Efficient Mapping and Navigation System for Weed Removal Robot in Confined Garden Spaces
Bibliographic record
Abstract
Autonomous navigation in confined and unstructured environments remains a core challenge for mobile robots. In dynamic outdoor spaces such as gardens, GPS signals can be unreliable, and traditional simultaneous localization and mapping (SLAM) systems struggle with occlusions and terrain variability. This paper presents an efficient mapping and navigation system integrated with an enhanced A* path-planning algorithm for weed removal robot in confined garden spaces. Unlike conventional systems requiring manual pre-mapping, the proposed approach can construct real-time maps while dynamically adjusting paths for obstacle avoidance and optimized coverage. The integration of adaptive Monte Carlo Localization (AMCL) and real-time LiDAR feedback ensures robust navigation in dynamic and unstructured environments. The experiment test results demonstrate that the proposed system achieves enhanced mapping accuracy, reduced travel distance, and improved localization precision and outperforms the standard LiDAR-SLAM approaches. These findings highlight the system's potential to advance real-time autonomous navigation for outdoor mobile robotics, particularly in agricultural and autonomous gardening applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".