Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds
Author:
Nasrat Shady1ORCID, Kang Taewoong1ORCID, Park Jinwoo1ORCID, Kim Joonyoung1ORCID, Yi Seung-Joon1ORCID
Affiliation:
1. Electrical Engineering Department, Pusan National University, Busan 46241, Republic of Korea
Abstract
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.
Funder
Korean Government BK21FOUR Creative Human Resource Education ICT Convergence Korea Institute for Advancement of Technology
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference36 articles.
1. Almeida, D., and Karayiannidis, Y. (2019, January 11–13). A Lyapunov-Based Approach to Exploit Asymmetries in Robotic Dual-Arm Task Resolution. Proceedings of the 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France. 2. Chancharoen, R., Chaiprabha, K., Wuttisittikulkij, L., Asdornwised, W., Saadi, M., and Phanomchoeng, G. (2023). Digital Twin for a Collaborative Painting Robot. Sensors, 23. 3. TWOMEY, R. (2023, May 10). Three Stage Drawing Transfer. Available online: https://roberttwomey.com/three-stage-drawing-transfer/. 4. Putra, R.Y., Kautsar, S., Adhitya, R., Syai’in, M., Rinanto, N., Munadhif, I., Sarena, S., Endrasmono, J., and Soeprijanto, A. (2016, January 29–30). Neural Network Implementation for Invers Kinematic Model of Arm Drawing Robot. Proceedings of the 2016 International Symposium on Electronics and Smart Devices (ISESD), Bandung, Indonesia. 5. Baccaglini-Frank, A.E., Santi, G., Del Zozzo, A., and Frank, E. (2020). Teachers’ Perspectives on the Intertwining of Tangible and Digital Modes of Activity with a Drawing Robot for Geometry. Educ. Sci., 10.
|
|