Real-Time Relative Map Path-Planning Model for Magnetically Levitated Planar Actuators
Bibliographic record
Abstract
Magnetically levitated planar actuators (MLPAs) are promising candidates for material handling systems in flexible manufacturing systems. The magnetic movers enable contactless motion. However, pioneering research is needed on collision avoidance and path planning for collaborative manufacturing applications. An MLPA with${1.8} \times {2.4}\; {{\mathrm{m}}^{2}}$horizontal operating surfaces was constructed for planning the motion of Halbach array movers (HAMs) and disc-magnet movers (DMMs). The low-magnetic-flux-density region and mover-coil search details were combined to determine an avoidance boundary. A local path-planning algorithm is proposed to navigate the movers safely along the desired path via a relative frame of reference. The operating speeds are determined based on the power consumption. Demanding higher operating powers, HAM is selected as the relative frame of reference. The optimal path is found in the relative map and converted to the MLPA frame for path tracking. A tangent-based method is embraced to search for the optimal path. A proof shows that the path optimality is preserved in all inertial frames. The feasibility of the proposed path-planning method is considered and verified. The proposed algorithm accommodates the structured workflows inherent in patterned manufacturing processes while allowing flexible conveyance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".