Obstacle avoidance of a mobile robot using hybrid learning approach

IEEE Transactions on Industrial Electronics

Volumn 52, Issue 3, 2005, Pages 898-905

  Er, Meng Joo ^a   Deng, Chang ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Fuzzy control; Reinforcement learning; Supervised learning

Indexed keywords

COLLISION AVOIDANCE; COMPUTER SIMULATION; FUZZY CONTROL; IDENTIFICATION (CONTROL SYSTEMS); LEARNING ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; ROBOT LEARNING;

HYBRID LEARNING; REINFORCEMENT LEARNING; SUPERVISED LEARNING;

MOBILE ROBOTS;

EID: 21044436480     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2005.847576     Document Type: Article

References (18)

1. R. A. Brooks, "A robust layered control system for a mobile robot," IEEE J. Robot. Automat., vol. 2, no. 1, pp. 14-23, Mar. 1986.
2. R. C. Arkin, Behavior-Based Robotics. Cambridge, MA: MIT Press, 1998.
3. M. Colombetti and M. Dorigo et al., "Behavior analysis and training - A methodology for behaviors engineering," IEEE Trans. Syst. Man, Cybern. B, Cybern., vol. 26, no. 3, pp. 365-380, Jun. 1996.
4. A. Saffiotti, "Fuzzy logic in autonomous robotics: behavior coordination," in Proc. 6th IEEE Int. Conf. Fuzzy Systems, vol. 1, 1997, pp. 573-578.
5. J. Godjevac and N. Steele, "Neuro-fuzzy control for basic mobile robot behaviors," in Fuzzy Logic Techniques for Autonomous Vehicle Navigation, D. Driankov and A. Saffiotti, Eds. Heidelberg, Germany: Physica-Verlag, 2001.
6. P. Reigner and V. Hansen et al., "Incremental supervised learning for mobile robot reactive control," Robot. Autonomous Syst., vol. 19, pp. 247-257, 1997.
7. C. Ribeiro, "Reinforcement learning agents," Artif. Intell. Rev., vol. 17, pp. 223-250, 2002.
8. L. Jouffe, "Fuzzy inference system learning by reinforcement methods," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 28, no. 3, pp. 338-355, Aug. 1998.
9. C. T. Lin and C. S. G. Lee, "Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems," IEEE Trans. Fuzzy Syst., vol. 2, no. 1, pp. 46-63, Feb. 1994.
10. C. Ye and N. H. C. Yung et al., "A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 33, no. 1, pp. 17-27, Feb. 2003.
11. N. E. Sharkey, "Learning from innate behaviors: a quantitative evaluation of neural network controllers," Mach. Learn., vol. 31, pp. 115-139, 1998.
12. S. Wu and M. J. Er et al., "A fast approach for automatic generation of fuzzy roles by generalized dynamic fuzzy neural networks," IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp. 578-594, Aug. 2001.
13. F. Mondada and E. Franzi et al., "Mobile robot miniaturization: a tool for investigation in control algorithms," Informatik, pp. 17-20, Feb. 1994.
14. T. Fukuda and N. Kubota, "An intelligent robotic system based on a fuzzy approach," Proc. IEEE, vol. 87, no. 9, pp. 1448-1470, Sep. 1999.
15. T. Nilsson. (2001, Mar.) KiKS is a Khepera Simulator. [Online] Available: http://www.kiks.f2s.com
16. V. Braitenberg, Vehicles. Cambridge, MA: MIT Press, 1984.
17. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Englewood Cliffs, NJ: Prentice-Hall, 1997.
18. R. J. Willian, "Simple statistical gradient-following algorithms for connectionist reinforcement learning," Mach. Learn., vol. 8, pp. 229-256, 1992.