International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2569

Full Length Research Paper

Experimental investigations of immune fuzzy Q-learning algorithms for robot obstacle avoidance

Suchart Punpaisarn and Sarawut Sujitjorn*
The School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand.
Email: [email protected]

  •  Accepted: 11 February 2012
  •  Published: 16 November 2012


This article presents a new approach to car-like robot control for obstacle avoidance and target tracking. The proposed approach employs cooperative algorithms including artificial immune algorithms, fuzzy logic and Q-learning denoted shortly as IFQ-learning control. The article explains the artificial immune system and the proposed algorithms. The fuzzy Q-learning algorithms are also presented. The article elaborates the control design as well as extensive experimental results. Very satisfactory robot performances are achieved via the proposed IFQ-learning control. VDO clips illustrating the experiments are available on the website


Key words: Q-learning, artificial immune, fuzzy logic, robot, obstacle avoidance.