Optimized RRT Planning With CMA-ES for Autonomous Navigation of Magnetic Microrobots in Complex Environments
Bibliographic record
Abstract
Magnetic field-driven microrobots have shown high potential in the field of medical applications. The utilization of magnetic fields is particularly favorable due to its ability to penetrate deep tissues while ensuring high safety. Despite significant advancements in the fabrication, functionalization, and locomotion of magnetic microrobots, autonomous navigation is of paramount importance for magnetic microrobots. In light of this objective, this article introduces a novel navigation framework, using an improved path planning navigation method. The proposed method introduces a path planning algorithm, covariance matrix adaptation evolution strategy (CMA-ES) and rapidly-exploring random trees (RRT) (CMA-ES-RRT), which skillfully combines the advantages of both CMA-ES and RRT. The proposed framework not only guarantees a smooth path but also takes it a step further by significantly minimizing the overall navigational path length. These dual benefits are especially critical in medical applications, significantly improving the convenience of subsequent path tracking. Through meticulous algorithm comparisons and thorough analyses, our approach emerges as a superior choice, excelling in both path smoothness and length optimization. Extensive environmental validation analyzes unequivocally demonstrate our method's superiority over traditional RRT and its variants in terms of path smoothness and navigation path length.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".