Reach-Avoid Control Synthesis for a Quadrotor UAV with Formal Safety Guarantees
Bibliographic record
Abstract
Reach-avoid specifications are one of the most common tasks in autonomous aerial vehicle (UAV) applications. Despite the intensive research and development associated with control of aerial vehicles, generating feasible trajectories though complex environments and tracking them with formal safety guarantees remain challenging. In this paper, we propose a control framework for a quadrotor UAV that enables accomplishing reach-avoid tasks with formal safety guarantees. In this proposed framework, we integrate geometric control theory for tracking and polynomial trajectory generation using Bezier curves, where tracking errors are accounted for in the trajectory synthesis process. To estimate the tracking errors, we revisit the stability analysis of the closed-loop quadrotor system, when geometric control is implemented. We show that the tracking error dynamics exhibit local exponential stability when geometric control is implemented with any positive control gains, and we derive tight uniform bounds of the tracking error. We also introduce sufficient conditions to be imposed on the desired trajectory utilizing the derived uniform bounds to ensure the well-definedness of the closed-loop system. For the trajectory synthesis, we present an efficient algorithm that enables constructing a safe tube by means of sampling-based planning and safe hyper-rectangular set computations. Then, we compute the trajectory, given as a piecewise continuous Bezier curve, through the safe tube, where a heuristic efficient approach that utilizes iterative linear programming is employed. We present extensive numerical simulations with a cluttered environment to illustrate the effectiveness of the proposed framework in reach-avoid planning scenarios.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".