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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 31108

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Adaptive Path Planning for Mobile Robot Obstacle Avoidance
Generally speaking, the mobile robot is capable of sensing its surrounding environment, interpreting the sensed information to obtain the knowledge of its location and the environment, planning a real-time trajectory to reach the object. In this process, the issue of obstacle avoidance is a fundamental topic to be challenged. Thus, an adaptive path-planning control scheme is designed without detailed environmental information, large memory size and heavy computation burden in this study for the obstacle avoidance of a mobile robot. In this scheme, the robot can gradually approach its object according to the motion tracking mode, obstacle avoidance mode, self-rotation mode, and robot state selection. The effectiveness of the proposed adaptive path-planning control scheme is verified by numerical simulations of a differential-driving mobile robot under the possible occurrence of obstacle shapes.
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[1] T. C. Lee, C. Y. Tsai, and K. T. Song, ¶ÇÇüFast parking control of mobile robots: a motion planning approach with experimental validation,¶ÇÇé IEEE Trans. Contr. Syst. Technol., vol. 12, no. 5, pp. 661¶ÇÇü676, 2004.
[2] T.-H. S. Li, S. J. Chang, and Y. X. Chen, ¶ÇÇüImplementation of human-like driving skills by autonomous fuzzy behavior control on an FPGA-based car-like mobile robot,¶ÇÇé IEEE Trans. Ind. Electron., vol. 50, no. 5, pp. 867¶ÇÇü880, 2003.
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[5] W. Tsui, M. S. Masmoudi, F. Karray, I. Song, and M. Masmoudi, ¶ÇÇü Soft-computing-based embedded design of an intelligent wall/lane-following vehicle,¶ÇÇé IEEE/ASME Trans. Mechatronics, vol. 13, no. 1, pp. 125¶ÇÇü135, 2008.
[6] C. Ye, H. C. Yung, and D. Wang, ¶ÇÇüA fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance,¶ÇÇé IEEE Trans. Syst. Man, Cybern. B, vol. 33, no. 1, pp. 17¶ÇÇü27, 2003.
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[9] J. Tu and S. Yang, ¶ÇÇüGenetic algorithm based path planning for a mobile robot,¶ÇÇé IEEE Int. Conf. Robotics and Automation, pp. 1221¶ÇÇü1226, 2003.
[10] Y. Hu and S. Yang, ¶ÇÇüA knowledge based genetic algorithm for path planning of a mobile robot,¶ÇÇé IEEE Int. Conf. Robotics and Automation, pp. 4350¶ÇÇü4355, 2004.
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[21] S. J. Yoo, Y. H. Choi, and J. B. Park, ¶ÇÇüGeneralized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach,¶ÇÇé IEEE Trans. Circuit Syst. I, vol. 53, no. 6, pp. 1381¶ÇÇü1394, 2006.
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