Fixed-time adaptive cooperative control architecture for air-ground target tracking using UAV and UGV with dynamic constraints
Why this work is in the frame
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Bibliographic record
Abstract
Tracking targets using localisation methods often involves coordinated efforts between different types of vehicles. In robotics, airground visual cooperation has become increasingly prominent. However, localisation accuracy remains a challenge due to the lack of precise cooperative mechanisms for identifying targets. To address this, we propose a cooperative tracking framework using a UAV and a UGV, operating under line-of-sight and relative distance constraints. Our approach employs symmetric and asymmetric barrier functions within a backstepping design to manage multiple performance and safety constraints. A radial basis function (RBF) neural network is used to estimate the target's velocity, with adaptive laws learning the ideal weight matrix. Additionally, a terminal sliding surface method is integrated with the neural network to handle attitude uncertainties and external disturbances. The proposed control strategy guarantees that tracking errors converge to a small neighbourhood around zero within a fixed time. Numerical simulations demonstrate the effectiveness of the approach.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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