| Project/Area Number |
20H02116
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 20020:Robotics and intelligent system-related
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| Research Institution | Osaka University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
堀 一浩 新潟大学, 医歯学系, 教授 (70379080)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥16,900,000 (Direct Cost: ¥13,000,000、Indirect Cost: ¥3,900,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2021: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2020: ¥9,230,000 (Direct Cost: ¥7,100,000、Indirect Cost: ¥2,130,000)
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| Keywords | 咀嚼ロボット / 柔軟物マニピュレーション / ソフトロボティクス / 食塊形成 / テクスチャーセンシング |
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| Outline of Research at the Start |
本研究は，ヒトの食塊形成およびテクスチャー（食感）評価といった咀嚼機能の工学的理解と再現に向け，咀嚼ロボットシミュレーション手法を確立する．咀嚼過程におけるヒトの歯および舌の運動特性を実測，解析し，ロボットの機構・動作設計のための歯・舌運動モデルを作成する．歯舌両有型ロボットシミュレータを設計開発し，人工咀嚼，すなわち歯・舌－食品といった柔剛複合系の相互作用を介した食塊形成マニピュレーションを実現する．深層学習を用いて食塊形成過程の時空間力覚情報に基づくテクスチャー推定モデルを構築する．
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| Outline of Final Research Achievements |
Humans combine complex motions of the teeth and tongue during mastication to crush food, mix it with saliva, and form a swallowable food bolus. The fields of food science and industry desire to reproduce such a food bolus formation by robots to evaluate food quantitatively. This study proposed a novel robotic mastication simulator dedicated to the faithful reproduction of food bolus formation. First, three primitives involved in food bolus formation were defined: crushing, mixing, and gathering. Subsequently, the structure and motions of the robot were designed to execute these primitives, and the robotic mastication simulator was developed. Sequentially performing the primitives, the robot attempted to reproduce the human food bolus formation. Finally, human and the proposed robot masticated test foods, and the resulting food bolus images were analyzed using a convolutional neural network. It was shown that the proposed robot has a potential to reproduce human food bolus formation.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、ヒトの咀嚼の工学的理解と再現に向けて、食塊形成マニピュレーション技能を具現化するためのロボットモデル（機構・制御系、計測系、情報処理系）構築といった学術的意義を有する。また、本研究で提案した咀嚼ロボットは、超高齢社会において、ヒトの摂食メカニズムの解明、咀嚼困難者の病態評価、および、介護用食品の開発支援に貢献できる可能性を有している。
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