# Autonomous Navigation for Robot-assisted Intraluminal and Endovascular Procedures: A Systematic Review

arXiv: https://arxiv.org/abs/2305.04027 · HTML: https://mlsanity.com/paper/2305.04027/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2023.

## Built on by (most cited first, 8 of 8)

- [Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments](https://mlsanity.com/paper/2402.00537/) (2024)
- [BronchoCopilot: Towards Autonomous Robotic Bronchoscopy via Multimodal Reinforcement Learning](https://mlsanity.com/paper/2403.01483/) (2024)
- [SLAM assisted 3D tracking system for laparoscopic surgery](https://mlsanity.com/paper/2409.11688/) (2024)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning](https://mlsanity.com/paper/2409.15688/) (2024)
- [DynaWeightPnP: Toward global real-time 3D-2D solver in PnP without correspondences](https://mlsanity.com/paper/2409.18457/) (2024)
- [Dynamic Exploration on Segment-Proposal Graphs for Tubular Centerline Tracking](https://mlsanity.com/paper/2506.18930/) (2025)
- [Real-Time Guidewire Tip Tracking Using a Siamese Network for Image-Guided Endovascular Procedures](https://mlsanity.com/paper/2507.00051/) (2025)

## Abstract (arXiv)

Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and vessels. As navigation through lumens and vessels is quite complex, interest grows to establish autonomous navigation techniques for IP and EI for reaching the target area. Current research efforts are directed toward increasing the Level of Autonomy (LoA) during the navigation phase. One key ingredient for autonomous navigation is Motion Planning (MP) techniques. This paper provides an overview of MP techniques categorizing them based on LoA. Our analysis investigates advances for the different clinical scenarios. Through a systematic literature analysis using the PRISMA method, the study summarizes relevant works and investigates the clinical aim, LoA, adopted MP techniques, and validation types. We identify the limitations of the corresponding MP methods and provide directions to improve the robustness of the algorithms in dynamic intraluminal environments. MP for IP and EI can be classified into four subgroups: node, sampling, optimization, and learning-based techniques, with a notable rise in learning-based approaches in recent years. One of the review's contributions is the identification of the limiting factors in IP and EI robotic systems hindering higher levels of autonomous navigation. In the future, navigation is bound to become more autonomous, placing the clinician in a supervisory position to improve control precision and reduce workload.