Trocar Docking

Trocar docking, the automated insertion of an instrument into a trocar (a cannula used in minimally invasive surgery), is a key challenge in robotic surgery aiming to improve precision and reduce surgeon workload. Current research focuses on developing autonomous and semi-autonomous docking systems using computer vision for trocar localization and pose estimation, often incorporating force feedback and hand-eye coordination techniques to ensure safe and accurate insertion. These advancements leverage optimization algorithms and machine learning models, such as those employing self-supervised learning for improved hand-eye information fusion, to enhance the precision and robustness of robotic surgical procedures. The resulting improvements in accuracy and reduced invasiveness have significant implications for various surgical specialties, including laparoscopic and retinal surgery.