Data-Driven Detection of Needle Buckling Events in Robotic Needle Steering

Abstract

In robotic needle steering, flexible asymmetric-tip needles can steer around obstacles to reach targets deep within tissue. Due to tissue inhomogeneity and needle flexibility, needle buckling can occur, preventing accurate placement. This paper focuses on detecting needle buckling using axial force and needle-tip position readings from sensors. Our algorithm uses errors between the force readings and a predictive force model generated from those readings to track rapid changes in the measured forces. Using this prediction error and needle-tip position, the algorithm detects unexpected force increase, strict needle buckling, and buckling with sliding events at the needle-tip. The metrics for the detections are derived using a standard three-sigma rule and a sigmoid function to ensure generalizability of this method to a variety of tissue types. Our algorithm was tested using insertions into a gelatin tissue with an embedded rectangular obstacle designed to elicit buckling events. Needle buckling was detected at a maximum of 2[Formula: see text]mm after collision with the obstacle. Our algorithm was tested for robustness with insertions in an ex vivo tissue under different boundary conditions. Our algorithm was also able to detect buckling events 1–2[Formula: see text]s sooner than human detection times, showing significance for future autonomous control.