World Academy of Science, Engineering and Technology
Published June 26, 2011 | Version 4784
Journal article Open

Adaptive Path Planning for Mobile Robot Obstacle Avoidance

Creators

Description

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.

Files

4784.pdf

Files (305.6 kB)

Name Size Download all
md5:dd43d1cd8c82f9080a8720bdb945b739
305.6 kB Preview Download

Additional details

References

  • 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.
  • 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.
  • H. Seraji and A. Howard, ¶ÇÇü Behavior-based robot navigation on challenging terrain: a fuzzy logic approach, ¶ÇÇé IEEE Trans. Robot. Automat., vol. 18, no. 3, pp. 308¶ÇÇü321, 2002.
  • C. L. Hwang, L. J. Chang, and Y. S. Yu, ¶ÇÇüNetwork-based fuzzy decentralized sliding-mode control for car-like mobile robots,¶ÇÇé IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 574¶ÇÇü585, 2007.
  • 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.
  • 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.
  • J. H. Lilly, ¶ÇÇüEvolution of a negative-rule fuzzy obstacle avoidance controller for an autonomous vehicle,¶ÇÇé IEEE Trans. Fuzzy Syst., vol. 15, no. 4, pp. 718¶ÇÇü728, 2007.
  • Q. Li, W. Zhang, Y. Yin, Z. Wang, and G. Liu, ¶ÇÇüAn improved genetic algorithm of optimum path planning for mobile robots,¶ÇÇé Int. Conf. Intelligent Systems Design and Applications, vol. 2, pp. 637¶ÇÇü642, 2006.
  • 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. [11] W. Wu and Q. Ruan, ¶ÇÇüA gene-constrained genetic algorithm for solving shortest path problem,¶ÇÇé Int. Conf. Signal Processing, pp. 2510¶ÇÇü2513, 2004. [12] J. Borenstein and Y. Koren, ¶ÇÇüThe vector field histogram-fast obstacle avoidance for mobile robots,¶ÇÇé IEEE Trans. Robot. Automat., vol. 7, no. 3, pp. 278¶ÇÇü288, 1991. [13] A. Zhu and S. X. Yang, ¶ÇÇüNeurofuzzy-based approach to mobile robot navigation in unknown environments,¶ÇÇé IEEE Trans. Syst. Man, Cybern. C, vol. 37, no. 4, pp. 610¶ÇÇü621, 2007. [14] F. Amigoni and S. Gasparini, ¶ÇÇüBuilding segment-based maps without pose information,¶ÇÇé Proc. IEEE, vol. 94, no. 7, pp. 1340¶ÇÇü1359, 2006. [15] G. L. Mariottini, G. Oriolo, and D. Prattichizzo, ¶ÇÇüImage-based visual servoing for nonholonomic mobile robots using epipolar genmetry,¶ÇÇé IEEE Trans. Robotics, vol. 23, no. 1, pp. 87¶ÇÇü100, 2007. [16] M. Wang and J. N. K. Liu, ¶ÇÇü Fuzzy logic-based real-time robot navigation in unknown environment with dead ends, ¶ÇÇé Robot. Autonomous Syst., vol. 56, no. 7, pp. 625¶ÇÇü643, 2008. [17] J. Velagic, B. Lacevic, and B. Perunicic, ¶ÇÇüA 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches,¶ÇÇé Robot. Autonomous Syst., vol. 54, no. 12, pp. 989¶ÇÇü1004, 2006. [18] K. M. Krishna and P. K. Kalra, ¶ÇÇüPerception and remembrance of the environment during real-time navigation of a mobile robot,¶ÇÇé Robot. Autonomous Syst., vol. 37, pp. 25¶ÇÇü51, 2001. [19] M. Wang and J. N. K. Liu, ¶ÇÇüFuzzy logic based robot path planning in unknown environments,¶ÇÇé Int. Conf. Machine Learning and Cybernetics, vol. 2, pp. 813¶ÇÇü818, 2005. [20] G. Antonelli, S. Chiaverini, and G. Fusco, ¶ÇÇü A fuzzy-logic-based approach for mobile robot path tracking,¶ÇÇé IEEE Trans. Fuzzy Syst., vol. 15, no. 2, pp. 211¶ÇÇü221, 2007. [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.