Robotic Assisted Surgery

Robotic-assisted surgery (RAS) aims to improve minimally invasive procedures through enhanced precision, dexterity, and safety. Current research heavily focuses on improving real-time scene understanding using advanced computer vision techniques like dense tracking algorithms and 3D reconstruction from visual and tactile data, often incorporating deep learning models such as Siamese networks and convolutional neural networks. These advancements, along with improved robotic control strategies like admittance control and imitation learning, are crucial for automating complex surgical tasks, reducing errors, and ultimately improving patient outcomes.