Navigation of unmanned vehicles using a swarm of intelligent dynamic landmarks
Why this work is in the frame
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Bibliographic record
Abstract
The work presented here describes a novel 3D navigation method for teams of unmanned vehicles using intelligent dynamic landmarks (IDLs). The technique allows robots to navigate in diverse structured and unstructured environments both indoor and outdoor avoiding typical disadvantages of traditional navigational techniques. Some robots comprising the team are considered as IDLs while others use such landmarks to accomplish their task. The proposed approach does not require any type of traditional external landmarks or any kind of environmental model. Instead, robots continuously perform direct measurements of their relative position with respect to neighboring robots with which they interchange relative position information to verify relative and global localization. Robots process the obtained information to generate ego-centric estimates of the relative position of other robots using an origami graph. The proposed technique allows the team's configurations to change according to the task to be performed and allows effective navigation even under robots' mechanical and/or sensor failures.
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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 | 0.000 | 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 it