Semi-autonomous exploration with robot teams in urban search and rescue
Bibliographic record
Abstract
This paper presents the development of a semi-autonomous exploration approach for a rescue robot team exploring unknown urban search and rescue (USAR) environments. The approach consists of a direction-based exploration technique utilized by multiple robots to search an unknown cluttered environment. The technique uses an occupancy grid approach that uniquely considers: 1) the terrain information of an environment by classifying obstacle cells as climbable or non-climbable cells, as well as 2) the direction of approach of a robot into a cell in order to determine a robot's ability to traverse a cell of interest. A distance threshold technique is employed to determine when the robots in a team should share this information with each other to minimize exploration overlap. The performance of the direction-based semi-autonomous exploration approach was investigated and compared to autonomous exploration of the same robot teams in simulations conducted in USARSim. The results verified that there was a statistically significant increase in exploration coverage using the semi-autonomous exploration mode over the fully autonomous exploration mode. The simulations also verified the potential use of semi-autonomous exploration of a team with multiple rescue robots.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".