Assessment of Distributed Flocking Algorithms for Multi-Fixed-Wings UAVs using SIL Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper aims to implement and validate the flocking approaches known as Olfati-Saber and Vásárhelyi algorithms that are originally developed for fully-actuated systems for coordinating underactuated Unmanned Aerial Vehicles (UAVs) teams in a stochastic environment using a Software-in-the-Loop (SIL) Simulation Platform based on realistic aircrafts with nonlinear dynamic models. These algorithms allow the UAVs to maintain the cohesion, alignment and separation between the agents of the team during flight by attending to the distance criteria during the path-following and avoiding collision among them. For validating the investigated approaches, the proposed SIL platform uses a team of virtual UAVs based on the non-linear aerodynamic model of the Cessna 172 Skyhawk implemented in a ROS/Gazebo architecture, while the Matlab/Simulink executes of the flocking control algorithms. Simulation results demonstrated that the flocking algorithms can efficiently coordinate the underactuated UAVs teams considering their nonlinear dynamics and also the communication among all of them during the execution of different flight maneuvers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it