Shape Reconstruction and Tip Force Estimation in Tendon-Driven Soft Robots Using Physics-Informed Neural Networks
Bibliographic record
Abstract
This paper presents a theoretical model for the shape reconstruction of soft robots under a tip force, a tip moment, and a distributed load along the body, akin to blood flow forces acting on ablation catheters in the left atrium. The model is based on the Euler-Bernoulli beam theory and cubic Bézier curve approximation. Verification against a finite element model shows a maximum relative error below 5%. However, the average refresh rate (~1.1 Hz) falls short for clinical use, presenting a limitation. To address this, an artificial neural network (ANN) model is introduced and trained using data from the theoretical model over a range of tip forces, moments, and distributed loads. The ANN model exhibited an average refresh rate of 50 Hz, making it suitable for clinical applications. Given the importance of tip force estimation in minimally invasive surgery, an additional ANN model was developed to estimate the tip force, based on the deformed shape, tip moment, and distributed load. The model is trained and validated using rearranged data from the theoretical model and compared with in-vitro experimental results from a custom apparatus simulating blood flow interactions with ablation catheters. A strong agreement between the theoretical and experimental results was found, which confirms the model’s validity. This study underscores the potential of neural network models for accurate shape reconstruction and force estimation in soft robots, particularly in minimally invasive surgeries.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".