Mobile Robot Navigation Based on Q-Learning Technique

Lazhar Khriji; Farid Touati; Kamel Benhmed; Amur Al-Yahmedi

doi:10.5772/10528

journal article Jan 01, 2011

Mobile Robot Navigation Based on Q-Learning Technique

Lazhar Khriji

Farid Touati

Kamel Benhmed Amur Al-Yahmedi

International Journal of Advanced Robotic Systems Vol. 8 No. 1 · SAGE Publications

View at Publisher Save 10.5772/10528

Abstract

This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.

Topics

No keywords indexed for this article. Browse by subject →

References

17

[1]

10.1016/s0888-613x(00)00056-6

[2]

Akira N. IJCSNS International Journal of Computer Science and Network Security (2008)

[3]

10.1109/irds.2002.1041610

[4]

Antonelli G.C. & Fusco S.G. (2007). A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking. IEEE Trans. on Systems, Man and Cybernetics: Systems and Humans, pp. 211–221. 10.1109/tfuzz.2006.879998

[5]

10.1109/70.88137

[6]

Cang Y. (2001)

[7]

Das T. & Kar I. (2006). Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots. IEEE Trans. on Control Systems Technology, pp. 501–510. 10.1109/tcst.2006.872536

[8]

Dongbing G. Part C: Applications and Reviews (2007)

[9]

Fatmi A. Inter. Journal of Applied Mathematics and Computer Sciences (2006)

[10]

10.1109/tfuzz.2004.832538

[11]

Hasegawa Y., Tanahashi H. & Fukuda T. (2001). Behavior coordination of brachiation robot based on behavior phase-shift. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 526–531. 10.1109/iros.2001.973410

[12]

10.1109/tie.2005.847576

[13]

Lanzi P.L. (2008). Learning classifier systems: then and now”, Evol. Intel. Springer-Verlag, pp.63–82. 10.1007/s12065-007-0003-3

[14]

Parasuraman S. (2005)

[15]

10.1109/3468.911370

[16]

Youcef Z. (2004)

[17]

Ziying Z., Rubo Z. & Xin L. (2008). Research on Hierarchical Fuzzy Behavior Learning of Autonomous Robot. Internet Computing in Science and Engineering, pp. 43–46. 10.1109/icicse.2008.60

Cited By

40

Mobile Robot Path Planning Using a QAPF Learning Algorithm for Known and Unknown Environments

Ulises Orozco-Rosas, Kenia Picos · 2022

IEEE Access

Metrics

40

Citations

17

References

Details

Published: Jan 01, 2011
Vol/Issue: 8(1)
License: View

Authors

L

Lazhar Khriji

Electrical and Computer Engineering Department, Sultan Qaboos University

F

Farid Touati

Electrical and Computer Engineering Department, Sultan Qaboos University

K

Kamel Benhmed

Electrical and Computer Engineering Department, Sultan Qaboos University

A

Amur Al-Yahmedi

Electrical and Computer Engineering Department, Sultan Qaboos University

Cite This Article

Lazhar Khriji, Farid Touati, Kamel Benhmed, et al. (2011). Mobile Robot Navigation Based on Q-Learning Technique. International Journal of Advanced Robotic Systems, 8(1). https://doi.org/10.5772/10528

Mobile Robot Navigation Based on Q-Learning Technique

You May Also Like