Dynamic tracking of moving objects in microassembly through visual servoing
Bibliographic record
Abstract
Precise micropart alignment is a crucial factor in most gripper-based microassembly processes. For the micropart to be grasped or manipulated, these processes require the micropart to be positioned and oriented properly for the microgripper. At present, many of the these processes still rely on operators to monitor and align the micropart manually through visual images provided by the camera on top of the assembly line. However, due to the limited field of view of the microassembly system microscope, the micropart may move outside of the visual monitoring area at some point during the manipulation process. The present work proposes an integrated microassembly algorithm that performs the assembly process regardless of the micropart initial orientation. The algorithm automatically aligns and tracks the micropart during the manipulation process. As the micropart rotates to the required grasping orientation, the algorithm projects the future motion of the micropart, repositions it, and simultaneously, using a PID control algorithm, maintains the micropart within the field of view of the microscope. The proposed algorithm eliminates the need for a manual alignment process, which is time consuming and is subject to error. The algorithm was implemented and evaluated on an in-house 6 DOF microassembly manipulator. Experimental results confirmed that the proposed algorithm successfully tracked and corrected a 45-degree misaligned micropart at a specified location within the camera field of view with a steady-state error of +/-15 pixels.
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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) | 0.000 | 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 | 0.001 | 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".