Experiments on Collaborative Transport of Cable-suspended Payload with Quadrotor UAVs
Bibliographic record
Abstract
The use of drones to transport cargo is an important application of unmanned aerial vehicles. Given the limited payload capacity of a typical small drone, the notion of utilizing multiple drones to transport heavy payloads presents a promising alternative. This article describes an easy to deploy system of multiple drones with a cable-suspended payload to enable flight testing of guidance, navigation, and control strategies for such systems in realistic operating conditions, outside of a laboratory. A unique aspect of our system is the use of Ethernet cables to ensure fast and reliable communications between vehicles. Deploying the system with a basic leader- follower guidance strategy and the PX4 flight stack for low-level control of each vehicle, we demonstrate collaborative payload transport through an extensive experimental campaign. We are able to autonomously transport payloads up to 2kg with two vehicles and up to 3kg with three off-the-shelf, 1kg vehicles. The paper also presents a brief discussion of failure cases and points to worthwhile directions for further research on this topic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".