Distributed collaborative localization for a heterogeneous multi-robot system
Bibliographic record
Abstract
This paper studies the problem of collaborative localization for a heterogeneous multi-robotic system (MRS), particularly an MRS with one or more robots with accurate self-localization capabilities (leader) and several robots with little or no such capabilities (child). Finite-range sensing is one of the major limitation in the collaboration of an MRS when the child robots rely on inter-robot relative measurements (IRRM) between themselves and the leader robot for localization. This study proposes a collaborative localization (CL) scheme, which has the ability to localize child robots even when they operate beyond the field of view (FOV) of the leader robots. A distributed sensor fusion architecture is introduced in order to reduce the communication bandwidth, processing power, and memory usage requirements for the leader robot. Thus, the resulting implementation is scalable in terms of the number of robots in the team. The performance of the proposed localization scheme was evaluated in Monte Carlo simulations and a series of experiments using a team of six mobile robots. Both the experiment and the simulation results demonstrated that the proposed CL scheme is capable of establishing a localization for child robots with 1~10 cm positional accuracy and 0.01~0.1 rad orientational accuracy, even when they operate beyond the FOV of the leader robot.
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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 | 0.000 | 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 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".