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Localization and obstacle avoidance in soccer competition of humanoid robot by gait and vision system

Part of: Robot Athletes and Entertainers

Published online by Cambridge University Press:  20 November 2019

Shu-Yin Chiang Open the ORCID record for Shu-Yin Chiang [Opens in a new window]  and
Jia-Huei Lu
Shu-Yin Chiang*
Affiliation:
Department of Information and Telecommunications Engineering, Ming Chuan University, 5 De Ming Road, Gui Shan District, Taoyuan City 333, Taiwan; e-mail: [email protected]
Jia-Huei Lu
Affiliation:
Department of Information and Telecommunications Engineering, Ming Chuan University, 5 De Ming Road, Gui Shan District, Taoyuan City 333, Taiwan; e-mail: [email protected]
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Abstract

In this study, we designed a localization and obstacle avoidance system for humanoid robots in the Federation of International Robot-soccer Association (FIRA) HuroCup united soccer competition event. The localization is implemented by using grid points, gait, and steps to determine the positions of each robot. To increase the localization accuracy and eliminate the accumulated distance errors resulting from step counting, the localization is augmented with image pattern matching using a system model. The system also enables the robot to determine the ball’s position on the field using a color model of the ball. Moreover, to avoid obstacles, the robots calculate the obstacle distance using data extracted from real-time images and determine a suitable direction for movement. With the integration of this accurate self-localization algorithm, ball identification scheme, and obstacle avoidance system, the robot team is capable of accomplishing the necessary tasks for a FIRA soccer game.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e16
Copyright
© Cambridge University Press, 2019 

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References

Awaludin, I., Hidayatullah, P., Hutahaean, J. & Parta, D. G. 2013. Detection and object position measurement using computer vision on humanoid soccer. In 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), 88–92.Google Scholar
Chiang, J. -S., Hsia, C. -H., Chang, S. -H., Chang, W. -H., Hsu, H. -W., Tai, Y. -C., Li, C. -Y. & Ho, M. -H. 2010. An efficient object recognition and self-localization system for humanoid soccer robot. In Proceedings of SICE Annual Conference, 2269–2278.Google Scholar
Chiang, J. -S., Hsia, C. -H. & Hsu, H. -W. 2013. A stereo vision-based self-localization system. IEEE Sensors Journal 13, 1677–1689.CrossRefGoogle Scholar
Chiang, J. -S., Hsia, C. -H., Hsu, H. -W. & Li, C. -I. 2011. Stereo vision-based self-localization system for RoboCup. In IEEE International Conference on Fuzzy Systems, 2763–2770.Google Scholar
Chiang, S. -Y. 2016. Vision-based obstacle avoidance system with fuzzy logic for humanoid robots. The Knowledge Engineering Review 32, 1–11.Google Scholar
Chiang, S. -Y., Guo, X. & Hu, H. -W. 2014. Real time self-localization of omni-vision robot by pattern match system. In International Conference on Advanced Robotics and Intelligent System, June.CrossRefGoogle Scholar
Chiang, S. -Y., Wei, C. -A. & Chen, C. -Y. 2016. Real-time self-localization of a mobile robot by vision and motion system. International Journal of Fuzzy Systems 18, 999–1007.CrossRefGoogle Scholar
Ha, I., Tamura, Y. & Asama, H. 2011. Gait pattern generation and stabilization for humanoid robot based on coupled oscillators. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 3207–3212.Google Scholar
He, L., Chao, Y., Suzuki, K. & Wu, K. 2009. Fast connected-component labeling. Pattern Recognition 42, 1977–1987.CrossRefGoogle Scholar
Hsia, C.-H., Chang, W.-H. & Chiang, J.-S. 2012. A real-time object recognition system using adaptive resolution method for humanoid robot vision development. In Journal of Applied Science and Engineering 15(2), 187–196.Google Scholar
Menegatti, E., Pretto, A., Scarpa, A. & Pagello, E. 2006. Omnidirectional vision scan matching for robot localization in dynamic environments. IEEE Transactions on Robotics 22, 523–535.CrossRefGoogle Scholar
Merke, A., Welker, S. & Riedmiller, M. 2004. Line based robot localization under natural light conditions. In Workshop on Agents in Dynamic and Real Time Environments.Google Scholar
Minakata, H., Hayashibara, Y., Ichizawa, K., Horiuchi, T., Fukuta, M., Fujita, S., Kaminaga, H., Irieand, K. & Sakamoto, H. 2008. A method of single camera robocup humanoid robot localization using cooperation with walking control. In 2008 10th IEEE International Workshop on Advanced Motion Control.Google Scholar
Rodriguez, D., Farazi, H., Allgeuer, P., Ficht, G., Pavlichenko, D., Brandenburger, A., Kürsch, J. & Behnke, S. 2018. Advanced soccer skills and team play of RoboCup 2017 TeenSize Winner NimbRo. In RoboCup 2017: Robot World Cup XXI, LNCS 11175, 448–460.Google Scholar