Automatic page-turning mechanism with near-field electroadhesive force for linearly correctable imaging

Junseok Lee, Wonseok Jeon, Youngsu Cha, Hyunseok Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Recently in tandem with the spread of portable devices for reading electronic books, devices for digitizing paper books, called book scanners, are developed to meet the increased demand for digitizing privately owned books. However, conventional book scanners still have complex components to mechanically turn pages and to rectify the acquired images that are inevitably distorted by the curvy book surface. Here, we present the multi-scale mechanism that turns pages electronically using electroadhesive force generated by a micro-scale structure. Its another advantage is that perspective correction of image processing is applicable to readily reconstruct the distorted images of pages. Specifically, to turn one page at a time not two pages, we employ a micro-scale structure to generate near-field electroadhesive force that decays rapidly and accordingly attracts objects within tens of micrometers. We analyze geometrical parameters of the micro-scale structure to improve the decay characteristics. We find that the decay characteristics of electroadhesive force definitely depends upon the geometrical period of the micro-scale structure, while its magnitude depends on a variety of parameters. Based on this observation, we propose a novel electrode configuration with improved decay characteristics. Dynamical stability and kinematic requirements are also examined to successfully introduce near-field electroadhesive force into our digitizing process.

Original languageEnglish
Title of host publicationIROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages279-285
Number of pages7
ISBN (Electronic)9781538626825
DOIs
Publication statusPublished - 2017 Dec 13
Event2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada
Duration: 2017 Sep 242017 Sep 28

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2017-September
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
CountryCanada
CityVancouver
Period17/9/2417/9/28

Bibliographical note

Funding Information:
★The authors would like to thank Professor Dae-Eun Kim and the team Cre. 5 for helpful discussion in developing the initial prototype of the proposed mechanism. This work was supported by the Global Frontier R&D Program on “Human-centered Interaction for Coexistence” funded by the National Research Foundation of Korea grant funded by the Korean Government (MSIP) (2011-0031425) and the Technology Innovation Program (10073129, Development of driving and manipulation intelligence based on deep learning and inverse reinforcement learning for dual arm mobile robot) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) 1School of Mechanical Engineering, Yonsei University, Seoul, Korea 2Center for Robotics Research, Korea Institute of Science and Technology, Seoul, Korea ∗Email: hsyang@yonsei.ac.kr Fig. 1. The overview of our proposed mechanism that turns pages electronically using near-field electroadhesive force. We employ a microscale structure (inset; scale bar: 500 µm) designed to generate near-field electroadhesive force that decays within tens of micrometers to attract objects within the limited range. The rapid decay allows lifting a single top page not more than two pages. The lifted page is flattened during the process, and the acquired image of the flattened page is rectified with the algorithm for perspective correction (inset).

Funding Information:
This work was supported by the Global Frontier RandD Program on Human-centered Interaction for Coexistence funded by the National Research Foundation of Korea grant funded by the Korean Government (MSIP) (2011-0031425) and the Technology Innovation Program (10073129, Development of driving and manipulation intelligence based on deep learning and inverse reinforcement learning for dual arm mobile robot) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea)

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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