Hierarchical Decentralized Receding Horizon Control of Multiple Vehicles with Communication Failures
Why this work is in the frame
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Bibliographic record
Abstract
This work presents a new approach for designing decentralized receding horizon controllers (DRHC) for cooperative multiple vehicle systems with inter-vehicle communication delays arising from communication failures. Using DRHC each vehicle plans its own state trajectory over a finite prediction time horizon. The neighboring vehicles then exchange their predicted trajectories at each sample time to maintain cooperation objectives. Such communication failures lead to large, inter-vehicle communication delays of exchanged information. Large inter-vehicle communication delays can potentially lead to degraded cooperation performance and unsafe vehicle motion. To maintain desired cooperation performance during faulty conditions, the proposed fault-tolerant DRHC architecture estimates the tail part of the neighboring vehicle trajectory that is unavailable due to communication delays. Furthermore, to address the safety of the team against possible collisions during faulty situations, a fault-tolerant DRHC is developed, which provides safety using a safe protection zone called a tube around the trajectory of faulty neighboring vehicles. The radius of the tube increases with communication delay and maneuverability. A communication failure diagnosis algorithm is also developed. The required communication capability for the fault-diagnosis algorithm and fault-tolerant DRHC suggests a hierarchical fault-tolerant DRHC architecture. Simulations of formation flight of miniature hovercrafts are used to illustrate the effectiveness of the proposed fault-tolerant DRHC architecture.
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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) | 0.000 | 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