Academic Journal
Bio-inspired fiber-optic-neural network enabled multi-physical sensing for tissue-safe robotic adhesion.
| Τίτλος: | Bio-inspired fiber-optic-neural network enabled multi-physical sensing for tissue-safe robotic adhesion. |
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| Συγγραφείς: | Zhang Y; Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, 100192, China; Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University, Beijing, 100016, China., Wang Z; Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, 100192, China; Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University, Beijing, 100016, China., Zhang H; Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, 100192, China; Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University, Beijing, 100016, China., Han J; Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China., Zhao G; Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China., Li X; Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, 100192, China; Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University, Beijing, 100016, China., Ren Y; Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, 100192, China; Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University, Beijing, 100016, China., Sun G; Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, 100192, China; Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University, Beijing, 100016, China. Electronic address: guangkai.sun@buaa.edu.cn., Zhu L; Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, 100192, China; Beijing Laboratory of Optical Fiber Sensing and System, Beijing Information Science & Technology University, Beijing, 100016, China. Electronic address: zhulianqing@bistu.edu.cn. |
| Πηγή: | Biosensors & bioelectronics [Biosens Bioelectron] 2026 Sep 15; Vol. 308, pp. 118750. Date of Electronic Publication: 2026 May 04. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Advanced Technology Country of Publication: England NLM ID: 9001289 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4235 (Electronic) Linking ISSN: 09565663 NLM ISO Abbreviation: Biosens Bioelectron Subsets: MEDLINE |
| Imprint Name(s): | Publication: Oxford : Elsevier Advanced Technology Original Publication: [Barking, Essex, England] : Elsevier Applied Science, 1989- |
| Ιατρικοί όροι (MeSH): | Robotic Surgical Procedures*/instrumentation , Biosensing Techniques*/instrumentation , Fiber Optic Technology*/instrumentation , Neural Networks, Computer*, Humans ; Equipment Design ; Animals ; Tissue Adhesions |
| Περίληψη: | Smart perceptive suction cups capable of non-destructively adhering to tissues are in high demand in robot-assisted surgery. However, current surgical suction cups often over-adhere to tissues due to a lack of perception ability, causing tissue damage and posing surgical risks. To address this issue, we propose a bionic suction cup incorporating a multi-physical sensing method for tissue damage monitoring. The system integrates fiber optic receptors with a physical model guided multitask cascade neural network, enabling real-time detection of multiple safety relevant mechanical parameters, such as mechanical compression, contact force, adhesion force, and vacuum level during suction adhesion. These measurements provide essential feedback for dynamic regulation of the adhesion state, thereby preventing tissue damage. We believe the proposed sensing method offers an effective perceptual framework for the development of next generation adhesion based surgical instruments and robotic systems, paving the way for more robust and non-destructive tissue manipulation. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Bioinspired sensing; Fiber optic receptor; Multitask cascade neural network; Non-destructive tissue adhesion; Robotic surgery assistance |
| Entry Date(s): | Date Created: 20260511 Date Completed: 20260716 Latest Revision: 20260716 |
| Update Code: | 20260717 |
| DOI: | 10.1016/j.bios.2026.118750 |
| PMID: | 42114325 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1873-4235 |
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| DOI: | 10.1016/j.bios.2026.118750 |