UAV autonomy for complex environments
Bibliographic record
Abstract
Complexity is a dominant, multi-dimensional attribute of the battlespace, and is evident in the geography, manmade infrastructure, force asymmetry and organizational processes. The Unmanned Aerial Vehicle represents a strategic enabler for military operations in complex environments by providing a flexible means of acquiring real-time information and deriving actionable knowledge. Limitations arising from remotely piloted UAV operation together with the desired operational flexibility in complex environments both dictate the need for increasingly autonomous UAV operation within a rigorous airspace integration framework. UAV autonomy relies primarily on access to missioncritical information from on-board sensors and networked datalink, together with comprehensive, efficient and robust algorithms for decisions on course of action. Global battlefield networking extends the notion of individual vehicle operation to a coordinated team, whose members carry out complementary and/or redundant tasks. DRDC research on cooperative teaming of UAVs covers in particular the development and implementation of cooperative control based on model predictive control. In the context of operations in complex environments, the present paper discusses the selected approach to cooperative control, and presents applications to formation flight, collision avoidance, real-time implementation and multi-processing, and fault-detection, isolation and recovery.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".