A Linkage-Based Gripper Design with Optimized Data Transmission for Aerial Pick-and-Place Tasks
Bibliographic record
Abstract
Aerial grasping is beginning to revolutionize industrial applications through robotics in Industry 4.0. However, this sector still lacks a gripper mechanism effective in autonomous grasping of in-house cargo and simple enough for rapid generation and implementation on a variety of industrial drones. A novel four-bar linkage rigid gripper was developed to address these challenges. This gripper is constructed of lightweight multi-material 3D printed components facilitating rapid construction and designs. The linkage setup allows for easy scaling while modular end effectors optimize performance for varying gripping applications. Manual gripping tests along with autonomous pick-and-place missions were conducted to evaluate the overall performance. The results demonstrate viability and point towards design adjustments and robust control algorithms for improved autonomous grasping under ground effect. The gripper in this work was designed and tested on the COEX Clover Drone available in the host lab. Its design can be extended and adjusted to any other aerial vehicles in general.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".