Application of Fuzzy Neural Network in Parameter Optimization of Mobile Robot Path Planning Using Potential Field

2007 ◽  
Author(s):  
Zhenwen Su ◽  
Bi Zeng ◽  
Guangchang Liu ◽  
Feng Ye ◽  
Minglin Xu
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Mobile Robot ◽  
Parameter Optimization ◽  
Potential Field ◽  
Fuzzy Neural Network ◽  
Robot Path Planning ◽  
Fuzzy Neural ◽  
Robot Path

An Adaptive Path Planning Based on Improved Fuzzy Neural Network for Multi-Robot Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 1396-1424
Author(s):  
Zhiguo Shi ◽  
Huan Zhang ◽  
Jingyun Zhou ◽  
Junming Wei
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Fuzzy Neural Network ◽  
Robot Path Planning ◽  
A Algorithm ◽  
Effective Performance ◽  
Robot Systems ◽  
Fuzzy Neural ◽  
Adaptive Control Strategy ◽  
Robot Path

The fuzzy neural network (FNN) is the combination of fuzzy theory with neural network, which has advantages of validity and adaptability in robot path planning. However, the path planning based on the FNN is not optimal because of the limitations of the subjective experience and motion mutation and the dead-zone. In this paper, FNN is improved by using A* graph search algorithm to guarantee an optimal path, providing the rationality and the feasibility, in which the grid map is divided into two stages, including the A* algorithm in the first stage and FNN in the second stage. In addition, a neural network based on adaptive control strategy is introduced to compensate the sensor failure and ensures the stability, which is caused by the loss of data and information uncertainty. The simulation results show that the approach is with effective performance in the robot path planning.

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