A Navigation and Decision Making Architecture for Unmanned Ground Vehicles: Implementation and Results with the Raptor UGV
Bibliographic record
Abstract
Abstract : Researchers at Defence R&D Canada- Suffield, under the Autonomous Land Systems (ALS) and Cohort projects, have been working to extend/enhance the capabilities of Unmanned Ground Vehicles (UGVs) beyond tele-operation. The goal is to create robotic platforms that are effective with minimal human supervision in outdoor environments. This report is a summary of the progress made in high level vehicle control, specifically the implementation and testing of algorithms providing point-to-point navigation and decision making capabilities for UGVs. To reach goals by traversing unknown terrain requires a number of navigation functions, including path tracking, obstacle avoidance, path planning and decision making modules. This report presents details of the theoretical underpinnings, the software design architecture, and results of implementing autonomous navigation and decision making software on a robotic platform, given competing priorities and limited sensing technologies.
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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 | 0.001 | 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 | 0.000 | 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".