Neural-fuzzy controller for real-time mobile robot navigation

Author(s):  
Kim C. Ng ◽  
Mohan M. Trivedi
Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 466
Author(s):  
Cheng-Hung Chen ◽  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng ◽  
Hsueh-Yi Lin ◽  
Cheng-Yi Yu

This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control.


2015 ◽  
Vol 76 ◽  
pp. 449-454 ◽  
Author(s):  
J. Hossen ◽  
S. Sayeed ◽  
A.K.M. Parvez Iqbal

1990 ◽  
Vol 2 (1) ◽  
pp. 35 ◽  
Author(s):  
R.A. Lotufo ◽  
A.D. Morgan ◽  
E.L. Dagless ◽  
D.J. Milford ◽  
J.F. Morrissey ◽  
...  

Author(s):  
Maryam Refaee ◽  
Mohammad Hossein Kazemi ◽  
MohammadAli Nekoui ◽  
S. Amir Ghoreishi

2003 ◽  
Vol 52 (4) ◽  
pp. 1335-1340 ◽  
Author(s):  
P. Rusu ◽  
E.M. Petriu ◽  
T.E. Whalen ◽  
A. Cornell ◽  
H.J.W. Spoelder

Sign in / Sign up

Export Citation Format

Share Document