Collaborative Task Planning for PCB-Driven Magnetic Microrobots Using Deep Reinforcement Learning
Bibliographic record
Abstract
Magnetic field-driven microrobots hold great promise for medical applications due to their ability to safely navigate within complex environments. Traditional global magnetic field approaches are typically limited to controlling single microrobots, making multimicrorobot coordination a challenge. To overcome such challenge, we develop a novel microrobot system driven by a 12 × 12 microcoil array on a printed circuit board, which generates localized magnetic fields for independent control of multiple microrobots. This system enables autonomous multitask planning under complex obstacle constraints. Using deep reinforcement learning enhanced by A* algorithm guidance, we facilitate efficient and dynamic collaborative task planning. The proposed approach improves adaptability and coordination between microrobots in environments with multiple obstacles. Experimental results, including tests in dynamic complex environment with multimicrorobots, demonstrate that our system effectively achieves coordinated, interference-free paths. This work offers a significant advancement in dynamic task planning and coordination of multimicrorobots, providing a powerful solution for complex collaborative tasks such as autonomous and efficient reagent detection and drug fusion in medical treatment.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".