<title>Novel mobility platforms utilizing intelligent algorithms</title>
Bibliographic record
Abstract
The mobility requirement for Unmanned Ground Vehicles (UGVs) is expected to increase significantly as the number of conflicts shift from open terrain operations to the increased complexity of urban settings. In preparation for this role Defence R&D Canada-Suffield is exploring novel mobility platforms utilizing intelligent mobility algorithms that will each contribute to improved UGV mobility. The design of a mobility platform significantly influences its ability to maneuver in the world. Highly configurable and mobile platforms are typically best suited for unstructured terrain. Intelligent mobility algorithms seek to exploit the inherent dexterity of the platform and available world representation of the environment to allow the vehicle to engage extremely irregular and cluttered environments. As a result, the capabilities of vehicles designed with novel platforms utilizing intelligent mobility algorithms will outperform larger vehicles without these capabilities. However, there exist many challenges in the development of UGV systems to satisfy the increased mobility requirement for future military operations. This paper discusses a research methodology proposed to overcome these challenges, which primarily involves the definition and development of novel mobility platforms for intelligent mobility research. It addresses intelligent mobility algorithms and the incorporation of world representation and perception research in the creation of necessary synergistic systems. In addition, it presents an overview of the novel mobility platforms and research activities at Defence R&D Canada-Suffield aimed at advancing UGV mobility capabilities in difficult and relevant military environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".