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Deep Learning Computer Vision-Based Automated Localization and Positioning of the ATHENA Parallel Surgical Robot.

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Τίτλος: Deep Learning Computer Vision-Based Automated Localization and Positioning of the ATHENA Parallel Surgical Robot.
Συγγραφείς: Covaciu, Florin, Gherman, Bogdan, Al Hajjar, Nadim, Zima, Ionut, Popa, Calin, Pusca, Alexandru, Ciocan, Andra, Vaida, Calin, Iordan, Anca-Elena, Tucan, Paul, Chablat, Damien, Pisla, Doina
Πηγή: Electronics (2079-9292); Jan2026, Vol. 15 Issue 2, p474, 28p
Θεματικοί όροι: Computer vision, Surgical robots, Deep learning, Minimally invasive procedures, Object recognition (Computer vision), Spatial orientation, Localization problems (Robotics), Artificial intelligence
Περίληψη: Manual alignment between the trocar, surgical instrument, and robot during minimally invasive surgery (MIS) can be time-consuming and error-prone, and many existing systems do not provide autonomous localization and pose estimation. This paper presents an artificial intelligence (AI)-assisted, vision-guided framework for automated localization and positioning of the ATHENA parallel surgical robot. The proposed approach combines an Intel RealSense RGB–depth (RGB-D) camera with a You Only Look Once version 11 (YOLO11) object detection model to estimate the 3D spatial coordinates of key surgical components in real time. The estimated coordinates are streamed over Transmission Control Protocol/Internet Protocol (TCP/IP) to a programmable logic controller (PLC) using Modbus/TCP, enabling closed-loop robot positioning for automated docking. Experimental validation in a controlled setup designed to replicate key intraoperative constraints demonstrated submillimeter positioning accuracy (≤0.8 mm), an average end-to-end latency of 67 ms, and a 42% reduction in setup time compared with manual alignment, while remaining robust under variable lighting. These results indicate that the proposed perception-to-control pipeline is a practical step toward reliable autonomous robotic docking in MIS workflows. [ABSTRACT FROM AUTHOR]
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Βάση Δεδομένων: Complementary Index
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  Data: Deep Learning Computer Vision-Based Automated Localization and Positioning of the ATHENA Parallel Surgical Robot.
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  Data: Manual alignment between the trocar, surgical instrument, and robot during minimally invasive surgery (MIS) can be time-consuming and error-prone, and many existing systems do not provide autonomous localization and pose estimation. This paper presents an artificial intelligence (AI)-assisted, vision-guided framework for automated localization and positioning of the ATHENA parallel surgical robot. The proposed approach combines an Intel RealSense RGB–depth (RGB-D) camera with a You Only Look Once version 11 (YOLO11) object detection model to estimate the 3D spatial coordinates of key surgical components in real time. The estimated coordinates are streamed over Transmission Control Protocol/Internet Protocol (TCP/IP) to a programmable logic controller (PLC) using Modbus/TCP, enabling closed-loop robot positioning for automated docking. Experimental validation in a controlled setup designed to replicate key intraoperative constraints demonstrated submillimeter positioning accuracy (≤0.8 mm), an average end-to-end latency of 67 ms, and a 42% reduction in setup time compared with manual alignment, while remaining robust under variable lighting. These results indicate that the proposed perception-to-control pipeline is a practical step toward reliable autonomous robotic docking in MIS workflows. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/electronics15020474
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        Text: English
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      – SubjectFull: Deep learning
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              Text: Jan2026
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