Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery
This study introduces a Neuro-Visual Adaptive Control (NVAC) architecture designed to enhance precision and safety in robot-assisted surgery. The proposed system enables semi-autonomous guidance of the laparoscope based on image input. To achieve this, the architecture integrates the following: (1)...
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MDPI AG
2025-04-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/4/135 |
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| author | Claudio Urrea Yainet Garcia-Garcia John Kern Reinier Rodriguez-Guillen |
| author_facet | Claudio Urrea Yainet Garcia-Garcia John Kern Reinier Rodriguez-Guillen |
| author_sort | Claudio Urrea |
| collection | DOAJ |
| description | This study introduces a Neuro-Visual Adaptive Control (NVAC) architecture designed to enhance precision and safety in robot-assisted surgery. The proposed system enables semi-autonomous guidance of the laparoscope based on image input. To achieve this, the architecture integrates the following: (1) a computer vision system based on the YOLO11n model, which detects surgical instruments in real time; (2) a Model Reference Adaptive Control with Proportional–Derivative terms (MRAC-PD), which adjusts the robot’s behavior in response to environmental changes; and (3) Closed-Form Continuous-Time Neural Networks (CfC-mmRNNs), which efficiently model the system’s dynamics. These networks address common deep learning challenges, such as the vanishing gradient problem, and facilitate the generation of smooth control signals that minimize wear on the robot’s actuators. Performance evaluations were conducted in CoppeliaSim, utilizing real cholecystectomy images featuring surgical tools. Experimental results demonstrate that the NVAC achieves maximum tracking errors of 1.80 × 10<sup>−</sup><sup>3</sup> m, 1.08 × 10<sup>−</sup><sup>4</sup> m, and 1.90 × 10<sup>−</sup><sup>3</sup> m along the x, y, and z axes, respectively, under highly significant dynamic disturbances. This hybrid approach provides a scalable framework for advancing autonomy in robotic surgery. |
| format | Article |
| id | doaj-art-bb49cf3e7ddd4707893a2ccde48bb081 |
| institution | OA Journals |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Technologies |
| spelling | doaj-art-bb49cf3e7ddd4707893a2ccde48bb0812025-08-20T02:18:15ZengMDPI AGTechnologies2227-70802025-04-0113413510.3390/technologies13040135Neuro-Visual Adaptive Control for Precision in Robot-Assisted SurgeryClaudio Urrea0Yainet Garcia-Garcia1John Kern2Reinier Rodriguez-Guillen3Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170020, ChileElectrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170020, ChileElectrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170020, ChileElectrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170020, ChileThis study introduces a Neuro-Visual Adaptive Control (NVAC) architecture designed to enhance precision and safety in robot-assisted surgery. The proposed system enables semi-autonomous guidance of the laparoscope based on image input. To achieve this, the architecture integrates the following: (1) a computer vision system based on the YOLO11n model, which detects surgical instruments in real time; (2) a Model Reference Adaptive Control with Proportional–Derivative terms (MRAC-PD), which adjusts the robot’s behavior in response to environmental changes; and (3) Closed-Form Continuous-Time Neural Networks (CfC-mmRNNs), which efficiently model the system’s dynamics. These networks address common deep learning challenges, such as the vanishing gradient problem, and facilitate the generation of smooth control signals that minimize wear on the robot’s actuators. Performance evaluations were conducted in CoppeliaSim, utilizing real cholecystectomy images featuring surgical tools. Experimental results demonstrate that the NVAC achieves maximum tracking errors of 1.80 × 10<sup>−</sup><sup>3</sup> m, 1.08 × 10<sup>−</sup><sup>4</sup> m, and 1.90 × 10<sup>−</sup><sup>3</sup> m along the x, y, and z axes, respectively, under highly significant dynamic disturbances. This hybrid approach provides a scalable framework for advancing autonomy in robotic surgery.https://www.mdpi.com/2227-7080/13/4/135autonomous robotic surgical assistantmodel reference-based neuro-visual adaptive controlsurgical instrument detectionCfC-mmRNNdata-driven inverse modelingvisual tracking |
| spellingShingle | Claudio Urrea Yainet Garcia-Garcia John Kern Reinier Rodriguez-Guillen Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery Technologies autonomous robotic surgical assistant model reference-based neuro-visual adaptive control surgical instrument detection CfC-mmRNN data-driven inverse modeling visual tracking |
| title | Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery |
| title_full | Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery |
| title_fullStr | Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery |
| title_full_unstemmed | Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery |
| title_short | Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery |
| title_sort | neuro visual adaptive control for precision in robot assisted surgery |
| topic | autonomous robotic surgical assistant model reference-based neuro-visual adaptive control surgical instrument detection CfC-mmRNN data-driven inverse modeling visual tracking |
| url | https://www.mdpi.com/2227-7080/13/4/135 |
| work_keys_str_mv | AT claudiourrea neurovisualadaptivecontrolforprecisioninrobotassistedsurgery AT yainetgarciagarcia neurovisualadaptivecontrolforprecisioninrobotassistedsurgery AT johnkern neurovisualadaptivecontrolforprecisioninrobotassistedsurgery AT reinierrodriguezguillen neurovisualadaptivecontrolforprecisioninrobotassistedsurgery |