Static Workspace Optimization of Aerial Cable Towed Robots With Land-Fixed Winches
Bibliographic record
Abstract
This article focuses on the static workspace (SW) of aerial cable towed robots (ACTRs) with land-fixed winches and provides an optimization approach to maximize the size of such a workspace. In the structure of the studied robots, land-fixed winches beside the ACTRs, actuated by unmanned aerial vehicles (UAVs), are used to manipulate a platform to reach high-altitude poses and balance platform's interaction force/moment in such poses. Capability of UAVs in choosing and holding different positions and orientations enables the studied robots to adapt their available net wrench set (AW) to various required net wrench sets. In order to find the SW of ACTRs with land-fixed winches, at first, AW of a generic robot for a given arrangement of UAVs is developed analytically. Then, a geometrical approach is provided to find all collision-free arrangements of the UAVs. Based on that, a performance index is derived and optimized to find an optimal collision-free arrangement of the UAVs, which maximizes the magnitude of the force that can be balanced by the platform in any arbitrary direction. Finally, the application of the proposed optimization approach in size maximization of the SW is shown in an example.
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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 | 0.000 |
| 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 | 0.001 |
| 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".