Visual Navigation of a Wheeled Mobile Robot Using Front Image in Semi-Structured Environment

Keita Kurashiki; Mareus Aguilar; Sakon Soontornvanichkit

doi:10.20965/jrm.2015.p0392

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JRM Vol.27 No.4 pp. 392-400

doi: 10.20965/jrm.2015.p0392

(2015)

Paper:

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Visual Navigation of a Wheeled Mobile Robot Using Front Image in Semi-Structured Environment

Keita Kurashiki, Mareus Aguilar, and Sakon Soontornvanichkit

Graduate School of Engineering, Osaka University
2-1 Yamada-oka, Suita, Osaka 565-0871, Japan

Received:

February 3, 2015

Accepted:

May 26, 2015

Published:

August 20, 2015

Keywords:

autonomous navigation, image based control, nonholonomic mobile robot, semi-structured environment, Tsukuba Challenge

Abstract

Mobile robot with a stereo camera

Autonomous mobile robots has been an active research recently. In Japan, the Tsukuba Challenge is held annually since 2007 in order to realize autonomous mobile robots that coexist with human beings safely in society. Through technological incentives of such effort, laser range finder (LRF) based navigation has rapidly improved. A technical issue of these techniques is the reduction of the prior information because most of them require precise 3D model of the environment, that is poor in both maintainability and scalability. On the other hand, in spite of intensive studies on vision based navigation using cameras, no robot in the Challenge could achieve full camera navigation. In this paper, an image based control law to follow the road boundary is proposed. This method is a part of the topological navigation to reduce prior information and enhance scalability of the map. As the controller is designed based on the interaction model of the robot motion and image feature in the front image, the method is robust to the camera calibration error. The proposed controller is tested through several simulations and indoor/outdoor experiments to verify its performance and robustness. Finally, our results in Tsukuba Challenge 2014 using the proposed controller is presented.

Cite this article as:

K. Kurashiki, M. Aguilar, and S. Soontornvanichkit, “Visual Navigation of a Wheeled Mobile Robot Using Front Image in Semi-Structured Environment,” J. Robot. Mechatron., Vol.27 No.4, pp. 392-400, 2015.

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References

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

[1] [1] S. Thrun, M. Montemerlo, H. Dahlkamp, et al., “Stanley: The Robot that Won the DARPA Grand Challenge,” J. of Field Robotics, Vol.23, No.1, pp. 661-692, 2006.

[2] [2] C. Urmson, J. Anhalt, H. Bae, et al., “Autonomous Driving in Urban Environments: Boss and the Urban Challenge,” J. of Field Robotics Special Issue on the 2007 DARPA Urban Challenge, Part I, Vol.25, No.1, pp. 425-466, 2008.

[3] [3] S. Yuta, M. Mizukawa, H. Hashimoto, H. Tashiro, and T. Okubo, “An Open Experiment of Mobile Robot Autonomous Navigation at the Pedestrian Streets in the City – Tsukuba Challenge,” Int. Conf. on Mechatronics and Automation (ICMA), pp. 904-909, Aug 2011.

[4] [4] T. Suzuki, M. Kitamura, Y. Amano, and N. Kubo, “Autonomous Navigation of a Mobile Robot Based on GNSS/DR Integration in Outdoor Environments,” J. of Robotics and Mechatronics, Vol.26, No.2, pp. 214-224, 2014.

[5] [5] K. Okawa, “Three Tiered Self-Localization of Two Position Estimation Using Three Dimensional Environment Map and Gyro-Odometry,” J. of Robotics and Mechatronics, Vol.26, No.2, pp. 196-203, 2014.

[6] [6] N. Akai, K. Inoue, and K. Ozaki, “Autonomous Navigation Based on Magnetic and Geometric Landmarks on Environmental Structure in Real World,” J. of Robotics and Mechatronics, Vol.26, No.2, pp. 158-165, 2014.

[7] [7] M. Agrawal, K. Konolige, and R. Bolles, “Localization and Mapping for Autonomous Navigation in Outdoor Terrains’ A Stereo Vision Approach,” IEEE Workshop on Applications of Computer Vision, 2007.

[8] [8] F. Chaumette and S. Huthchinson, “Visual servo control I. Basic approaches,” Robotics and Automation Magazine, Vol.13, No.4, pp. 82-90, 2006.

[9] [9] E. Malis, “Survey of vision-based robot control,” ENSIETA European Naval Ship Design Short Course, 2002.

[10] [10] K. Kurashiki and T. Fukao, “Image-based Robust Arbitrary Path Following of Nonholonomic Mobile Robots,” Trans. on the Society of Instrument and Control Engineers, Vol.47, No.5, pp. 238-246, 2011 (in Japanese).

[11] [11] J.-J. E. Slotine and W. Li, “Applied Nonlinear Control,” Prentice Hall, 1991.

[12] [12] Y. Kanayama, Y. Kimura, F. Miyazaki, and T. Noguchi, “A stable tracking control method for an autonomous mobile robot,” IEEE Int. Conf. on robotics and automation, pp. 384-389, 1990.

[13] [13] G. Bradski and A. Kaehler, “Learning OpenCV, Computer Vision with the OpenCV Library,” O’Reilly Media, 2008.

[14] [14] A. Oliva and A. Torralba, “Building the gist of a scene: the role of global image features in recognition,” Progress in Brain Research, Vol.155, 2006.

[15] [15] J. Hays and A. A. Efros, “im2gps: estimating geographic information from a single image,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2008.

[16] [16] S. Yuta, “Tsukuba Challenge 2014: Open Experiment of Autonomous Navigation of Mobile Robots in the City,” SI2014, number 1F3-1, 2014 (in Japanese).