Task Taxonomy for Autonomous Unmanned Aerial Manipulator: A Review
Bibliographic record
Abstract
Abstract The development of unmanned aerial manipulators (UAMs) allows a novel class of flying robots to carry out a wide variety of tasks in difficult environments due to their versatility and autonomy. However, the different tasks that can be carried out might call for different control strategies. To this end, one needs to categorize the possible tasks accomplishable by UAMs. This paper proposes a novel taxonomy, which is the result of a video information acquisition methodology combined with a review of research works in the literature. The different elements of the taxonomy are separated using a higher level of abstraction in a way that the general description of the tasks are considered and not its operational details. To illustrate the fact that algorithms must adapt to different tasks, a description of the usual UAM architecture is carried out. Four categories of criteria are used in the taxonomy to differentiate all possible tasks. These categories are the interaction type, the actual task definition, the environment condition and the time sensitivity of the task. This taxonomy forms the basis for possible machine-learning-based task classifiers that could be used in autonomous UAMs control and mission planning. Multiple tasks defined in the taxonomy can be combined to accomplish complex missions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".