Synchronous Mobile Robots Formation Control

Sapiee, Mohd Razali Mohamad; Annuar, Khalil Azha Mohd

doi:10.12928/telkomnika.v16i3.8397

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Generally speaking, the robot is large in size, and requires engineers from multiple disciplines to collaborate and discuss to complete all the work. The badminton robot developed by Sapiee and Annuar (2018) is composed of a robotic arm fixed on the rails of the court and an off-court processor. During training, the shuttlecocks flight trajectory is read through the two off-court cameras; the data is processed in the off-court desktop computer and is then transmitted back to the controller to control the motor-driven manipulator to hit the shuttlecock, which can realize the function of playing against an opponent (Sapiee and Annuar, 2018).…”

Section: Research Status Of Badminton Robotmentioning

confidence: 99%

“…The badminton robot developed by Sapiee and Annuar (2018) is composed of a robotic arm fixed on the rails of the court and an off-court processor. During training, the shuttlecocks flight trajectory is read through the two off-court cameras; the data is processed in the off-court desktop computer and is then transmitted back to the controller to control the motor-driven manipulator to hit the shuttlecock, which can realize the function of playing against an opponent (Sapiee and Annuar, 2018). Mizuno et al (2019) developed a fully automatic badminton game robot, which could receive and serve in the same environment as the human athlete; the Kinect sensor and the space shuttle in this system were applied to predict and detect the motion trajectory of the shuttlecock.…”

Section: Research Status Of Badminton Robotmentioning

confidence: 99%

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Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

2021

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This study was developed to explore the role of the intelligent badminton training robot (IBTR) to prevent badminton player injuries based on the machine learning algorithm. An IBTR is designed from the perspectives of hardware and software systems, and the movements of the athletes are recognized and analyzed with the hidden Markov model (HMM) under the machine learning. After the design was completed, it was simulated with the computer to analyze its performance. The results show that after the HMM is optimized, the recognition accuracy or data pre-processing algorithm, based on the sliding window segmentation at the moment of hitting reaches 96.03%, and the recognition rate of the improved HMM to the robot can be 94.5%, showing a good recognition effect on the training set samples. In addition, the accuracy rate is basically stable when the total size of the training data is 120 sets, after the accuracy of the robot is analyzed through different data set sizes. Therefore, it was found that the designed IBTR has a high recognition rate and stable accuracy, which can provide experimental references for injury prevention in athlete training.

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Section: Research Status Of Badminton Robotmentioning

confidence: 99%

Section: Research Status Of Badminton Robotmentioning

confidence: 99%

Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

2021

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Cooperative control method of robot formation movement path based on machine vision

Zhang¹

2022

JCM

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Because the formation path information is not extracted in the process of robot formation motion path cooperative control, the image processing time is long and the effect of avoiding obstacles to reach the target position is poor. Therefore, a robot formation motion path cooperative control method based on machine vision is proposed. Through the design of embedded chip control module, image acquisition module and communication module, the application process of machine vision technology is designed, and the target features in the motion path of robot formation are extracted, including other robot targets and obstacle targets in the formation. Then, the formation path information is extracted based on image preprocessing. On this basis, using the formation form of virtual structure-driver following method, the obstacle avoidance control of robot formation motion path is completed by potential field method. The experimental results show that after using this method, the images of its CCD camera when performing various processing are less than 200 ms. The moving environment image shows that this method has high processing performance for robot formation, and under the control of this method, the formation can change the formation and intelligent robot to avoid obstacles and reach the target position.

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Synchronous Mobile Robots Formation Control

Cited by 2 publications

References 3 publications

Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

Cooperative control method of robot formation movement path based on machine vision

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