Real-time fault-tolerant cooperative control of multiple UAVs-UGVs in the presence of actuator faults
Bibliographic record
Abstract
This paper investigates fault-tolerant cooperative control (FTCC) strategy for a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. When actuator faults occur in one or more of the UGVs, two cases are considered: 1) the faulty UGV cannot complete its assigned task due to a severe fault occurrence, it has to get out from the formation mission. Then, FTCC strategy is designed to re-assign the mission to the remaining healthy vehicles; and 2) the faulty UGV can continue the mission with degraded performance, then the other team members will reconfigure their controllers considering the capability of faulty UGV. Thus, the FTCC strategy is designed to re-coordinate the motion of each UAV-UGV in the team. FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Real-time experiments are presented in order to demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".