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Gao H, Liu H, Jia H, Lin Z, Zou Y, Xu Z, Huang S, Tan H, Wu H, Chen W, Gao A. Multi-axis robotic forceps with decoupled pneumatic actuation and force sensing for cochlear implantation. Nat Commun 2025; 16:1648. [PMID: 39952944 PMCID: PMC11828907 DOI: 10.1038/s41467-025-56958-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 02/03/2025] [Indexed: 02/17/2025] Open
Abstract
Delicate manual microsurgeries rely on sufficient hands-on experience for safe manipulations. Automated surgical devices can enhance the effectiveness, but developing high-resolution, multi-axis force-sensing devices for micro operations remains challenging. In this study, a 6-axis force-sensing pneumatic forceps with a serial-parallel robotic platform for cochlear implantation is developed. The forceps features a curved body shape embedded with parallel and inclined fiber Bragg grating sensors for 6-axis force sensing, and a pneumatic gripper with decoupled actuation is located at its end for actively grasping and releasing the electrode array. The robotic platform comprises a customized spherical parallel mechanism and a robotic arm, which can provide independent 3-DOF rotations and 3-DOF translations. The feasibility of the developed robotic forceps is validated through cadaveric studies on a temporal bone and a human cadaveric head. In summary, the robotic forceps provides a decoupled mechanism for pneumatic actuation and force sensing, further demonstrating its potential for force interaction and stable operation during robotic microsurgery.
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Affiliation(s)
- Hongyan Gao
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, PR China
- Department of Automation, Shanghai Jiao Tong University, and the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, PR China
| | - Huanghua Liu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, PR China
- Department of Automation, Shanghai Jiao Tong University, and the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, PR China
| | - Huan Jia
- Department of Otorhinolaryngology Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Zecai Lin
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, PR China
- Department of Automation, Shanghai Jiao Tong University, and the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, PR China
| | - Yun Zou
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, PR China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China
| | - Zheng Xu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, PR China
- Department of Automation, Shanghai Jiao Tong University, and the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, PR China
| | - Shaoping Huang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, PR China
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China
| | - Haoyue Tan
- Department of Otorhinolaryngology Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Hao Wu
- Department of Otorhinolaryngology Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
| | - Weidong Chen
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, PR China.
- Department of Automation, Shanghai Jiao Tong University, and the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, PR China.
| | - Anzhu Gao
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, PR China.
- Department of Automation, Shanghai Jiao Tong University, and the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, PR China.
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Qiu Y, Ashok A, Nguyen CC, Yamauchi Y, Do TN, Phan HP. Integrated Sensors for Soft Medical Robotics. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2308805. [PMID: 38185733 DOI: 10.1002/smll.202308805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/24/2023] [Indexed: 01/09/2024]
Abstract
Minimally invasive procedures assisted by soft robots for surgery, diagnostics, and drug delivery have unprecedented benefits over traditional solutions from both patient and surgeon perspectives. However, the translation of such technology into commercialization remains challenging. The lack of perception abilities is one of the obstructive factors paramount for a safe, accurate and efficient robot-assisted intervention. Integrating different types of miniature sensors onto robotic end-effectors is a promising trend to compensate for the perceptual deficiencies in soft robots. For example, haptic feedback with force sensors helps surgeons to control the interaction force at the tool-tissue interface, impedance sensing of tissue electrical properties can be used for tumor detection. The last decade has witnessed significant progress in the development of multimodal sensors built on the advancement in engineering, material science and scalable micromachining technologies. This review article provides a snapshot on common types of integrated sensors for soft medical robots. It covers various sensing mechanisms, examples for practical and clinical applications, standard manufacturing processes, as well as insights on emerging engineering routes for the fabrication of novel and high-performing sensing devices.
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Affiliation(s)
- Yulin Qiu
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, 2052, Australia
- Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - Aditya Ashok
- Australian Institute of Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, Queensland, 4067, Australia
| | - Chi Cong Nguyen
- Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - Yusuke Yamauchi
- Australian Institute of Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, Queensland, 4067, Australia
- Department of Materials Science and Engineering, School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Thanh Nho Do
- Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, New South Wales, 2052, Australia
- Tyree Foundation Institute of Health Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - Hoang-Phuong Phan
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, 2052, Australia
- Tyree Foundation Institute of Health Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia
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Zhou C, Lin Z, Huang S, Li B, Gao A. Progress in Probe-Based Sensing Techniques for In Vivo Diagnosis. BIOSENSORS 2022; 12:943. [PMID: 36354452 PMCID: PMC9688418 DOI: 10.3390/bios12110943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Advancements in robotic surgery help to improve the endoluminal diagnosis and treatment with minimally invasive or non-invasive intervention in a precise and safe manner. Miniaturized probe-based sensors can be used to obtain information about endoluminal anatomy, and they can be integrated with medical robots to augment the convenience of robotic operations. The tremendous benefit of having this physiological information during the intervention has led to the development of a variety of in vivo sensing technologies over the past decades. In this paper, we review the probe-based sensing techniques for the in vivo physical and biochemical sensing in China in recent years, especially on in vivo force sensing, temperature sensing, optical coherence tomography/photoacoustic/ultrasound imaging, chemical sensing, and biomarker sensing.
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Affiliation(s)
- Cheng Zhou
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zecai Lin
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shaoping Huang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Bing Li
- Institute for Materials Discovery, University College London, London WC1E 7JE, UK
| | - Anzhu Gao
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
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Ha XT, Wu D, Lai CF, Ourak M, Borghesan G, Menciassi A, Poorten EV. Contact Localization of Continuum and Flexible Robot Using Data-Driven Approach. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3176723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Xuan Thao Ha
- Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Di Wu
- Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | | | - Mouloud Ourak
- Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Gianni Borghesan
- Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Arianna Menciassi
- The BioRobotics Institute, Scuola Superiore Sant–Anna, Pontedera, Italy
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