Design and Real-Time Optimization for a Magnetic Actuation System With Enhanced Flexibility
Bibliographic record
Abstract
In this article, a magnetic actuation system based on three mobile electromagnetic coils is designed and a control strategy for the system is proposed. Enhanced flexibility combined with optimization algorithms enables the system to satisfy various requirements in applications, such as avoiding collision between the coils and the obstructions within the workspace, placing the coils to optimal positions to enhance energy efficiency, generating wide varieties of magnetic field for complex tasks, and tracking the location of the robot with enlarged workspace. To reach that purpose, a model of the system is built for magnetic field calculation, and a real-time optimization algorithm based on particle swarm optimization combined with a collision detection algorithm is proposed and implemented to calculate optimal positions for coils and at the same time avoid collision. We fabricate a prototype system, named RoboMag, to prove the concept. Simulations and experiments on helical swimmer and soft robot are conducted to evaluate the performance. Compared with two conventional control strategies, the demanded currents for long-distance actuation are reduced by up to 62.7%. The calculation process is conducted in real time and the coils are able to avoid collision with the barriers inside the workspace during actuation. Moreover, generation and steering of a microrobotic swarm is demonstrated, showing the capability of the system in generating programmed dynamic fields for complicated tasks.
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.
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".