Path Efficiency Enhanced Exploration Method for Mobile Robot in Unknown Unstructured Environments
Bibliographic record
Abstract
Autonomous exploration of unknown environments is one of the fundamental capabilities of intelligent mobile robots, which affects their adaptability in complex environment navigation tasks. Traditional frontier-based exploration methods prioritize exploration coverage while neglecting path cost, whereas some novel machine learning-based approaches often require substantial training and tuning to maintain effectiveness. To address this issue, this paper integrates the frontier-based method with artificial potential fields to develop a new local navigation approach, enabling the robot to explore a local area while avoiding obstacles. Subsequently, the newly proposed global exploration strategy can effectively guide the robot to explore different local areas in an orderly manner by selecting the nearest area with exploration value, which is calculated based on the principle of minimum potential energy. Simulations and experiments demonstrate that in unstructured scenarios with irregular layouts, the proposed method ensures good exploration coverage while significantly reducing path cost compared to existing methods, thereby improving the efficiency of the robot in the exploration task.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".