Mobile robots exploration strategies and requirements: A systematic mapping study
Bibliographic record
Abstract
A variety of autonomous exploration tasks have been successfully performed in several types of environments using different types of robotic platforms. The robotic task, the operational environment, and the robot embodiment represent the dimensions of the “problem space” in robot exploration. At the same time, a lot of exploration strategies are documented in the literature that provide partial solutions to the exploration problem. They define the “solution space” in robot exploration. To our knowledge, no previous work has provided a methodical overview of robot exploration strategies from the point of view of both the problem and solution spaces. In this systematic mapping study, we build a taxonomy of autonomous robot exploration strategies and application requirements and classify existing approaches according to it. The goal is to analyze research trends over time, and identify possible research gaps, open challenges, and promising future directions in order to support researchers and practitioners in generalizing, communicating, and applying the findings of the robot exploration knowledge field.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".