🤖 AI Summary
This study addresses the limitations of conventional fiber Bragg grating (FBG)-based shape sensing methods—such as low spatial resolution and reliance on surface adhesion—in steerable concentric tube drilling robots. The authors propose a novel, integrated shape-sensing assembly (SSA) embedded within the flexible drill’s internal channel, combining a single optical frequency domain reflectometry (OFDR) fiber with a NiTi flat wire to enable continuous, high-resolution strain measurement along the entire length without requiring external mounting. This approach allows real-time reconstruction of three-dimensional deformation and represents the first application of OFDR-based intrinsic shape sensing in concentric tube drilling robots. Experimental validation in both free-bending configurations and J-shaped drilling trajectories within Sawbones phantoms demonstrates the method’s high accuracy, robustness, and practical utility.
📝 Abstract
This paper introduces a novel shape-sensing approach for Concentric Tube Steerable Drilling Robots (CT-SDRs) based on Optical Frequency Domain Reflectometry (OFDR). Unlike traditional FBG-based methods, OFDR enables continuous strain measurement along the entire fiber length with enhanced spatial resolution. In the proposed method, a Shape Sensing Assembly (SSA) is first fabricated by integrating a single OFDR fiber with a flat NiTi wire. The calibrated SSA is then routed through and housed within the internal channel of a flexible drilling instrument, which is guided by the pre-shaped NiTi tube of the CT-SDR. In this configuration, the drilling instrument serves as a protective sheath for the SSA during drilling, eliminating the need for integration or adhesion to the instrument surface that is typical of conventional optical sensor approaches. The performance of the proposed SSA, integrated within the cannulated CT-SDR, was thoroughly evaluated under free-bending conditions and during drilling along multiple J-shaped trajectories in synthetic Sawbones phantoms. Results demonstrate accurate and reliable shape-sensing capability, confirming the feasibility and robustness of this integration strategy.