  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.
