Robotic needle steering in deformable tissues with extreme learning machines
This work addresses needle positioning accuracy in medical robotics, but it is incremental as it builds on existing control frameworks with a new machine learning approach.
The study tackled the problem of robotic needle steering in soft tissues by using Extreme Learning Machines to generate fast control commands, achieving a 66% speed improvement over inverse simulation while maintaining acceptable precision on unseen trajectories.
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.