Efficient distributed multi-robot localization: A target tracking inspired design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main reported solutions for the problem of multi-robot relative localization require synchronous communication between robots, where the network should communicate each time a relative measurement is logged in the team. This paper proposes a localization method, which can accommodate communication at a low predefined rate rather than forcing communication each time a measurement is logged. This is achieved without explicitly accumulating past measurements locally at each robot. This capability is necessary to support increasing number of robots in a team, under finite communication and computation resources. The design includes a novel fusion strategy, a consistent estimation method, and a state based initialization method, embedded in a distributed target tracking framework. The design is efficient in terms of computation demand, since it scales linearly with the number of robots. Additionally, the design is efficient in terms of communication demand, since communication is neither required to be synchronized with sensor readings, nor constrained to a specific network topology. The paper validates the proposed approach for its initialization capability, consistency of estimates, and robustness of performance, through several numerical simulations and using a publicly available multi-robot data set.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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