Automatic optimized 3D path planner for steerable catheters with heuristic search and uncertainty tolerance

In this paper, an automatic planner for minimally invasive neurosurgery is presented. The solution can provide the surgeon with the best path to connect a user-defined entry point with a target in accordance with specific optimality criteria guaranteeing the clearance from obstacles which can be found along the insertion pathway. The method is integrated onto the EDEN2020∗ programmable bevel-tip needle, a multi-segment steerable probe intended to be used to perform drug delivery for glioblastomas treatment. A sample-based heuristic search inspired to the BIT* algorithm is used to define the optimal solution in terms of path length, followed by a smoothing phase required to meet the kinematic constraint of the catheter. To account for inaccuracies in catheter modeling, which could de- termine unexpected control errors over the insertion procedure, an uncertainty margin is defined so that to include a further level of safety for the planning algorithm. The feasibility of the proposed solution was demonstrated by testing the method in simulated neurosurgical scenarios with different degree of obstacles occupancy and against other sample-based algorithms present in literature: RRT, RRT* and an enhanced version of the RRT-Connect.