Unknown Maze Map with Unknown Coordinates Exploration Through HPHS and CvaR Framework
Bibliographic record
Abstract
This research focuses on the problem of robots searching for exits with unknown coordinates in a completely unknown environment. An autonomous exploration framework integrating hierarchical division and risk assessment is proposed to deal with the problem. Firstly, the system utilises depth-first search (DFS) in combination with a Visited Map to construct an initial feasible one path. Subsequently, environmental perception and map updating are realised through Occupancy Map - SLAM. At the high-level stage, the HPHS framework is introduced to partition the global environment into coarse-grained regions. Potential exploration targets are screened according to regional accessibility and frontier density. At the local level, frontier clustering is employed to generate candidate points. Simultaneously, the Conditional Value at Risk (CVaR) model is adopted for risk-sensitive selection. This is intended to enhance the robustness and efficiency of exploration. The experimental results demonstrate that this method can effectively avoid local dilemmas in dense unknown mazes while maintaining exploration coherence. Additionally, it surpasses traditional strategies based on A* or simple frontier search in aspects such as coverage rate, path rationality, and convergence speed. This proposed scheme offers a novel solution idea for autonomous exploration in complex, unknown environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".