A High-Precision Collaborative Control Algorithm for Multi-Agent System Based on Enhanced Depth Image Fusion Positioning

Deng, Zhenghong; Zhang, Tianfan; Liú, Dan; Xiao, Jing; Li, Zhe

doi:10.1109/access.2020.2973344

Cited by 5 publications

(4 citation statements)

References 41 publications

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“…Usually, there are two ways to obtain skeleton points: one is to obtain point cloud data through special devices such as depth sensors [22], and then perform pose recognition through the point cloud, to obtain the skeleton and key points, which can ensure high recognition accuracy, but relying on special equipment with a huge amount of computation, and has a depth-of-field limitation, being unfavorable to the environment of multiple dancing [23]; the other one is realized by ordinary camera such as OpenPose pose recognition algorithm, which can obtain high accuracy under various working conditions with no need of special equipment, and also gradually becomes the mainstream scheme of pose recognition.…”

Section: Methodsmentioning

confidence: 99%

“…The above evaluations are highly dependent on expert experience. Combining the human movement as well as the structural characteristics of the human body, this paper chooses the 20 nodes defined by Deng et al [22] to represent the dance movements, as shown in Figure 3. Once a standard pose model is defined, the actual pose of the current test subject can be compared with the standard.…”

Section: Feature Description and Determination Of Dance Movementmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Standardized Evaluation and Human Pose Estimation: A Novel Approach to Motion Perception

Liu,

Zhang,

et al. 2023

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Motion perception, pivotal to myriad specialized tasks, necessitates the enhancement of proficiency through sustained repetition of perception-action cycles to meet standard benchmarks. Attaining advanced skill levels often demands additional practice. However, traditional pedagogical and evaluation systems predominantly hinge on the subjective experiences of instructors and evaluators. This dependence precipitates two principal challenges in the domain of motion perception-related professional work. Firstly, learners grapple with securing timely, adequate guidance during learning and practice, given the slow, trial-and-error nature of key point acquisition. Secondly, objective evaluation, fraught with instability, fails to consistently deliver accurate quantitative assessments, thereby adversely impacting the learning process. In response to these challenges, this study introduces a deep learning-based approach for standardized evaluation and human pose estimation. The methodology begins with the utilization of OpenPose for body joint detection. This is followed by a Deep Neural Network (DNN)-informed strategy for posture information extraction. Lastly, leveraging our team's extensive experience in dance instruction, a novel method for describing and discerning differences in dance movements is proposed. This approach enables a quantitative evaluation and provides intuitive feedback on the mechanics of dance movements, thereby enhancing the monitoring of participants' progress. Validated experimentally, the proposed methodology demonstrates precision in motion perception and quantitative evaluation. It not only offers practical guidance for enhancing the quality of dance instruction but also provides a valuable reference for other applications.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Description and Determination Of Dance Movementmentioning

confidence: 99%

Deep Learning-Based Standardized Evaluation and Human Pose Estimation: A Novel Approach to Motion Perception

Liu,

Zhang,

et al. 2023

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“…Aiming at the problem of low efficiency in traditional brand design methods, Deng and other scholars proposed a 3D modeling illustration design method combined with massive data information. This method can realize the innovative evaluation basis of brand illustration innovative design system by simulating the three-dimensional structure diagram in illustration design, but it has the problem of low efficiency [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Research on Brand Illustration Innovative Design Modeling Based on Industry 4.0

Liu

Ping

2022

Computational Intelligence and Neuroscience

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With China’s attention to the requirements of brand illustration innovative design, under the background of Industry 4.0, the advantages of applying 3D modeling method in the field of traditional illustration design are becoming more and more prominent. Based on this, this paper studies the modeling method of brand illustration innovative design based on Industry 4.0 and constructs a 3D analysis model of illustration design based on cattle cooperative hybrid algorithm. This paper innovates the innovative design method of brand illustration under the background of Industry 4.0 from the three aspects of illustration structure, illustration style, and illustration creation method, uses the cattle cooperative hybrid algorithm to quantify its innovation, and realizes the intelligent evaluation of its innovation and the quantitative representation of modeling method based on the illustration database. The experimental results show that the illustration innovative design analysis model based on cattle collaborative hybrid algorithm can effectively combine the industrial 4.0 background, realize the modeling innovation at the three-dimensional level, and significantly improve its innovative design efficiency and modeling success rate.

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“…The attention to collaborative control in MASs has increased in recent years, and it has been widely applied [1][2][3][4][5][6] in fields such as intelligent transportation systems, smart grids, and robot cooperation. Collaborative control enables intelligent agents to cooperate and coordinate with each other [7], leading to improved overall system performance, robustness, and reduced communication costs.…”

Section: Introductionmentioning

confidence: 99%

On Optimized Cooperative Control for Stochastic Nonlinear Multiagent Systems Using Switched-triggered Input

Wang,

Wang

et al. 2024

Preprint

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This article investigates the optimized backstepping (OB) cooperative control problem for stochastic nonlinear multiagent systems (MASs) with non-affine nonlinear faults. In addition, to save resources and achieve better system performance, a novel switched-triggered mechanism (STM) is proposed for updating the actual controller. Based on the identifier-critic-actor structure and OB technique, the optimal controller is constructed. Additionally, a Butterworth low-pass filter has been incorporated to tackle the challenge of non-affine nonlinear faults. Finally, It is proved that all signals of the controlled system are bounded in probability, and the output of each follower can accurately track the leader's output. Simulation results are presented to validate the effectiveness of the proposed control scheme.

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A High-Precision Collaborative Control Algorithm for Multi-Agent System Based on Enhanced Depth Image Fusion Positioning

Cited by 5 publications

References 41 publications

Deep Learning-Based Standardized Evaluation and Human Pose Estimation: A Novel Approach to Motion Perception

Deep Learning-Based Standardized Evaluation and Human Pose Estimation: A Novel Approach to Motion Perception

Research on Brand Illustration Innovative Design Modeling Based on Industry 4.0

On Optimized Cooperative Control for Stochastic Nonlinear Multiagent Systems Using Switched-triggered Input

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