A robot calligraphy system: From simple to complex writing by human gestures

Chao, Fei; Huang, Yuxuan; Zhang, Xin; Shang, Changjing; Yang, Longzhi; Zhou, Changle; Hu, Huosheng; Lin, Chunshui

doi:10.1016/j.engappai.2016.12.006

Cited by 44 publications

(22 citation statements)

References 41 publications

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“…The demonstrator performs the writing motions using their right arm, and the Kinect device captures the trajectories of the arm movement to inform the robot. The trajectory recording method was developed in our previous work, as documented in Ref [11]. The stroke trajectory information is presented by the 2-dimensional trajectories of the human demonstrator's right hand.…”

Section: A Robotic Systemmentioning

confidence: 99%

“…Robotic writing, a research field that appeals to many robotic scientists [1]- [4], focuses on how to design control algorithms to drive robotic end-effectors to write complex characters or letters [5], [6]. Handwriting, a typical human motion, is a highly demanding task that requires kinematics and dynamics, and complex characters, such as Chinese characters containing human-like handwriting, are very challenging for robots to draw [7]- [11]. Indeed, Chinese character writing is much more complicated than English writing because one Chinese character may consist of many strokes that must be placed in particular positions.…”

Section: Introductionmentioning

confidence: 99%

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Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Chao

Huang

Lin

et al. 2019

IEEE Trans. Human-Mach. Syst.

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Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters. This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot's manipulator to write the characters in a different style. The key technologies of the proposed approach include the following:(1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and (2) matching the decomposed strokes to robotic writing trajectories learned from human gestures. Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator. Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories. Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character. Seven common Chinese characters have been used in an experiment for system validation and evaluation. The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style.

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Section: A Robotic Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Chao

Huang

Lin

et al. 2019

IEEE Trans. Human-Mach. Syst.

Self Cite

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show abstract

“…Moreover, the noise due to the user movement affects the signal and thus lowers the signal to noise ratio which consequently decreases the accuracy level. A robot calligraphy system applies human arm gestures to establish a font database and then uses the database to write words and characters (Chao et al, 2017). This system requires a special sensor, complex mechanical design and lengthy training phases.…”

Section: Introductionmentioning

confidence: 99%

Development of an Android Recognition Open-Loop Tracking Control of Writing Robotic Arm

Fahim¹,

Dey²,

Rashiduzzaman³

et al. 2019

Journal of Mechatronics and Robotics

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Writing is one of the medium of the manifestations of the expression which is staged by going through many evolutionary phases for the time being. And most importantly robots and technology are dramatically meliorated in the 21st century. So, robotic writing hand was introduced with the collaboration with human brain and technology. The whole methodology is staged for the development of a low-cost robotic arm able to draw in an arbitrary 2-D plane through an android application. The Android app will take drawing inputs on its Android screen and the data bits are transferred to the robot through wireless standard IEEE 802.15.1 communication technology. The human friendly robotic arm uses the polar coordinate system instead of the Cartesian coordinate system for determining the end-effector position. The modified slider model with the rope-pulley mechanism makes the robot compact. The description of the mechanical and electrical design along with the practical and simulation results are presented in this paper.

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“…The authors in Jalal and Kamal (2014) use similar RGB-D sensor to obtain depth silhouettes of human activities from which body points information are extracted for the activity recognition system. Zhou et al (2018) also used an RGB-D sensor to capture human skeleton information as part of a system for controlling a mobile robot using human gestures which is also a similar application proposed by Chao et al (2017). Another approach is shown in the work in Gu et al (2012) where the RGB-D sensor is used to obtain orientation-based human representation of each joint to the human centroid in 3D space.…”

Section: Related Workmentioning

confidence: 99%

Human activity learning for assistive robotics using a classifier ensemble

et al. 2018

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Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers-support vector machines (SVMs), K -nearest neighbour and random forest-is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset-the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities.

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A robot calligraphy system: From simple to complex writing by human gestures

Cited by 44 publications

References 41 publications

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Development of an Android Recognition Open-Loop Tracking Control of Writing Robotic Arm

Human activity learning for assistive robotics using a classifier ensemble

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