Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the problem of building an occupancy grid map of an unknown environment using a swarm comprising resource-constrained robots, i.e., robots with limited exteroceptive and inter-robot sensing capabilities. Past approaches have, commonly, used random-motion models to disperse the swarm and explore the environment randomly, which do not necessarily consider prior information already contained in the map. Herein, we present a collaborative, effective exploration strategy that directs the swarm toward ‘promising’ frontiers by dividing the swarm into two teams: landmark robots and mapper robots, respectively. The former direct the latter, toward promising frontiers, to collect proximity measurements to be incorporated into the map. The positions of the landmark robots are optimized to maximize new information added to the map while also adhering to connectivity constraints. The proposed strategy is novel as it decouples the problem of directing the resource-constrained swarm from the problem of mapping to build an occupancy grid map. The performance of the proposed strategy was validated through extensive simulated experiments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it