Local logic optimization algorithm for autonomous mobile robot based on fuzzy logic

2018 ◽  
Author(s):  
Fei Xu ◽  
ShaoChang Wang ◽  
WeiXia Yang
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Autonomous Mobile Robot ◽  
Logic Optimization ◽  
Local Logic

scholarly journals Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

2020 ◽  
Vol 89 ◽  
pp. 106076 ◽  
Author(s):  
Fatin H. Ajeil ◽  
Ibraheem Kasim Ibraheem ◽  
Mouayad A. Sahib ◽  
Amjad J. Humaidi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Autonomous Mobile Robot ◽  
Multi Objective

scholarly journals Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hajer Omrane ◽  
Mohamed Slim Masmoudi ◽  
Mohamed Masmoudi
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Mobile Robot Navigation ◽  
Infrared Range ◽  
Indoor Environments ◽  
Autonomous Mobile Robot ◽  
Ir Sensors ◽  
Navigation Algorithms ◽  
Trajectory Tracking Controller

This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path.

scholarly journals Optimal tracking control of a trajectory planned via fuzzy reactive approach for an autonomous mobile robot

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141876062 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Objective Function ◽  
Tracking Control ◽  
Autonomous Mobile Robot ◽  
Linear Quadratic ◽  
Optimal Tracking ◽  
Tracking Controller ◽  
Scenarios Simulation ◽  
Simulation Results

This article deals with the design of an optimal tracking controller for a wheeled mobile robot. The tracking control can be performed to track either a given or a planned trajectory. In our study, an improved linear quadratic tracker is adopted to track a path planned using an improved reactive approach that combines the dynamic window with the fuzzy logic to make the robot movement toward the target faster, smoother, and safer whatever the complexity of the environment. The fuzzy logic is used to dynamically adjust the weights of the terms included in the dynamic window objective function according to different environmental scenarios. Simulation results of the path planning and the tracking control prove that the proposed approaches are significantly superior to the conventional ones.

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.

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