Robotic-assisted tracking control of magnetoactive soft continuum robots in magnetic gradients
Bibliographic record
Abstract
Abstract Stroke and other neurovascular disorders demand advanced biomedical technologies capable of precise operation in confined anatomical environments. Magnetoactive soft continuum robots (MSCRs) offer flexibility and magnetic responsiveness for minimally invasive interventions, yet real-time control under nonuniform magnetic fields remains challenging. This study presents a novel robotic-assisted, closed-loop feedforward–feedback proportional–integral–derivative (FFPID) control strategy for accurate tip deflection and trajectory tracking of MSCRs actuated by a rotatable permanent magnet. A nonlinear quasi-static model of the MSCR is first developed to account for the effects of magnetic torque, magnetic body force, and gravity. The model is further extended to simulate MSCR behavior in a fluidic environment, better reflecting physiological vascular conditions. The external nonuniform magnetic field generated by a permanent magnet is modeled using finite element (FE) simulations in COMSOL Multiphysics and experimentally validated. A deep neural network trained on the FE dataset is utilized to efficiently predict 2D magnetic field magnitudes and gradients, providing real-time inputs to the quasi-static model. The developed model is subsequently employed to fine-tune the coefficients of the proposed FFPID control algorithm. The MSCR is fabricated and characterized through mechanical and magnetic testing, and a dedicated experimental platform is developed to evaluate its performance in both ambient and fluidic environments under varying magnetic gradients and fluid flow rates. A multi-tracker Eye-to-Hand calibration framework is implemented to ensure magnetic field alignment. Moreover, a trained deep learning–based model is developed for MSCR detection and deformation estimation. Four hardware-in-the-loop experiments are conducted using a six-degree-of-freedom robotic arm to assess the controller’s performance across different MSCR materials and boundary configurations. The results demonstrate the robustness and precision of the proposed FFPID control strategy across various experimental conditions, including operation under different fluid flow rates. The controller consistently outperforms the conventional PID in tracking experiments, highlighting its strong potential for real-time, robot-assisted neurovascular applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".