A Dual Calibration Framework for Exploring Environments using Heterogeneous Robot Swarms
Bibliographic record
Abstract
Exploring complex environments using heterogeneous robot swarms (RSs) is a considerable challenge in terms of coordination, sensing, and information fusion. Existing approaches suffer from a lack of systematic analysis that fully exploits the complementary capabilities of heterogeneous agents. To bridge this gap, we propose a novel spatial calibration framework that integrates both virtual and physical calibration mechanisms to enable coordinated operation between two distinct robot swarms, RS-A and RS-B. RS-A, characterized by high mobility and a broad field of view, performs continuous, large-scale monitoring and identifies candidate regions of interest. RS-B, equipped with high-precision sensors, is dispatched to these regions to conduct fine-grained data collection and return accurate environmental information, facilitating comprehensive environmental mapping. To this end, we develop a distributed control method for spatial partitioning, position optimization, and information exchange within the swarm, based on improved coverage control and a flooding-based broadcast algorithm for intra-swarm communication. We further design a control architecture that enables inter-swarm collaboration. The proposed framework effectively addresses the limitations of homogeneous RSs in environmental exploration by integrating fast, coarse-grained surveillance with slow, fine-grained investigation through heterogeneous coordination. Finally, the effectiveness of our proposed framework is validated through simulation results.
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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".