A Two-Stage Formation Flying Strategy to Reduce the Mission Time
Bibliographic record
Abstract
Unmanned aerial vehicles (UAV) interoperability in a system of systems (SoS) is a relatively new line of research which is being investigated for future combat systems. This paper is concerned with formation flying of UAVs with minimum time mission requirement. It is assumed that a known finite set of different configurations exists, which characterizes the mission. This means that the desired configuration at each point in time belongs to this set. A reconfiguration strategy is then introduced which is carried out in two phases. The first phase starts upon the completion of the latest reconfiguration task. In this phase, each UAV moves to a pre-determined location which is obtained to be as close as possible to all potential next destinations given by the known set. All UAVs stay in this location during the idle time, i.e., while no new mission command is issued. The second phase begins once a new command is issued to reconfigure the formation. In this phase, all UAVs will move to the location specified by the new command. This two-stage strategy minimizes the reconfiguration time, which is quite desirable in many real-world applications. Simulation results demonstrate that the proposed strategy results in a significant reduction in the reconfiguration time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".