Preliminary field trials of autonomous path following
Bibliographic record
Abstract
As part of the ongoing Responsive AUV Localization and Mapping project (REALM) Memorial University has been developing an autonomous path localization and following system. This Qualitative Navigation System (QNS) localizes an AUV to a predefined path and generates control inputs to maintain the AUV along that path without the need for an absolute position estimate. QNS processes sonar data into the two-dimensional image space and performs feature extraction and matching. The results of this matching are input into a filter which allows localization of the AUV on the path and determination of either a waypoint or heading to maintain the AUVs traversal along the path. This ability to autonomously follow a path will be of great use for long term environmental monitoring. In May of 2013 QNS was deployed on Memorial's Explorer AUV and field tested in Holyrood, Newfoundland. International Submarine Engineering, manufacturer of the Explorer AUV, provided an interface which allowed the QNS software to request control of the AUV, provide command inputs, and relinquish control. A test path consisting of two connected 755m and 470m line sections was defined and used for preliminary tests. Although testing time was constrained, a series of successful tests were completed in which the AUV autonomously detected the path, localized itself and traversed the path to completion. The results of these tests validate the concept of QNS and the AUV control interface and will drive ongoing development and testing.
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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) | 0.000 | 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 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".