Autonomous task planning and situation awareness in robotic surgery
This work addresses the challenge of autonomous task planning in robotic surgery, which could reduce reliance on human surgeons, though it appears incremental as it builds on existing methods for specific tasks.
The paper tackles the problem of automating complex surgical tasks that require reasoning and multiple actions, proposing a framework that integrates logic-based planning, motion planning, and situation awareness, validated on a standard surgical training task.
The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigated by researchers, while the execution of structured tasks requiring reasoning on the environment and the choice among multiple actions is still managed by human surgeons. In this paper, we propose a framework to implement surgical task automation. The framework consists of a task-level reasoning module based on answer set programming, a low-level motion planning module based on dynamic movement primitives, and a situation awareness module. The logic-based reasoning module generates explainable plans and is able to recover from failure conditions, which are identified and explained by the situation awareness module interfacing to a human supervisor, for enhanced safety. Dynamic Movement Primitives allow to replicate the dexterity of surgeons and to adapt to obstacles and changes in the environment. The framework is validated on different versions of the standard surgical training peg-and-ring task.