Multi-thread technology based autonomous underwater vehicle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An autonomous underwater vehicle (AUV) is implemented with multi-thread technology under QNX Neutrino Real time Operating System. The hybrid system and autonomous control principle of AUV are introduced. The event generator thread and event analysis and decision making thread are two key threads in the autonomous control system to handle discrete events, which are described in detail. Also the histogram in motion mapping algorithm with growth rate operator is adopted to process the sonar data and to express the uncertainty of sonar data. The obstacle avoidance algorithm based on sonar data is designed to enable vehicle to avoid obstacle immediately, effectively and safely. Finally we verify this autonomous control system with simulation study and experiment in lake. The simulation and experiment results show that the proposed AUV autonomous control system can fulfill the missions effectively.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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