Autonomous vehicle systems research at National Research Council Canada, Institute for Marine Dynamics (NRC-IMD)
Bibliographic record
Abstract
This paper provides general information on autonomous systems (vehicles) that operate in water, on land, in air and in space. The associated ideas lead directly to considerations that affect the design of fully-autonomous vehicles. We include a discussion of some of the engineering, logistical and systems-integration aspects of autonomous operations, including the use of multiple vehicles in a coordinated "fleet". We outline some of the technical challenges associated with working within a limited energy budget and mention some possible strategies for vehicle-to-vehicle communications and for vehicle-network configurations and management. The results from this research will permit planners and developers of autonomous vehicle systems to better allocate such limited assets in order to provide more effective operations in hazardous environments. We describe the design of one instance of such a vehicle, in the form of a highly-manoeuvrable autonomous underwater vehicle (AUV). We go on to describe our initial successes and lessons learned, and, our plans for subsequent development of the AUV and its role as a prototype member of small a "fleet" of AUVs. We describe some parallel work-in-progress (with a single AUV) that has commercial application in support of the Canadian East Coast offshore oil and gas industry. This work involves the AUV carrying suitable sensors to detect and quantify the presence of certain chemicals in ocean water samples. Current and future results from our research can be generalized to more effective operations of autonomous vehicles on land, in the air and oceans, and in space
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.022 |
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".