A robot calligraphy system: From simple to complex writing by human gestures. (March 2017)

• Record Type: Journal Article
• Authors: Chao, Fei; Huang, Yuxuan; Zhang, Xin; Shang, Changjing; Yang, Longzhi; Zhou, Changle; Hu, Huosheng; Lin, Chih-Min
• Is Part Of: Engineering applications of artificial intelligence. Volume 59(2016:Nov.)
• Page Start: 1
• Page End: 14
• Publication Date: 2017-03
• Subjects: 00-01 -- 99-00; Robotic writing -- Robotic calligraphy -- Human–robot interaction -- Human gesture recognition -- Classifier ensemble; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.12.006
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Robotic writing is a very challenging task and involves complicated kinematic control algorithms and image processing work. This paper, alternatively, proposes a robot calligraphy system that firstly applies human arm gestures to establish a font database of Chinese character elementary strokes and English letters, then uses the created database and human gestures to write Chinese characters and English words. A three-dimensional motion sensing input device is deployed to capture the human arm trajectories, which are used to build the font database and to train a classifier ensemble. 26 types of human gesture are used for writing English letters, and 5 types of gesture are used to generate 5 elementary strokes for writing Chinese characters. By using the font database, the robot calligraphy system acquires a basic writing ability to write simple strokes and letters. Then, the robot can develop to write complex Chinese characters and English words by following human body movements. The classifier ensemble, which is used to identify each gesture, is implemented through using feature selection techniques and the harmony search algorithm, thereby achieving better classification performance. The experimental evaluations are carried out to demonstrate the feasibility and performance of the proposed method. By following the motion trajectories of the human right arm, the end-effector of the robot can successfully write the English words or Chinese characters that correspond to the arm trajectories.