| 研究課題/領域番号 |
19K14950
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分20020:ロボティクスおよび知能機械システム関連
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| 研究機関 | 立命館大学 |
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研究代表者 |
Zhu Mingzhu 立命館大学, 立命館グローバル・イノベーション研究機構, 研究員 (50806180)
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| 研究期間 (年度) |
2019-04-01 – 2020-03-31
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| 研究課題ステータス |
中途終了 (2019年度)
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| 配分額 *注記 |
3,380千円 (直接経費: 2,600千円、間接経費: 780千円)
2021年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2020年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2019年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
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| キーワード | Soft robotics / 3D Printing / Triboelectric / Self-powered / Soft gripper / Robust grasping / Data-driven |
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| 研究開始時の研究の概要 |
Traditional soft robotic hands usually have low stiffness due to soft materials and compliant structures, and arbitrary grasping posture without proper control method, posing challenges to ensure the grasping robustness during high-speed movement. Data-driven robust high-speed grasping of a variable stiffness soft-hand-eye system is proposed to address these issues. A novel grasping data set, including wrist orientation, grasping direction and stiffness variation will be developed for controlling the variable stiffness soft-hand-eye system based on deep leaning to ensure grasping robustness.
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| 研究実績の概要 |
Significant advances in the area of additive manufacturing have provided a novel fabrication method to develop soft robots with its self-powered sensors. In this paper, multi-material 3D printing is used to directly print out a soft robotic finger with its triboelectric bending sensor. The reinforced soft finger with single-electrode triboelectric curvature sensor (RSF-S-TECS) combines the advantages of multi-material 3D printing and stretchable electrodes to achieve fast prototyping and easy system integration. The triboelectric curvature sensor is located on the top of the finger. One of the active layers of the S-TECS is directly printed on the top surface of the reinforced finger body by multi-material 3D printing, and another active layer is made of PDMS attached to a stretchable electrode. The S-TECS behavior is evaluated for different active surface profiles, applied forces and operational frequency using an automated setup. The integrated S-TECS can measure a finger curvature up to 8.2 m-1 under an extremely low working frequency of 0.06 Hz, proving the effectiveness of the proposed S-TECS as a self-powered curvature sensor. This work presents a novel design and fabrication method of S-TECS for its potential applications in multi-material 3D printed soft robotics.
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