Fuzzy Logic Controller with Fuzzylab Python Library and the Robot Operating System for Autonomous Mobile Robot Navigation

2020 ◽  
Vol 14 (1) ◽  
pp. 48-54
Author(s):  
Eduardo Avelar ◽  
Oscar Castillo ◽  
José Soria
Keyword(s):  
Operating System ◽  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Autonomous Mobile Robot ◽  
Robot Operating System

Optimization of Fuzzy Logic Controller Using PSO for Mobile Robot Navigation in an Unknown Environment

2014 ◽  
Vol 541-542 ◽  
pp. 1053-1061 ◽  
Author(s):  
Mohammed Algabri ◽  
Hedjar Ramdane ◽  
Hassan Mathkour ◽  
Khalid Al-Mutib ◽  
Mansour Alsulaiman
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Membership Function ◽  
Fuzzy Logic Controller ◽  
Robot Navigation ◽  
Field Performance ◽  
Mobile Robot Navigation ◽  
Autonomous Mobile Robot ◽  
Swarm Optimization ◽  
The Membership Function

The control of autonomous mobile robot in an unknown environments include many challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy logic controlling depends on the membership function, so the membership function adjusting is a time consuming process. In this paper, we optimized a fuzzy logic controller (Fuzzy) by automatic adjusting the membership function using a particle swarm optimization (PSO). The proposed method (PSO-Fuzzy) is implemented and compared with Fuzzy using Khepera simulator. Moreover, the performance of these approaches compared through experiments using a real Khepera III platform.

Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation

2004 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Devendra P. Garg
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
Path Tracking ◽  
Mobile Robot Navigation ◽  
Reinforcement Learning Algorithm

This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot’s path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm.

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