Contact-aware Path Planning for Autonomous Neuroendovascular Navigation

TL;DR

This work addresses autonomous navigation of pre-bent passive neuroendovascular tools through complex vessels by exploiting contact with the vessel walls. It introduces a deterministic, time-efficient planner that uses pre-operative and intra-operative images, three motion primitives ($wall\-guided\;glide$, $free\-space\;rebound$, $angled\-catheter\;launch$) and surface-constrained RRT planning on a vascular mesh, followed by inverse kinematics to execute on a $4$-DOF robotic platform. The approach achieves 100% planning success within $22.8\,\mathrm{s}$ worst-case across three aortic-arch variants, with per-iteration cost of $0.439\,\mathrm{ms}$, and sub-millimeter tracking errors ($<0.64\,\mathrm{mm}$) in experiments. The results suggest substantial potential for faster, autonomy-assisted neuroendovascular interventions, including rural access, while highlighting the need for closed-loop control to compensate for friction and dynamics in real clinical settings.

Abstract

We propose a deterministic and time-efficient contact-aware path planner for neurovascular navigation. The algorithm leverages information from pre- and intra-operative images of the vessels to navigate pre-bent passive tools, by intelligently predicting and exploiting interactions with the anatomy. A kinematic model is derived and employed by the sampling-based planner for tree expansion that utilizes simplified motion primitives. This approach enables fast computation of the feasible path, with negligible loss in accuracy, as demonstrated in diverse and representative anatomies of the vessels. In these anatomical demonstrators, the algorithm shows a 100% convergence rate within 22.8s in the worst case, with sub-millimeter tracking errors (less than 0.64 mm), and is found effective on anatomical phantoms representative of around 94% of patients.

Figures (7)

• Figure 1: Description of autonomous neuroendovascular robotic solution. a) vessel anatomy; b) actuation platform. Reproduced from Tamhankar2025TowardsPlanning © 2025 IEEE.
• Figure 2: Schematic of the three motion primitives in our kinematic planner: (a) wall-guided glide, with the guidewire (dark gray) sliding along the vessel wall; (b) free-space rebound, where the guidewire traverses a bifurcation and re-contacts the opposite wall; (c) angled-catheter launch, where the guidewire exits the catheter (purple) at a fixed bend angle.
• Figure 3: 3D printed models of three aortic arch variants: a) Type I (classic), b) Type II (bovine), and c) Type III (direct vertebral origin).
• Figure 4: Success rate versus number of iterations for three aortic arch variants: (a) classic, (b) bovine, and (c) direct vertebral origin.
• Figure 5: Geometric parametrization of failure conditions for the angled-catheter launch.