Motion Planning For Steerable Needles in 3D Environments with Obstacles Using Rapidly-Exploring Random Trees and Backchaining

Jijie Xu, Vincent Duindam, Ron Alterovitz, and Ken Goldberg

Steerable needles composed of a highly flexible material and with a bevel tip offer greater mobility compared to rigid needles for minimally invasive medical procedures. However, planning the motion of a steerable needle in a 3D environment is challenging because the needle is subject to nonholonomic motion contraints and is underactuated.

In this work, we apply a sampling-based motion planning technique to steerable bevel-tip needles in 3D environments with obstacles. Inspired by the Rapidly-exploring Random Trees (RRTs) method, we develop a motion planner to quickly build a tree to search the configuration space using a new exploration strategy, which generates new states by randomly sampling the control space instead of using the deterministic approach of classic RRTs.

Since feasible paths might not be found for any given entry point and target configuration, we address the feasible entry point planning problem to find feasible entry points in a specified entry zone for any given target configuration. To solve this problem, we developed a motion planning algorithm based on RRTs with backchaining, which grows the tree backward from the target to explore the configuration space.

Exploration of the RRTs		The feasible path found