Towards robotic calligraphy

Huebel, Nico; Mueggler, Elias; Waibel, Markus; D’Andrea, Raffaello

doi:10.1109/iros.2012.6386275

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…In a number of studies, robots can only write Chinese character's strokes rather than write an entire Chinese character; these studies disassembled each Chinese character into di®erent strokes, and assigned the stroke positions to the robots to¯nish the writing. 5,15,[20][21][22][23] These approaches can create high quality robotic writing; however, they are inconvenient in making the robots write new characters. Additionally, only a few works have used data gloves and brain-machine interface devices to collect writing gesture information, and then convert this information to robotic joint values.…”

Section: Robotic Writing and Related Workmentioning

confidence: 99%

“…5 Handwriting, being a typical human motion, is a highly demanding task involving both kinematics and dynamics. 6 Moreover, robotic writing needs to include a redundant number of degrees of freedom (DOFs).…”

Section: Introductionmentioning

confidence: 99%

“…The mainstream way is multi-joint robots, which are more suitable for Chinese character writing, e.g., Ref. 5. Therefore, in this paper, we use human-robot interaction via a multi-joint robotic arm.…”

Section: Introductionmentioning

confidence: 99%

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Robotic Free Writing of Chinese Characters via Human–Robot Interactions

Chao

Chen

Shen

et al. 2014

Int. J. Human. Robot.

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Implementation of robotic writing ability is recognized as a di±cult task, which involves complicated image processing and robotic control algorithms. This paper introduces a novel approach to robotic writing by using human-robot interactions. The method applies a motion sensing input device to capture a human demonstrator's arm trajectories, uses a gesture determination algorithm to extract a Chinese character's strokes from these trajectories, and employs noise¯ltering and curve¯tting methods to optimize the strokes. The approach displays real-time captured trajectories to the human demonstrator; therefore, the human demonstrator is able to adjust his/her gesture to achieve a better character writing e®ect. Then, our robot writes the human-gestured character by using the robotic arm's joint values. The inverse kinematics algorithm generates the joint values from the stroke trajectories. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters. Additionally, this approach allows the robot to achieve a satisfactory writing quality for characters with a simple structure, with the potential to write more complex characters.

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Section: Robotic Writing and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robotic Free Writing of Chinese Characters via Human–Robot Interactions

Chao

Chen

Shen

et al. 2014

Int. J. Human. Robot.

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“…After the parabola fitting process, each human gesture trajectory D training ( − → P ) has been converted to an array of points − → s (x k , y k ), k ∈ [1,9]. The array is sent to the Robotic Arm Control module, the inverse kinematic algorithm is applied to transform the array to the robotic arm's joint values.…”

Section: Stroke Generationmentioning

confidence: 99%

“…The robotic writing ability is an interesting research field, which focuses on how to automatically control robotic actuators to write complex characters from single strokes [1]. It is very difficult for robots to draw Chinese characters containing human-like handwriting features.…”

Section: Introductionmentioning

confidence: 99%

A reduced classifier ensemble approach to human gesture classification for robotic Chinese handwriting

Chao

Sun

Wang

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character's strokes. This approach derives the font information from human gestures by using a motion sensing input device. Five elementary strokes are used to form Chinese characters, and each elementary stroke is assigned to a type of human gestures. Then, a classifier ensemble is applied to identify each gesture so as to recognize the characters that gestured by the human demonstrator. The classier ensemble's size is reduced by feature selection techniques and harmony search algorithm, thereby achieving higher accuracy and smaller ensemble size. The inverse kinematics algorithm converts each stroke's trajectory to the robot's motor values that are executed by a robotic arm to draw the entire character. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters.

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