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.

翻译：微创手术需求的增长加速了经体腔与血管实施的腔内手术(IP)及血管内介入(EI)的推广应用。由于经腔道与血管导航具有较高复杂性，建立IP与EI自主导航技术以到达目标区域的研究兴趣日益浓厚。当前研究致力于提升导航阶段的自主等级(LoA)，其中运动规划(MP)技术是实现自主导航的关键要素。本文基于LoA对MP技术进行分类综述，系统分析不同临床场景的技术进展。通过采用PRISMA方法的系统性文献分析，本研究汇总相关成果并探究临床目标、LoA水平、所采用的MP技术及验证方式。我们识别了各类MP方法的局限性，并提出了在动态腔内环境中提升算法鲁棒性的改进方向。IP与EI的MP可分为节点法、采样法、优化法及学习法四类子集，其中基于学习的方法近年来显著增长。本综述的贡献之一在于揭示了限制IP与EI机器人系统实现更高级别自主导航的关键因素。未来导航将趋于高度自主化，使临床医师处于监督地位，从而提升操控精度、降低工作负荷。