Fuzzy and Neuro-fuzzy Based Co-operative Mobile robots

2006 ◽  
pp. 578-583 ◽  
Author(s):  
D.T. Pham ◽  
M.H. Awadalla ◽  
E.E. Eldukhri
Keyword(s):  
Mobile Robots ◽  
Neuro Fuzzy

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

2017 ◽  
Vol 31 (S2) ◽  
pp. 1275-1289 ◽  
Author(s):  
Weria Khaksar ◽  
Tang Sai Hong ◽  
Khairul Salleh Mohamed Sahari ◽  
Mansoor Khaksar ◽  
Jim Torresen
Keyword(s):  
Tabu Search ◽  
Motion Planning ◽  
Mobile Robots ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Optimized Neuro-fuzzy Coordination for Multiple Four Wheeled Mobile Robots

2010 ◽  
Vol 9 (8) ◽  
pp. 1557-1570 ◽  
Author(s):  
H. Mehrjerdi ◽  
M. Saad ◽  
J. Ghommam ◽  
A. Zerigui
Keyword(s):  
Mobile Robots ◽  
Wheeled Mobile Robots ◽  
Neuro Fuzzy

Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment

2010 ◽  
Vol 224 (6) ◽  
pp. 1369-1381 ◽  
Author(s):  
D R Parhi ◽  
M K Singh
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Fuzzy Inference ◽  
Dynamic Environment ◽  
Fuzzy Logic System ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Logic System

This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include the front obstacle distance, the left obstacle distance, the right obstacle distance, and target steering. A learning algorithm based on the neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results; this proves the authenticity of the theory developed.

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