Electronics, Vol. 15, Pages 474: Deep Learning Computer Vision-Based Automated Localization and Positioning of the ATHENA Parallel Surgical Robot

Electronics doi: 10.3390/electronics15020474

Authors:
Florin Covaciu
Bogdan Gherman
Nadim Al Hajjar
Ionut Zima
Calin Popa
Alexandru Pusca
Andra Ciocan
Calin Vaida
Anca-Elena Iordan
Paul Tucan
Damien Chablat
Doina Pisla

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.

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Florin Covaciu www.mdpi.com