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Vision-based obstacle avoidance system with fuzzy logic for humanoid robots

Published online by Cambridge University Press:  08 September 2016

Shu-Yin Chiang*
Affiliation:
Department of Information and Telecommunications Engineering, Ming Chuan University, 5 De Ming Road, Gui Shan Distract, Taoyuan City 333, Taiwan e-mail: [email protected]

Abstract

This study presents the algorithm for a humanoid robot to accomplish an obstacle run in the FIRA HuroCup competition. It includes the integration of image processing and robot motion. DARwIn-OP (Dynamic Anthropomorphic Robot with Intelligence–Open Platform) was used as the humanoid robot, and it is equipped with a webcam as a vision system to obtain an image of what is in front of the robot. Image processing skills such as erosion, dilation, and eight-connected component labeling are applied to reduce image noise. Moreover, we use navigation grids with filters to avoid the obstacles. Fuzzy logic rules are used to implement the robot’s motion, allowing a humanoid robot to access any routes using obstacle avoidance to perform the tasks in the obstacle-run event.

Type
Review Article
Copyright
© Cambridge University Press, 2016 

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References

Benet, G., Blanes, F., Simó, J. E. & Pérez, P. 2002. Using infrared sensors for distance measurement in mobile robots. Robotics and Autonomous Systems 40(4), 255266.Google Scholar
Budiharto, W., Moniaga, J., Aulia, M. & Aulia, A. 2013. A framework for obstacles avoidance of humanoid robot using stereo vision. International Journal of Advanced Robotic Systems 10, 17.Google Scholar
Chao, C.-H., Hsueh, B.-Y., Hsiao, M.-Y., Tsai, S.-H. & Li, T.-H. S. 2009. Fuzzy target tracking and obstacle avoidance of mobile robots with a stereo vision system. International Journal of Fuzzy Systems 11(3), 183191.Google Scholar
Chen, C.-Y., Chiang, S.-Y. & Wu, C.-T. 2014. Path planning and obstacle avoidance for omni-directional mobile robot based on Kinect depth sensor. In National Symposium on System Science and Engineering, June.Google Scholar
Hancock, J., Hebert, M. & Thorpe, C. 1998. Laser intensity-based obstacle detection intelligent robots and systems. In Proceedings of the IEEE Conference on Intelligent Robotic Systems, 3, 1541–1546.Google Scholar
He, L., Chao, Y., Suzuki, K. & Wu, K. 2009. Fast connected-component labeling. Pattern Recognition 42(9), 19771987.Google 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. Journal of Applied Science and Engineering 15(2), 187196.Google Scholar
Li, H. & Yang, S. X. 2002. Ultrasonic sensor based fuzzy obstacle avoidance behaviors. In Proceedings of the IEEE International Conference on System, Man and Cybernetics, 2, 644–649.Google Scholar
Li, T.-H. S., Chang, S.-J. & Tong, W. 2004. Fuzzy target tracking control of autonomous mobile robots by using infrared sensors. IEEE Transactions on Fuzzy Systems 12(4), 491501.Google Scholar
Mendel, J. M. 1995. Fuzzy logic systems for engineering: a tutorial. Proceedings of IEEE 83(3), 345377.Google Scholar
Soumare, S., Ohya, A. & Yuta, S. 2002. Real-time obstacle avoidance by an autonomous mobile robot using an active vision sensor and a vertically emitted laser slit. In Intelligent Autonomous Systems, 301–308.Google Scholar
Wong, C.-C., Hwang, C.-L., Huang, K.-H., Hu, Y.-Y. & Cheng, C.-T. 2011. Design and implementation of vision-based fuzzy obstacle avoidance method on humanoid robot. International Journal of Fuzzy Systems 13(1), 4554.Google Scholar