioning by Multipath Mitigation using Omni-directional Infrared Camera,” Int. Conf. on Robotics and Automation, pp. 311-316, 2011.

[4] [4] T. Tomizawa et al., “Development of an Intelligent Senior-Car in a PedestrianWalkway,” Advanced Robotics, Vol.26, No.14, pp. 1577-1602, 2012.

[5] [5] S. A. Rahok et al., “Navigation Using an Environmental Magnetic Field for Outdoor Autonomous Mobile Robots,” Advanced Robotics, Vol.26, Nos.3-4, pp. 1751-1771, 2011.

[6] [6] N. Akai et al., “Monte Carlo Localization Using Magnetic Sensor and LIDAR for Real World Navigation,” Int. Symp. on System Integration, pp. 682-687, 2013.

[7] [7] K. Yamauchi et al., “Person Detection Method Based on Color Layout in Real World Robot Challenge 2013,” J. of Robotics and Mechatronics, Vol.26, No.2, 2014 (in press).

[8] [8] 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.

[9] [9] S. Thrun et al., “Probabilistic Robotics,” MIT Press, 2005.

[10] [10] A. Doucet et al., “Sequential Monte Carlo Methods in Practice,” Springer, 2001.

[11] [11] S. A. Rahok et al., “Play-back Navigation for Outdoor Mobile Robot Using Trajectory Tracking Based on Environmental Magnetic Field,” Int. Conf. on Robotics and Automation, pp. 625-630, 2011.