Robotic seam tracking system combining convolution filter and deep reinforcement learning

Zou, Yanbiao; Chen, Tao; Chen, Xiangzhi; Li, Jinchao

doi:10.1016/j.ymssp.2021.108372

Cited by 45 publications

(13 citation statements)

References 19 publications

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“…As shown in Table 1, the training of the model is done on a personal notebook, using a professional industrial camera and data acquisition software for datasets acquisition, and a server environment with running ability of deep learning is built. HALCON is a complete standard machine vision algorithm package developed by MVtec [23] , and it has a widely used machine vision integrated development environment, which saves product costs and shortens the software development cycle because of HALCON's flexible architecture facilitates the rapid development of machine vision, medical images and image analysis applications [24] . It has been recognized as the machine vision software with the best performance in the industrial circles of Europe and Japan [25] .…”

Section: Figure 1 Semantic Segmentation Model In Halconmentioning

confidence: 99%

Intelligent Deployment Solution for Tabling Adapting Deep Learning

You

Liu

2023

IEEE Access

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The deep learning object detection model can well extract the coordinate information of the separating point of the concentrate ore belt boundary line. However, with the drawbacks of acquired zonal image features, it is difficult to obtain the perfect prediction of the concentrate grade and recovery rate. In this study, by adapting deep learning semantic segmentation technique with DeepLab V3+, which can effectively extract multi-dimensional image features. The mapping relationship between image features and attributes of the Tabling equipment is achieved by constructing a multi-output support vector regression model optimized of a sparrow search algorithm (SSA-MSVR). The beneficiation indicators and operating parameters of the Tabling can be continually detected and optimized by an intelligent system that mainly includes image recognition softwares, automatic control units, data processing and communication workstations, and matching intelligent equipment, which accomplished the intelligent deployment solution of the Tabling beneficiation process.

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Section: Figure 1 Semantic Segmentation Model In Halconmentioning

confidence: 99%

Intelligent Deployment Solution for Tabling Adapting Deep Learning

You

Liu

2023

IEEE Access

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“…r 2 is the decay rate, which is a negative constant. The normalized process is usually used in the reward function design [45][46][47], and is calculated by I w /I n . I w is the IAE value obtained by subtracting the command speed and the measured output speed.…”

Section: Reward Function Designmentioning

confidence: 99%

PMSM Speed Control Based on Particle Swarm Optimization and Deep Deterministic Policy Gradient under Load Disturbance

et al. 2021

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Proportional integral-based particle swarm optimization (PSO) and deep deterministic policy gradient (DDPG) algorithms are applied to a permanent-magnet synchronous motor to track speed control. The proposed methods, based on notebooks, can deal with time delay challenges, imprecise mathematical models, and unknown disturbance loads. First, a system identification method is used to obtain an approximate model of the motor. The load and speed estimation equations can be determined using the model. By adding the estimation equations, the PSO algorithm can determine the sub-optimized parameters of the proportional-integral controller using the predicted speed response; however, the computational time and consistency challenges of the PSO algorithm are extremely dependent on the number of particles and iterations. Hence, an online-learning method, DDPG, combined with the PSO algorithm is proposed to improve the speed control performance. Finally, the proposed methods are implemented on a real platform, and the experimental results are presented and discussed.

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“…To monitor the changes in the workpiece during welding, methods using vision sensors have been studied [12][13][14]. However, for the welding processes which generate a large amount of fumes owing to the high flux inherent to the process, such as flux-cored arc welding (FCAW), the vision sensors are hardly applied.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform

Jang,

Lee,

Lee

et al. 2023

Science and Technology of Welding and Joining

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A monitoring technique for detecting changes in the root gap of butt joints during the flux-cored arc welding (FCAW) was proposed. FCAW experiments were conducted for both increasing and decreasing root gap conditions, and current and voltage were measured during the root-pass welding. The measured time series signals were used as input data for training Random Convolution Kernel Transform (ROCKET) algorithm, which consists of a feature extractor with multiple random kernels, and a linear classifier. A univariate model using current and voltage, respectively, and a multivariate model using both were compared, and the multivariate model showed the highest classification accuracy of 96.2%. Moreover, the classification errors were investigated by correlating the geometry of the root bead with the measured signals.

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Robotic seam tracking system combining convolution filter and deep reinforcement learning

Cited by 45 publications

References 19 publications

Intelligent Deployment Solution for Tabling Adapting Deep Learning

Intelligent Deployment Solution for Tabling Adapting Deep Learning

PMSM Speed Control Based on Particle Swarm Optimization and Deep Deterministic Policy Gradient under Load Disturbance

Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform

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