Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.07945 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917198472151040 |
|---|---|
| author | Tamhankar, Aabha Alterovitz, Ron Puri, Ajit S. Pittiglio, Giovanni |
| author_facet | Tamhankar, Aabha Alterovitz, Ron Puri, Ajit S. Pittiglio, Giovanni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07945 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Contact-aware Path Planning for Autonomous Neuroendovascular Navigation Tamhankar, Aabha Alterovitz, Ron Puri, Ajit S. Pittiglio, Giovanni Robotics 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. |
| title | Contact-aware Path Planning for Autonomous Neuroendovascular Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.07945 |