Multi-unmanned vehicle systems (nUxV) at Defence R&D Canada
Bibliographic record
Abstract
No single UxV is perfectly suited to all task assignments. A homogeneous UxV team, for example, a troop of identical UGVs, brings redundancy and reliability to a specific class of tasks. Heterogeneous UxV teams, for example, a troop of UGVs, a flight of low flying rotorcraft, and a high flying UAV, provide increased capability. They can tackle multiple tasks simultaneously through cooperative decision making, distributed task allocation, and collective mapping. Together, they can convoy payloads, provide communications, observe targets, shield troops, and, ultimately, deliver munitions. nUxVs have the potential to share, learn, and adapt information between like platforms and across platform types, to produce expanded capability and greater reliability. Current research exploits simple vehicle state exchange, communications relay and formation keeping. Our near-term research areas include map sharing and integration, task coordination, and heterogeneous nUxV teaming. Future research will address military nUxV C2; nUxV capability definition and understanding; behaviour-based and reactive nUxVs, emergence and stigmergy; and collaboration and interaction between human-robot teams.
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 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 | 0.000 |
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".