The Simulation and Path Tracking Control Study of Magnetic Miniature Soft Robots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Magnetic miniature soft robots hold significant potential in biomedical research, especially for targeted therapy, drug delivery, and cell manipulation. Precise path tracking control is crucial for these robots in complex biomedical applications. Here, we propose a Stanley path tracking control algorithm based on visual feedback for magnetic soft robots. First, a magnetic miniature soft crawling robot was designed and fabricated, and its crawling mechanism was detailed. Next, a simulation framework using the material point method (MPM) was constructed to simulate the movement and deformation of the miniature robot and to verify the proposed crawling mechanism. Finally, visual feedback technology was used to obtain the robot's position and posture, and the Stanley algorithm was applied for path tracking control in crawling mode. The effectiveness of the proposed path tracking control strategy has been verified through multiple experiments. Compared with the traditional Pure Pursuit control method, it has higher robustness and better control accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it