Internal Model Control Structure Inspired Robotic Calligraphy System

Wu, Ruiqi; Chao, Fei; Zhou, Changle; Chang, Xiang; Yang, Longzhi; Shang, Changjing; Zhang, Zihao; Shen, Qiang

doi:10.1109/tii.2023.3295415

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Second, a set of angular calibration brushstrokes as shown in Figure 6b should be painted with different values of z to obtain a reliable estimation of the brush parameter r and its dependency on z, which is described as a polynomial like (25).…”

Section: Discussion: Recommended Calibration Algorithmmentioning

confidence: 99%

“…Nevertheless, only a few words were said about the method of brush calibration and its theoretical justification. A notable question for creating a data-driven brush footprint model was considered in a work [24], and a feedback-based approach based on an internal model control for a calligraphy robot was described in [25]. One of the most detailed models of a brush was described in [26].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Physically Motivated Model of a Painting Brush for Robotic Painting and Calligraphy

Karimov,

Strelnikov,

Mazin

et al. 2024

Robotics

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Robot artistic painting and robot calligraphy do require brush models for brushstroke simulation and painting robot control. One of the main features of the brush is its compliance, which describes the relationship between the brush footprint shape and the pressure applied to the brush. In addition, during motion, the brush footprint position lags from the brush handle position in a complicated manner. To date, the question of creating a physically correct model of these effects and choosing the best method for the model parameter calibration has not been presented in the literature. In the current paper, we derive equations of the brush contact patch motion, give their closed-form solutions, and investigate three methods for the brush model calibration: capturing brush footprints on a matte glass with a camera, painting calibration brushstrokes, and capturing a brush shape side projection with a camera. As we show, calibration brushstrokes give us primary information on brush contact patch displacement during painting, and capturing the brush side projection allows the accurate estimation of the gap from the brush tip to the center of the contact patch. Capturing brush footprints is useful for creating a brushstroke executable model. As an example, a model for a round artistic brush was created and verified in three tests, including measuring the coordinates of an angular brushstroke center line, simulating an angular brushstroke, and writing a signature using a robotic setup.

show abstract

Section: Discussion: Recommended Calibration Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Physically Motivated Model of a Painting Brush for Robotic Painting and Calligraphy

Karimov,

Strelnikov,

Mazin

et al. 2024

Robotics

View full text Add to dashboard Cite

show abstract

RoDAL: style generation in robot calligraphy with deep adversarial learning

Wang,

Gong

2024

Appl Intell

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An End-to-End Generation Model for Chinese Calligraphy Characters Based on Dense Blocks and Capsule Network

Zhang,

Sun,

2024

Electronics

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Chinese calligraphy is a significant aspect of traditional culture, as it involves the art of writing Chinese characters. Despite the development of numerous deep learning models for generating calligraphy characters, the resulting outputs often suffer from issues related to stroke accuracy and stylistic consistency. To address these problems, an end-to-end generation model for Chinese calligraphy characters based on dense blocks and a capsule network is proposed. This model aims to solve issues such as redundant and broken strokes, twisted and deformed strokes, and dissimilarity with authentic ones. The generator of the model employs self-attention mechanisms and densely connected blocks to reduce redundant and broken strokes. The discriminator, on the other hand, consists of a capsule network and a fully connected network to reduce twisted and deformed strokes. Additionally, the loss function includes perceptual loss to enhance the similarity between the generated calligraphy characters and the authentic ones. To demonstrate the validity of the proposed model, we conducted comparison and ablation experiments on the datasets of Yan Zhenqing’s regular script, Deng Shiru’s clerical script, and Wang Xizhi’s running script. The experimental results show that, compared to the comparison model, the proposed model improves SSIM by 0.07 on average, reduces MSE by 1.95 on average, and improves PSNR by 0.92 on average, which proves the effectiveness of the proposed model.

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Internal Model Control Structure Inspired Robotic Calligraphy System

Cited by 3 publications

References 31 publications

Physically Motivated Model of a Painting Brush for Robotic Painting and Calligraphy

Physically Motivated Model of a Painting Brush for Robotic Painting and Calligraphy

RoDAL: style generation in robot calligraphy with deep adversarial learning

An End-to-End Generation Model for Chinese Calligraphy Characters Based on Dense Blocks and Capsule Network

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