Enhancing the Autonomy of Soft Robots in Minimally Invasive Surgeries Through Computer Vision

Abstract

Researchers have recognized the potential of employing soft robots in minimally invasive surgeries (MIS), which could significantly reduce the side effects associated with traditional surgical methods and enhance patient outcomes. However, the extent to which this potential is realized depends on the level of autonomy achieved by the soft robots. Lower levels of autonomy necessitate increased hands-on involvement during MIS, potentially compromising the robots' ability to perform procedures with consistent accuracy. Consequently, achieving high levels of accuracy in the autonomy of soft robots remains a significant challenge. The autonomy of these robots is influenced by various factors, including their capacity to accurately classify their surroundings, particularly anatomical structures, which is crucial for effective decision-making. To address the challenge posed by our research question, How can the application of Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL) improve the autonomy of soft robots in performing complex tasks in MIS, particularly in the precise classification of anatomical structures and decision making?, we conducted an in-depth literature review and investigation into the integration of DNNs and DRL within this context. Our study used a modeling and simulation methodology to evaluate and quantify the benefits of incorporating these advanced AI techniques. Through this approach, we measured the effects of DNNs and DRL on enhancing the autonomy of soft robots, particularly in their ability to perform complex tasks in MIS with improved precision and decision-making capabilities. This work represents a step toward optimizing robotic autonomy in surgical environments, potentially leading to more efficient and accurate outcomes in minimally invasive procedures.