Influence of Network Topology on UAVs Formation Control based on Distributed Consensus
Bibliographic record
Abstract
In the task of formation control, there are many autonomous agents with detection and communication capabilities, with positions defined in different reference coordinates. Through a consensus algorithm, agents reach a common understanding of information shared locally through a communication topology, allowing UAVs to move while following a reference trajectory and maintaining the desired geometric configuration. Motivated by the fact that the communication topology is essential for the task of coordinating multi-agent systems, in this article we investigate the influence and characteristics of the fixed communication network topology on the distributed consensus performance, considering four communication network models. The simulations are performed using a multi-agent software-in-the-loop simulation platform in a ROS/Gazebo architecture for the control and three-dimensional simulation of UAVs and Matlab/Simulink for the implementation and execution of the formation control algorithm based on consensus distributed with a leader-follower approach. We performed simulations for different parameters of the considered network topology models, using the same trajectory and formation shape. We present the results of simulation tests, visually and quantitatively evaluating the performance of the distributed consensus, and relating this performance to the communication network models considered in this work and the metrics extracted from these randomly generated communication topologies.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".