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Development of fundamental technologies to realize a society using drones

Research Project

Project/Area Number 17K19972
Research Category

Grant-in-Aid for Challenging Research (Exploratory)

Allocation TypeMulti-year Fund
Research Field Information science, computer engineering, and related fields
Research InstitutionInstitute of Physical and Chemical Research

Principal Investigator

Hamanaka Masatoshi  国立研究開発法人理化学研究所, 革新知能統合研究センター, チームリーダー (30451686)

Co-Investigator(Kenkyū-buntansha) 中野 不二男  京都大学, 宇宙総合学研究ユニット, 共同研究部門教員 (00595051)
Project Period (FY) 2017-06-30 – 2020-03-31
Project Status Completed (Fiscal Year 2019)
Budget Amount *help
¥5,980,000 (Direct Cost: ¥4,600,000、Indirect Cost: ¥1,380,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Keywordsドローン / GPS / 飛行網 / 深層学習 / 蟻コロニー最適化 / 粒子群最適化 / 着陸実験 / 超音波センサ / コンパスエラー / マルチタスク学習 / 超音波センサー / ドローンハイウェイ網 / 自己位置推定 / ディープラーニング / LiDAR / ティルトウィング型ドローン
Outline of Final Research Achievements

We developed three methods or systems for drone as follows. The position of a drone can be detected by using the global positioning system (GPS). However, GPS sometimes has difficulty capturing signals from satellites that are shielded by mountains and/or buildings. As a solution, we propose a flight area estimation method using a 3D map created on the basis of deep learning. Many flight path designing methods have been proposed; however, none of them addresses the issue of flight efficiency. We optimize each path using ant colony optimization and optimize the position of the terminal connecting the paths using particle swarm optimization. We propose to install an ultrasonic sensor on each arm of the drone and estimate the condition of the landing space from the time series of reflected waves for very short ultrasonic waves. In the measurement results shows that, reflected waves were small and changed irregularly for each sensor where the space is not suitable for landing.

Academic Significance and Societal Importance of the Research Achievements

ドローンを使用する上での問題は,安全性およびエネルギー効率をいかに高めるかである.GPSが使用できない場合における位置推定手法の構築および着陸地の状態把握システムは,ドローンの安全性を高め,経路最適化手法はドローンのエネルギー効率を高める.

Report

(4 results)
  • 2019 Annual Research Report   Final Research Report ( PDF )
  • 2018 Research-status Report
  • 2017 Research-status Report
  • Research Products

    (7 results)

All 2020 2019 2018

All Journal Article (5 results) (of which Peer Reviewed: 3 results) Presentation (2 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] ライダー・人工知能・陸域観測技術に基づくドローンハイウェイ構想2020

    • Author(s)
      浜中 雅俊
    • Journal Title

      ライダー技術特集, OplusE

      Volume: 42, 2

    • Related Report
      2019 Annual Research Report
  • [Journal Article] Surface-Condition Detection System of Drone-Landing Space using Ultrasonic Waves and Deep Learning2020

    • Author(s)
      Masatoshi Hamanaka
    • Journal Title

      Proceedings of 2020 International Conference on Unmanned Aircraft Systems (ICUAS2020)

      Volume: 1

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed
  • [Journal Article] AI活用によるGPSによらないドローンの自律飛行システムの開発2019

    • Author(s)
      浜中 雅俊
    • Journal Title

      人と共生するAI革命 ~活用事例からみる生活・産業・社会の未来展望~

      Volume: 2, 4

    • Related Report
      2019 Annual Research Report
  • [Journal Article] Optimum Design for Drone Highway Network2019

    • Author(s)
      Masatoshi Hamanaka
    • Journal Title

      Proceedings of 2019 International Conference on Unmanned Aircraft Systems (ICUAS2019)

      Volume: 1

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Deep Learning based Area Estimation for Unmanned Aircraft Systems using 3D Map2018

    • Author(s)
      Masatoshi Hamanaka
    • Journal Title

      Proceedings of International Conference on Unmanned Aircraft Systems (ICUAS2018)

      Volume: 2011 Pages: 416-423

    • DOI

      10.1109/icuas.2018.8453463

    • Related Report
      2018 Research-status Report
    • Peer Reviewed
  • [Presentation] Deep Learning based Area Estimation for Unmanned Aircraft Systems using 3D Map2018

    • Author(s)
      Masatoshi Hamanaka
    • Organizer
      International Conference on Unmanned Aircraft Systems (ICUAS2018)
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research
  • [Presentation] ディープラーニングに基づくドローン飛行エリアの推定 II2018

    • Author(s)
      浜中雅俊
    • Organizer
      人工知能学会全国大会第32回
    • Related Report
      2018 Research-status Report

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Published: 2017-07-21   Modified: 2021-02-19  

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