Collision-free 6D non-holonomic planning for Nested Cannulas

摘要

Natural orifice access is the next frontier in minimally invasive technology. This requires dexterity for reaching through complex translumenal paths to a target. We propose a fast algorithm to define shapes of tiny, Nested Cannula devices based on patient CT images to deliver diagnostic and therapeutic procedures, and apply it to deep lung access. Each pre-shaped tube is extended sequentially in either a curved or a straight direction, requiring the solution to a 6D non-holonomic problem with obstacle avoidance in order to reach through free anatomy.A 3D image of the lung provides the specification of free and forbidden regions as well as the core structure for a configuration space. By using an A~* search, each state holds the detailed specification leading to the 'goal'. These specifications include the shape, 3D orientation, and 3D position, which can be stored in an adjacent structure in high precision. This allows the normally massive 6D configuration space to be stored in an augmented 3D structure, reducing massive memory requirements by about two orders of magnitude. The adapted configuration space and A~* algorithm requires under a minute on a desktop PC to compute a set of shaped tubes that can reach far inside a segmented lung.This paper describes three advances. The first defines new ways to structure searched configuration spaces so that it no longer requires intractable memory. The second solves the non-holonomic 6D problem of defining shaped tubes that extend sequentially into the body while avoiding obstacles. The third incorporates the physics of the interacting tubes.