Pairwise observable relative localization in ground aerial multi-robot networks
Bibliographic record
Abstract
This paper addresses the problem of relative localization in a team of robots which consists both ground and aerial platforms. The robots are equipped with sensors for measuring both range and bearing of neighboring team members. Pairwise observability in such a team refers to the ability of two robots to estimate their relative poses, without strictly relying on information or measurements of other team members. This capability is important to realize many robotic behaviors such as sense and avoidance, formation control, and leader follower supervisory control, in a robust and minimally dependent manner. This paper presents an implementation and an analysis of a multi-robot relative localization network. In order to identify the necessary conditions for achieving pairwise observability, the study performs a nonlinear observability analysis. The analysis considers the cases where input velocities of the measured platforms are unknown, which is relevant to most drifting aerial platforms facing communication constraints and sensing limitations. The results of the analysis are experimentally demonstrated, along with the implications of the observability study in designing multi-robot teams and estimation frameworks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".