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.

Authors:

• Rong-Jong Wai
• Chia-Ming Liu

Keywords:

• Adaptive Path Planning
• Mobile Robot ObstacleAvoidance

References:

[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.
[3] 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.
[4] 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.
[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.
[7] 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.
[8] 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.
[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.
[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.