A reactive approach for mobile robot navigation in static and dynamic environment using fuzzy logic control

Boujelben, Maissa; Rekik, Chokri; Derbel, Nabil

doi:10.1504/ijmic.2017.084722

Cited by 5 publications

(4 citation statements)

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“…The actual trajectory tracking process will be disturbed by obstacles and other external environmental factors. Hence, the pose error will change significantly (Boujelben et al, 2017 ; Lu et al, 2017 ; Bencherif and Chouireb, 2019 ). Fuzzy logic can imitate the thinking way of the human brain and process systems with unknown models.…”

Section: Methodsmentioning

confidence: 99%

The Analysis of Trajectory Control of Non-holonomic Mobile Robots Based on Internet of Things Target Image Enhancement Technology and Backpropagation Neural Network

Zhao

Wang²,

Fan³

et al. 2021

Front. Neurorobot.

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The trajectory tracking and control of incomplete mobile robots are explored to improve the accuracy of the trajectory tracking of the robot controller. First, the mathematical kinematics model of the non-holonomic mobile robot is studied. Then, the improved Backpropagation Neural Network (BPNN) is applied to the robot controller. On this basis, a mobile robot trajectory tracking controller combining the fuzzy algorithm and the neural network is designed to control the linear velocity and angular velocity of the mobile robot. Finally, the robot target image can be analyzed effectively based on the Internet of Things (IoT) image enhancement technology. In the MATLAB environment, the performances of traditional BPNN and improved BPNN in mobile robots' trajectory tracking are compared. The tracking accuracy before and after the improvement shows no apparent differences; however, the training speed of improved BPNN is significantly accelerated. The fuzzy-BPNN controller presents significant improvements in tracking speed and tracking accuracy compared with the improved BPNN. The trajectory tracking controller of the mobile robot is designed and improved based on the fuzzy BPNN. The designed controller combining the fuzzy algorithm and the improved BPNN can provide higher accuracy and tracking efficiency for the trajectory tracking and control of the non-holonomic mobile robots.

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Section: Methodsmentioning

confidence: 99%

The Analysis of Trajectory Control of Non-holonomic Mobile Robots Based on Internet of Things Target Image Enhancement Technology and Backpropagation Neural Network

Zhao

Wang²,

Fan³

et al. 2021

Front. Neurorobot.

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show abstract

“…The NN has two inputs, one of them for finding the required distance from the instant position to the target. This calculation can be found by using (3). Another input for the second node is used to achieve the required rotation to make the mobile robot face the goal, after achieving this angle, the mobile robot can go directly to the goal.…”

Section: Path Tracking Control Using Nnmentioning

confidence: 99%

“…The path which results from this algorithm is a safe path with a large distance to obstacles but the robot cannot turn smoothly due to the sharp corner generated by the A* algorithm in the path. Fuzzy logic controller (FLC) is used in [3], [4] for finding the shortest and safest path and navigating in an unknown environment with different dynamic and static obstacles. Moreover, the type of robot used in that research has many constraints because it could not move in all directions without adjusting its rotation angle.…”

Section: Introductionmentioning

confidence: 99%

Multi-layer perceptron neural network mobile robot navigator in unknown environment

Al-Dahhan

Al‐Dahhan

Radeef

2023

IJEECS

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<span lang="EN-US">Recently, navigation in an unknown environment without hitting obstacles was considered a big challenge faced by researchers. The difficulty in finding a good mathematical model for the different systems is deciding to use artificial intelligent controllers to control the mobile robot movement. In this paper, designing two multi-layer-perceptron neural networks (MLP-NN) was done to control the movement of mobile robots in an unknown environment. The first MLP-NN is to control the linear velocity on the x-axis and angular velocity of the robot’s movement while the other MLP-NN is designed to avoid the static and dynamic obstacles faced by the robot while navigating in an unknown environment. The results show each controller's advantages in performing navigation tasks and avoiding obstacles in different environments.</span>

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“…It involves utilising the current position of the robot and moving to a target location based on a navigation strategy or algorithm. The navigation strategy could be based purely on reactive control (Chand 2015;Baklouti et al 2017;Boujelben et al 2017;Pandey 2017) or incorporate deliberative control (global path planner based) (Chand and Carnegie 2011;Patle et al 2019;Zhang 2019). Global path planners are well-suited for navigation in known environments.…”

Section: Introductionmentioning

confidence: 99%

Integrating an electronic compass for position tracking on a wheeled tricycle mobile robot

Chand

2022

Drone Syst. Appl.

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Dead-reckoning via encoders on wheeled-mobile robots is a simple but inaccurate method to estimate position. The major drawback of encoders is wheel slippage errors that accumulate over time. This problem is often addressed by using additional sensors such as compass, gyroscope, or GPS. This paper details the integration and effectiveness of a relatively low-cost solution using an electronic compass to reduce positioning error on a wheeled tricycle mobile robot. A customised Visual Studio program has been developed to adjust the settings of the electronic compass and integrate it with the Visual Studio based robot control system. The electronic compass heading data is fused with the encoder odometry heading data in three different ways: simple fusion, linear weighted fusion, and Kalman filter fusion. Simple fusion and linear weighted fusion rely on parameters determined from angular acceleration and angular velocity, respectively. The Kalman filter uses variance data for the encoders and electronic compass to determine an optimal heading. Experiments have been conducted in an indoor corridor environment to evaluate and compare the various fusion methods. Position error is successfully reduced and is sufficient to locate the robot within the corridor.

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A reactive approach for mobile robot navigation in static and dynamic environment using fuzzy logic control

Cited by 5 publications

References 0 publications

The Analysis of Trajectory Control of Non-holonomic Mobile Robots Based on Internet of Things Target Image Enhancement Technology and Backpropagation Neural Network

The Analysis of Trajectory Control of Non-holonomic Mobile Robots Based on Internet of Things Target Image Enhancement Technology and Backpropagation Neural Network

Multi-layer perceptron neural network mobile robot navigator in unknown environment

Integrating an electronic compass for position tracking on a wheeled tricycle mobile robot

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