Behavior-based control of brush drawing robot

Yang, Junyou; Guilin, Qiu; Ma, Le; Bai, Ding; Xu, Huibin

doi:10.1109/tmee.2011.6199408

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…A three DOF robotic arm made from LEGO NXT bricks was used for drawing on paper in [ 6 ], and was deemed suitable for educational projects on robotics and robot programming. Additionally, behavior-based control methods were used for brush drawing in [ 7 ], and a drawing robot system that can draw any picture automatically was proposed in [ 8 ]. Finally, the power of eye tracking as a powerful tool for assistive technologies was presented in [ 9 , 10 ], where a robot arm controlled by eye tracking was demonstrated.…”

Section: Related Workmentioning

confidence: 99%

Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds

Nasrat,

Kang,

Park

et al. 2023

Sensors

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In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the two objectives. Therefore, in this paper, we propose a new approach that combines both objectives by leveraging advanced machine learning techniques and a variable line width Chinese calligraphy pen. Our proposed system emulates the human drawing process, which entails planning the sketch and creating it on the canvas, thus providing a realistic and high-quality output. One of the main challenges in portrait drawing is preserving the facial features, such as the eyes, mouth, nose, and hair, which are crucial for capturing the essence of a person. To overcome this challenge, we employ CycleGAN, a powerful technique that retains important facial details while transferring the visualized sketch onto the canvas. Moreover, we introduce the Drawing Motion Generation and Robot Motion Control Modules to transfer the visualized sketch onto a physical canvas. These modules enable our system to create high-quality portraits within seconds, surpassing existing methods in terms of both time efficiency and detail quality. Our proposed system was evaluated through extensive real-life experiments and showcased at the RoboWorld 2022 exhibition. During the exhibition, our system drew portraits of more than 40 visitors, yielding a survey outcome with a satisfaction rate of 95%. This result indicates the effectiveness of our approach in creating high-quality portraits that are not only visually pleasing but also accurate.

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