scholarly journals Same Fuzzy Logic Controller for Two-Wheeled Mobile Robot Navigation in Strange Environments

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Awatef Aouf ◽  
Lotfi Boussaid ◽  
Anis Sakly
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Mobile Robot Navigation ◽  
Planning Problem ◽  
New Techniques ◽  
Travelling Time ◽  
Optimal Motion Planning ◽  
Efficient Alternative ◽  
Continuous Work

For any mobile device, the ability to navigate smoothly in its environment is of paramount importance, which justifies researchers’ continuous work on designing new techniques to reach this goal. In this work, we briefly present a description of a hard work on designing a Same Fuzzy Logic Controller (S.F.L.C.) of the two reactive behaviors of the mobile robot, namely, “go to goal obstacle avoidance” and “wall following,” in order to solve its navigation problems. This new technique allows an optimal motion planning in terms of path length and travelling time; it is meant to avoid collisions with convex and concave obstacles and to achieve the shortest path followed by the mobile robot. The efficiency of employing the proposed navigational controller is validated when compared to the results from other intelligent approaches; its qualities make of it an efficient alternative method for solving the path planning problem of the mobile robot.

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.

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.

scholarly journals TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Awatef Aouf ◽  
Lotfi Boussaid ◽  
Anis Sakly
Keyword(s):  
Mobile Robot ◽  
Mobile Robot Navigation ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Navigation Problem ◽  
Travelling Time ◽  
Teaching Learning Based Optimization ◽  
Efficient Alternative ◽  
Teaching Learning

This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a mobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train the parameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal. The obtained results using the suggested algorithm are validated by comparison with different results from other intelligent algorithms such as particle swarm optimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot.

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