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Vitreoretinal surgery is challenging even for expert surgeons owing to the delicate target tissues and the diminutive 7-mm-diameter workspace in the retina. In addition to improved dexterity and accuracy, robot assistance allows for (partial) task automation. In this work, we propose a strategy to automate the motion of the light guide with respect to the surgical instrument. This automation allows the instruments shadow to always be inside the microscopic view, which is an important cue for the accurate positioning of the instrument in the retina. We show simulations and experiments demonstrating that the proposed strategy is effective in a 700-point grid in the retina of a surgical phantom.
During retinal microsurgery, precise manipulation of the delicate retinal tissue is required for positive surgical outcome. However, accurate manipulation and navigation of surgical tools remain difficult due to a constrained workspace and the top-do
We propose a new method for six-degree-of-freedom (6-DoF) autonomous camera movement for minimally invasive surgery, which, unlike previous methods, takes into account both the position and orientation information from structures in the surgical scen
Autonomous robotic surgery has the potential to provide efficacy, safety, and consistency independent of individual surgeons skill and experience. Autonomous soft-tissue surgery in unstructured and deformable environments is especially challenging as
Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This task automation could lead to reduced surgeons cognitive workload, increa
Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dyn