scholarly journals Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1254 ◽  
Author(s):  
Cheng-Hung Chen ◽  
Shiou-Yun Jeng ◽  
Cheng-Jian Lin
Keyword(s):  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Search Algorithm ◽  
Experimental Results ◽  
Average Error ◽  
Stopover Site ◽  
Accumulated Reward ◽  
Wall Following

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

scholarly journals Implementation of Fuzzy Logic Controller for Wall Following and Obstacle Avoiding Robot

2019 ◽  
Vol 4 (1) ◽  
pp. 9-21
Author(s):  
Aryuanto Soetedjo ◽  
M. Ibrahim Ashari ◽  
Cosnas Eric Septian
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Ultrasonic Sensors ◽  
Obstacle Avoiding ◽  
Wall Following

This paper presents the development of wall following and obstacle avoiding robot using a Fuzzy Logic Controller. The ultrasonic sensors are employed to measure the distances between robot and the wall, and between the robot and the obstacle. A low cost Raspberry Pi camera is employed to measure the left/right distance between the robot and the obstacle. The Fuzzy Logic Controller is employed to steer the mobile robot to follow the wall and avoid the obstacle according to the multi sensor inputs. The outputs of Fuzzy Logic Controller are the speeds of left motor and right motor. The experimental results show that the developed mobile robot could be controlled properly to follow the different wall positions and avoid the different obstacle positions with the high successful rate of 83.33%.

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.

Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

2019 ◽  
Vol 20 (1) ◽  
pp. 51-62
Author(s):  
Rajmeet Singh ◽  
Tarun Kumar Bera
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Bond Graph ◽  
Membership Functions ◽  
Current Position ◽  
Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

Obstacle avoidance and path planning of humanoid robot using fuzzy logic controller aided owl search algorithm in complicated workspaces

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhishek Kumar Kashyap ◽  
Dayal R. Parhi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Search Algorithm ◽  
Optimal Path ◽  
Humanoid Robots ◽  
Steering Angle ◽  
Content Type ◽  
Hybrid Controller ◽  
Humanoid Nao

Purpose This paper aims to outline and implement a novel hybrid controller in humanoid robots to map an optimal path. The hybrid controller is designed using the Owl search algorithm (OSA) and Fuzzy logic. Design/methodology/approach The optimum steering angle (OS) is used to deal with the obstacle located in the workspace, which is the output of the hybrid OSA Fuzzy controller. It is obtained by feeding OSA's output, i.e. intermediate steering angle (IS), in fuzzy logic. It is obtained by supplying the distance of obstacles from all directions and target distance from the robot's present location. Findings The present research is based on the navigation of humanoid NAO in complicated workspaces. Therefore, various simulations are performed in a 3D simulator in different complicated workspaces. The validation of their outcomes is done using the various experiments in similar workspaces using the proposed controller. The comparison between their outcomes demonstrates an acceptable correlation. Ultimately, evaluating the proposed controller with another existing navigation approach indicates a significant improvement in performance. Originality/value A new framework is developed to guide humanoid NAO in complicated workspaces, which is hardly seen in the available literature. Inspection in simulation and experimental workspaces verifies the robustness of the designed navigational controller. Considering minimum error ranges and near collaboration, the findings from both frameworks are evaluated against each other in respect of specified navigational variables. Finally, concerning other present approaches, the designed controller is also examined, and major modifications in efficiency have been reported.

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