Cycloidal scanning with MEMS micro mirror for an omnidirectional long range LiDAR

Takeaki Kobayashi, Takashi Sasaki, Kazuhiro Hane

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

1 Citation (Scopus)

Abstract

An omnidirectional MEMS mirror for long range LiDAR is proposed on the basis of cycloidal scan assuming application for the self-driving vehicles. The proposed cycloidal scan is effective for adjusting scanning speed depending on distance. A 'Pantograph type' omnidirectional mirror is designed and fabricated using single crystalline silicon. The mirror has two electrostatically driven actuators. Voltages are applied for respective axes at two different phases and an optical angle of about 2.5 degrees is obtained for circular scans at a resonant frequency of 2281 Hz in the initial experiment.

Original languageEnglish
Title of host publicationOMN 2019 - 2019 International Conference on Optical MEMS and Nanophotonics, Proceedings
PublisherIEEE Computer Society
Pages142-143
Number of pages2
ISBN (Electronic)9781728145013
DOIs
Publication statusPublished - 2019 Jul
Event2019 International Conference on Optical MEMS and Nanophotonics, OMN 2019 - Daejeon, Korea, Republic of
Duration: 2019 Jul 282019 Aug 1

Publication series

NameInternational Conference on Optical MEMS and Nanophotonics
Volume2019-July
ISSN (Print)2160-5033
ISSN (Electronic)2160-5041

Conference

Conference2019 International Conference on Optical MEMS and Nanophotonics, OMN 2019
CountryKorea, Republic of
CityDaejeon
Period19/7/2819/8/1

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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  • Cite this

    Kobayashi, T., Sasaki, T., & Hane, K. (2019). Cycloidal scanning with MEMS micro mirror for an omnidirectional long range LiDAR. In OMN 2019 - 2019 International Conference on Optical MEMS and Nanophotonics, Proceedings (pp. 142-143). [8925249] (International Conference on Optical MEMS and Nanophotonics; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/OMN.2019.8925249