Robotic needle steering in deformable tissues with extreme learning machines

TL;DR

This paper introduces Extreme Learning Machines for rapid and precise robotic needle steering in soft tissues, significantly improving command speed and maintaining accuracy on unseen trajectories.

Contribution

It presents a novel application of Extreme Learning Machines trained on synthetic data to enhance control speed and accuracy in robotic needle steering.

Findings

Commands inferred 66% faster than inverse simulation

Model maintains acceptable precision on unseen trajectories

Improved control stability with faster command rates

Abstract

Control strategies for robotic needle steering in soft tissues must account for complex interactions between the needle and the tissue to achieve accurate needle tip positioning. Recent findings show faster robotic command rate can improve the control stability in realistic scenarios. This study proposes the use of Extreme Learning Machines to provide fast commands for robotic needle steering. A synthetic dataset based on the inverse finite element simulation control framework is used to train the model. Results show the model is capable to infer commands 66% faster than the inverse simulation and reaches acceptable precision even on previously unseen trajectories.