Learning-Based Kinematic Modeling for Concentric Tube Robot: Addressing Its Nonlinearity and Snapping Behavior
Gowoon Jeong, Seong Young Ko
AI summary
Problem
Accurately modeling the nonlinear, history-dependent kinematics of concentric tube robots, particularly snapping and hysteresis effects, remains a major challenge for real-time microsurgical control.
Approach
The authors developed a lightweight LSTM-MLP hybrid neural network with a history input buffer and directional rotation parameters to learn and predict both forward and inverse kinematics from experimental data.
Key results
- Forward kinematics RMSE of 0.69 mm and 0.16°
- Inverse kinematics RMSE of 1.22 mm and 2.46°
- Sub-millisecond computation time (~0.83 ms) for real-time control
- Accurate prediction of snapping and hysteresis outperforming conventional models
Why it matters
Enables safer, high-precision real-time control for minimally invasive microsurgical applications where unpredictable tube snapping and hysteresis currently limit reliability.
Abstract
The Concentric Tube Robot (CTR) has great promise for minimally invasive surgery. However, accurately modeling nonlinear and history-dependent behaviors remains a significant challenge. This letter proposes a learning-based forward and inverse kinematics model that accounts for the history dependence and nonlinearities of CTR, including the snapping behavior. A lightweight LSTM-MLP hybrid neural network with an input buffer and directional parameters was used to train forward and inverse kinematics models for 4-degree-of-freedom (DOF) CTR. The model was validated by comparing its predictions with actual values and results from a conventional torsional-compliant model (TCM) across random points, rotational trajectories, and arbitrary paths. This validation successfully demonstrated the model’s ability to capture snapping behavior. For forward kinematics, the model achieved a Root Mean Square Error (RMSE) of 0.69 mm and 0.16° with a computation time of 0.831±0.200 ms. The inverse kinematics model achieved an RMSE of 1.22 mm and 2.46° with a computation time of 0.816±0.200 ms. The proposed method improves the accuracy and speed of kinematic modeling by capturing nonlinear behaviors, such as snapping and hysteresis. The lightweight system ensures accurate real-time control and offers a safer and more reliable solution for microsurgical applications.