Line-of-sight task-space sensing for the localization of autonomous mobile devices
Bibliographic record
Abstract
In this paper, a multi line-of-sight (LOS) task-space sensing methodology is presented for guidance-based localization of mobile devices (e.g., autonomous vehicles and robots). The mobility requirement of the localization/docking application dictates the minimum number and the type (planar or spatial) of the lines of sight. It is envisioned that, a multi-LOS sensing system will be configured for the task at hand using several, one or two degree-of-freedom (dof), sensing modules. One such module is also proposed in this paper: it comprises a laser source, a (1 or 2 dof) galvanometer mirror and a photodetector. A guidance algorithm would only be invoked at the final stages of vehicle/robotic-end-effector motion after the long-range positioning phase has failed to locate the vehicle at its desired pose (position and orientation). By utilizing a multi-LOS based sensing system the guidance algorithm would successfully minimize the systematic errors of the vehicle, while allowing it to converge to its desired pose within the random noise limits. This has been verified in both simulation and experiments, as presented herein.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".