An Evolutionary-Optimized Surgical Path Planner for a Programmable Bevel-Tip Needle

Path planning algorithms for steerable needles in medical applications must guarantee the anatomical obstacle avoidance, reduce insertion length, and ensure compliance with needle kinematics. The majority of solutions from literature focus either on fast computation or path optimality, former at expense suboptimal paths, latter by making unbearable case a high-dimensional workspace. In this article, we implement three-dimensional planner neurosurgical applications, which keeps computational cost consistent standard preoperative fine-tunes estimated pathways accordance to multiple optimization objectives. From user-defined entry point, our method confines sample-based search within subsection original workspace considering degree curvature admitted needle. An evolutionary procedure is used maximize avoidance length. pool optimized examined through function determine best path. Simulations one dataset showed ability save time overcome state art terms probability failure, proving algorithm as valid complex environments.