A Deep Learning Approach for the Mobile-Robot Motion Control System

Farkh, Rihem; jaloud, Khaled Al; Alhuwaimel, Saad; Quasim, Mohammad Tabrez; Ksouri, Mekki

doi:10.32604/iasc.2021.016219

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…After that, the features of the data are extracted and selected, and each class of data is assigned with the corresponding classification labels. The classification model is obtained by training the classification algorithm using the training set data and then validating the model using the validation set data [6]. After optimizing the model parameters several times to determine a classification model with the best classification effect, the final classification model can be used to obtain classification results in the actual action recognition process, as shown in Figure 1.…”

Section: Badminton Action Recognition Processmentioning

confidence: 99%

Research on badminton path tracking algorithm based on machine vision

Ye,

Huang,

Chen

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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Section: Badminton Action Recognition Processmentioning

confidence: 99%

Research on badminton path tracking algorithm based on machine vision

Ye,

Huang,

Chen

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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“…In the same direction, the authors in [11] have built a robotics platform only for 25 USD (2018) to implement a line-follower application where several skills related to sensors, Bluetooth communication and microcontrollers' programming have been improved in a STEM framework. More successful STEM implementations using robotics platforms can be found in [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Although these studies are distributed over different periods and were designed using different tools and resources, they all led to results promoting the capability of thinking as well as improving 21 st century skills.…”

Section: Related Workmentioning

confidence: 99%

“…In this study, the MR platform has been designed to be functional, low-cost, and extendable to other applications [27]. The following parts have been assembled to construct the MR:…”

Section: Hardware Specificationmentioning

confidence: 99%

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Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

Alshammrei¹,

Boubaker²,

Kolsi³

2022

Computers, Materials &Amp; Continua

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Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots (MRs) in both research and education. In this paper, an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm. To achieve this research objectives, first, the MR obstacle-free environment is modeled as a diagraph including nodes, edges and weights. Second, Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point. During its movement, the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node. For this aim, the MR was equipped with an ultrasonic sensor used as obstacle detector. If an obstacle is found, the MR updates its diagraph by excluding the corresponding node. Then, Dijkstra algorithm runs on the modified diagraph. This procedure is repeated until reaching the target point. To verify the efficiency of the proposed approach, a simulation was carried out on a hand-made MR and an environment including 9 nodes, 19 edges and 2 obstacles. The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors. This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

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“…The path is usually predetermined and can be visible (for example, a black line on a white surface with high contrast colors) or invisible (for example, a magnetic field). [11][12][13]. Various sensing systems can be used to identify these unique markers or lines.…”

Section: Introductionmentioning

confidence: 99%

Vision Navigation Based PID Control for Line Tracking Robot

Farkh¹,

Aljaloud²

2023

Intelligent Automation &Amp; Soft Computing

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In a controlled indoor environment, line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots. A line tracking robot is a self-mobile machine that can recognize and track a painted line on the floor. In general, the path is set and can be visible, such as a black line on a white surface with high contrasting colors. The robot's path is marked by a distinct line or track, which the robot follows to move. Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation. Localization, automated map generation, autonomous navigation and path tracking is all becoming more frequent in vision applications. A visual navigation line tracking robot should detect the line with a camera using an image processing technique. The paper focuses on combining computer vision techniques with a proportional-integral-derivative (PID) controller for automatic steering and speed control. A prototype line tracking robot is used to evaluate the proposed control strategy.

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A Deep Learning Approach for the Mobile-Robot Motion Control System

Cited by 11 publications

References 15 publications

Research on badminton path tracking algorithm based on machine vision

Research on badminton path tracking algorithm based on machine vision

Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

Vision Navigation Based PID Control for Line Tracking Robot

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