A Compact Aerial Manipulator: Design and Control for Dexterous Operations
Bibliographic record
Abstract
Abstract The lack of aerial physical interaction capability is one of the choke points limiting the extension of aerial robot applications, such as rescue missions and aerial maintenance. We present a new aerial robotic manipulator (AEROM) for aerial dexterous operations in this work. It contains a robotic manipulator with 6-degree-of-freedom and a compact flight platform. Firstly, we propose a quantitative capability index to evaluate and guide the mechanical design of the AEROM. Based on the proposed quantitative index, we construct a lightweight bird-inspired manipulator to imitate a raptor hindlimb. An additional telescopic joint and an end-effector consisting of three soft fingers allow the AEROM to execute aerial interaction tasks. In addition, the wrist joints enable independent control of the end-effector attitude regardless of the flight platform. After explicitly analyzing the multi-source disturbances during the aerial operation tasks, we develop a refined anti-disturbance controller to compensate for the disturbances with different characteristics. The proposed controller further improves the position accuracy of end-effector to enable dexterous operations during aerial interaction tasks. Finally, the physical experiments verify the effectiveness of the proposed AEROM system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".