Path Planning for Concentric Tube Robots: a Toolchain with Application to Stereotactic Neurosurgery

TL;DR

This paper introduces a comprehensive path planning toolchain for concentric tube robots, utilizing obstacle representation and homotopy methods, demonstrated through a complex stereotactic neurosurgery application with real-world data.

Contribution

The paper develops a novel path planning approach combining obstacle modeling and homotopy techniques for concentric tube robots, validated on real neurosurgical data.

Findings

Successfully planned paths through over a thousand obstacles.

Demonstrated applicability to real-world stereotactic neurosurgery.

Provided a detailed example with complex obstacle fields.

Abstract

We present a toolchain for solving path planning problems for concentric tube robots through obstacle fields. First, ellipsoidal sets representing the target area and obstacles are constructed from labelled point clouds. Then, the nonlinear and highly nonconvex optimal control problem is solved by introducing a homotopy on the obstacle positions where at one extreme of the parameter the obstacles are removed from the operating space, and at the other extreme they are located at their intended positions. We present a detailed example (with more than a thousand obstacles) from stereotactic neurosurgery with real-world data obtained from labelled MPRI scans.