Bibliographic record
Abstract
Cooperative Localization (CL) synergizes strongly with multi-quadrotor systems, particularly Unmanned Aerial Vehicles (UAV), which are actively developed for applications such as monitoring, surveillance, and search-and-rescue missions. By enabling UAVs to measure inter-vehicle poses and share data to jointly estimate their states, CL enhances positioning performance and supplements Global Navigation Satellite Systems in cases of signal degradation or denial. This thesis develops a new Cooperative Localization System (CLS) for quadrotor UAVs, employing an error-state Kalman filter. Through a novel observability analysis, unobservable configurations specific to quadrotors are identified, ensuring the practical deployment of the system. The system's state estimation accuracy is validated through extensive numerical simulations. Building on this system, trajectory optimization is explored to improve CL performance. An uncertainty-aware trajectory optimizer, which minimizes estimation uncertainty through a measure of the Kalman covariance matrix, is developed. The differential flatness property of quadrotors and polynomial parameterization of trajectories address challenges with numerical integration in minimizing covariance. However, computational complexity limits this approach to offline trajectory optimization only, preventing its transition to real-time feedback control. To overcome these limitations, observability-aware trajectory optimization is introduced into the area of CL. A proof linking maximization of observability to minimization of estimation uncertainty supports this approach. Inspired by this, a novel optimal control problem is proposed, using a new approximation of the Local Observability Gramian to maximize observability. A solution to this Observability-Aware Control Problem is applied to real-time control of quadrotor UAVs in simulations and to trajectory optimization in flight tests. Both simulation and experimental results demonstrate the efficacy of Observability-Aware Control in enhancing positioning precision through CL. The progressive development of a CLS, an Uncertainty-Aware Trajectory Optimizer, and finally an Observability-Aware Controller, is envisioned as a significant contribution to cooperative navigation and control of UAVs in environments deprived of Global Navigation Satellite Systems (GNSS) coverage.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".