Model Predictive Control for Dynamic Quadrotor Bearing Formations
Bibliographic record
Abstract
Formation control of multi-agent systems deals with groups of robots forming specific spatial geometries. Combined with the advancements of unmanned aerial vehicles (UAVs) in the past decade, formation control may potentially be applied to tasks such as search-and-rescue, surveillance, even collaborative manipulation. A key challenge is the decentralization of formation control, where each agent behaves independently using onboard sensors and computation, improving the scaleability and robustness of the system.This paper proposes a decentralized controller based on model predictive control (MPC), for the control of formations of quadrotor UAVs defined by inter-agent bearings. The use of MPC allows the controller to account for attitude kinematics, improving upon the results of existing bearing formation control methods based on rigidity and visual servoing approaches, which typically only consider the quadrotor as a single or double integrator. Furthermore the near-optimality of MPC permits a more optimal use of the quadrotors dynamic capabilities for faster maneuvering. Extensive simulations are performed to demonstrate the improved transient formation convergence and fast maneuvering permitted by this controller. Experiments show that it is indeed a real-time feasible solution for bearing formation control.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".