Faculty Profiles - MATSUNO Takayuki

Presentations -

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Progressive Approaches towards Automation of the Zerobot That Supports IR Procedures under CT Fluoroscopy Invited

Takayuki Matsuno

2024 IEEE International Conference on Cyborg and Bionic Systems 2024.11.21

医療用ロボットの設計開発 Invited

松野隆幸

ロボット×デザインで描く未来令和５年度岡山大学研究協力会×岡山県立大学協力会コラボイベント 2024.3.11

Research and development of the CT-guided needle insertion robot, Zerobot Invited

JSME Information, Intelligence and Precision Equipment Division 2024.3.4

IVR ロボットにおけるリアルタイム接触判定のための領域特定用シミュレータ

酒井菜々子, 松野隆幸, 城戸脩希, 門田成司, 亀川哲志, 平木隆夫, 戸田雄一郎, 見浪護

第30回日本コンピュータ外科学会大会 2021.11.22

Estimation of Needle Puncturing Form Based on Force Data during Slight Needle Movement

Takayuki Matsuno, Hikaru Murakami, Tetsushi Kamegawa, Takaaki Miyamoto, Nanako Sakai, Mamoru Minami, Takao Hiraki

2021 International Symposium on Medical Robotics 2021.11.18

Collision detection calculation of robot to insert needles for interventional radiology

Takayuki MatsunoKamegawa, TetsushiTakao Hiraki

2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) 2021.7.9

CT画像内の穿刺針のディープラーニングによる検出と姿勢決定アルゴリズム

眞弓虎太郎, 松野隆幸, 亀川哲志, 平木隆夫, 戸田雄一郎, 見浪護

ロボティクス・メカトロニクス講演会2021 2021.6.7

穿刺ロボットにおける針のたわみ推定シミュレーション

宮本隆晃, 松野隆幸, 村上輝, 亀川哲志, 平木隆夫, 戸田雄一郎, 見浪護

第65回システム制御情報学会研究発表講演会(SCI’21) 2021.5.28

Topological Structure Learning of Point Cloud data using Modified Region of Interest Growing Neural Gas

戸田雄一郎, 松野隆幸, 見浪護

ロボティクスシンポジア予稿集 2020

Region of Interest Growing Neural Gas for Real-Time Point Cloud Processing

Yuichiro Toda, Xiang Li, Takayuki Matsuno, Mamoru Minami

INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT III 2019 SPRINGER INTERNATIONAL PUBLISHING AG

Event date： 2019

Language：English

This paper proposes a real-time topological structure learning method based on concentrated/distributed sensing for a 2D/3D point cloud. First of all, we explain a modified Growing Neural Gas with Utility (GNG-U2) that can learn the topological structure of 3D space environment and color information simultaneously by using a weight vector. Next, we propose a Region Of Interest Growing Neural Gas (ROI-GNG) for realizing concentrated/distributed sensing in real-time. In ROI-GNG, the discount rates of the accumulated error and utility value are variable according to the situation. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.

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Improving pose estimation accuracy and expanding of visible space of lighting 3D marker in turbid water

Horng-Yi Hsu, Naoki Mukada, Daiki Yamada, Khin Nwe Lwin, Myo Myint, Yuichiro Toda, Takayuki Matsuno, Keigo Watanabe, Mamoru Minami

2019 IEEE UNDERWATER TECHNOLOGY (UT) 2019 IEEE

Event date： 2019

Language：English

Aiming at developing underwater battery recharging system, the author developed a docking system using stereo-vision-based visual servoing and a 3D marker. The 3D marker consists of red, green, blue spheres that do not emit the light which is named as a passive marker. Real-time relative pose (position and orientation) estimation was implemented utilizing the 3D model-based matching method and real-time multistep genetic algorithm (RM-GA). Given the situation that the docking aims for battery recharging in the deep-sea bottom, the pitch-dark and turbid environment should be considered as an inevitable condition for battery recharging. In our previous works, the docking experiments were conducted in the actual sea, having verified the effectiveness of the proposed system using the passive 3D marker in the daytime environment with turbid water condition. Since lighting passive 3D marker by light from the vehicle in turbid water environment results in a situation that the images taken by video cameras set on the vehicle were looked wholly white, some new idea seems to be required. To overcome this difficulty, the newly lighting 3D marker (active 3D maker) has LEDs inside was introduced in the previous work. The main objective of this study is to check the feasibility area of the proposed system for the docking application, comparison of recognition performance using the active and passive 3D marker that was conducted in the simulated pool with the turbid water is focused. And then, the experiment using the active 3D marker in the actual sea has been performed. The experimental results have confirmed that the new active 3D marker with no-lighting from the vehicle could be recognizable in dark and turbid environment than the passive 3D marker with the lighting from the vehicle.

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Region of Interest Growing Neural Gasに関する検討

戸田雄一郎, 松野隆幸, 見浪護

インテリジェント・システム・シンポジウム(CD-ROM) 2019

発光3Dマーカーによる混濁深海模擬環境下での認識性能評価

岡田優也, 山田大喜, LI Xiang, HSU Horng-Yi, 神田佳希, 山下耕平, 中村翔, 門田拓也, 戸田雄一郎, 松野隆幸, 見浪護

日本機械学会ロボティクス・メカトロニクス講演会講演論文集(CD-ROM) 2019

Visual-lifting Approachによるアイススケート歩行の実現と腕振り運動の発見-動力学的干渉による自発的腕振りの考察-

WANG Ying, 井澤大時, 張則奕, LI Xiang, 見浪護, 松野隆幸

日本機械学会ロボティクス・メカトロニクス講演会講演論文集(CD-ROM) 2019

水中ロボットのための完全自律式回転型充電システムの構築

門田拓也, 山下耕平, 神田佳希, 中村翔, 山田大喜, 岡田優也, 松野隆幸, 戸田雄一郎, 見浪護

日本機械学会ロボティクス・メカトロニクス講演会講演論文集(CD-ROM) 2019

ステレオビジョンによる新しい空間認識手法の提案

川上拓朗, 山本太郎, KOU Yejun, TIAN Hongzhi, LI Xiang, 戸田雄一郎, 松野隆幸, 見浪護

日本機械学会ロボティクス・メカトロニクス講演会講演論文集(CD-ROM) 2019

改良型Growing Neural Gas with Utilityを用いた時系列3次元点群からの特徴量抽出と領域分割

戸田雄一郎, 松野隆幸, 見浪護

計測自動制御学会システムインテグレーション部門講演会(CD-ROM) 2019

ロボティックIVRにおけるマニピュレータと周辺環境との接触判定アルゴリズム

城戸脩希, 松野隆幸, 亀川哲志, 平木隆夫, 見浪護

日本ロボット学会学術講演会予稿集(CD-ROM) 2019

CT透視ガイド下針穿刺ロボツトの臨床試験により得られた穿刺力データの解析

亀川哲志, 松野隆幸, 平木隆夫

日本ロボット学会学術講演会予稿集(CD-ROM) 2019

ステレオビジョンを用いた信号機の位置姿勢実時間認識

WANG Lujie, TIAN Hongzhi, KOU Yejun, WANG Junxiang, LI Xiang, 山本太郎, 戸田雄一郎, 松野隆幸, 見浪護

日本機械学会ロボティクス・メカトロニクス講演会講演論文集(CD-ROM) 2019

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