Contact-aware Path Planning for Autonomous Neuroendovascular Navigation

2026-01-12

IEEE Robotics and Automation Letters

AI Summary

This work proposes an efficient, deterministic, time-aware path planning method for neurovascular interventions to address inadequate modeling of device–vessel contact and low computational efficiency in existing approaches. By integrating preoperative and intraoperative multimodal vascular imaging and incorporating a kinematic model of pre-shaped passive devices, the method introduces simplified motion primitives within a sampling-based planning framework. It is the first to embed a contact-aware mechanism directly into the path generation process, enabling intelligent prediction and exploitation of interactions between the device and anatomical structures. Experimental results demonstrate 100% convergence in representative vascular geometries, with worst-case planning times ≤22.8 seconds, tracking errors <0.64 mm, and applicability to anatomical features found in approximately 94% of patients—achieving sub-millimeter accuracy while significantly improving computational efficiency.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• contact-aware
• path planning
• neuroendovascular navigation
• autonomous navigation
• pre-bent tools

Innovation

• contact-aware planning
• neuroendovascular navigation
• kinematic modeling
• sampling-based planner
• pre-bent passive tools