Semi-autonomous exploration with robot teams in urban search and rescue
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it