Facilitating Safe Automated Navigation in Ice with Cooperating Autonomous Vehicles
Bibliographic record
Abstract
Autonomous shipping has gained much interest in the last decade stemming from several desirable safety and environmental benefits. A significant boost to Canada's autonomous shipping drive would be adequately monitoring ice conditions in shipping environments with unmanned assets and calculating safe navigation routes for autonomous ships. A workflow was created to simulate an unmanned aerial vehicle (UAV) cooperating with a maritime autonomous surface ship (MASS) to facilitate the MASS's safe navigation through an icy waterway. Both vehicles were modeled as MATLAB Simulink's Sim3D objects operating in an icy shipping environment representing a section of Canada's St Lawrence Seaway and built with Unreal Game Engine version 4.26.2. The UAV performed regular ice-condition-monitoring flights over the shipping environment with its vision and LiDAR sensors. Maps of Ice Numerals (IN) pertaining to the MASS's class were developed from processed data from the UAV's sensors. They served as inputs to a Rapidly Exploring Random Tree (RRT) algorithm that estimated safe obstacle free routes for the MASS through the shipping environment. Colour-coded hazard maps specifying safe and hazardous waterway regimes were also developed from IN maps. Hazard maps could serve as navigational aids to operators monitoring MASS operations in hazardous environments. Results obtained from testing the workflow on the MA TLAB Simulink + Unreal Engine simulation environment produced the desired INs and hazard maps with safe navigation routes for the MASS. These results show the possibility of having autonomous assets corporate to facilitate safe autonomous navigation in ice.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".