A Soft Obstacle Search Strategy for Various Solitary Robots in an Unknown Environment
Bibliographic record
Abstract
Abstract The problem of coordination without a priori informationabout the environment is important in robotics. Applications vary fromformation control to search and rescue. This paper considers the problemof search by a group of solitary robots: self-interested robots without apriori knowledge about each other, and with restricted communicationcapacity. When the capacity of robots to communicate is limited, theymay obliviously search in overlapping regions (i.e. be subject to inter-ference). Interference hinders robot progress, and strategies have beenproposed in the literature to mitigate interference (Wellman et al., 2011;Hourani et al., 2013). Interaction of solitary robots has attracted muchinterest in robotics, but the problem of mitigating interference when timefor search is limited remains an important area of research. We proposea coordination strategy based on the method of cellular decomposition(Choset, 2001) where we employ the concept of soft obstacles: a robotconsiders cells assigned to other robots as obstacles. The performance ofthe proposed strategy is demonstrated by means of simulation experi-ments in both actual and simulated environments. Simulations indicatethe utility of the strategy in situations where a known upper bound onthe search time precludes search of the entire environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".