Path planning for orchard mobile robots based on an improved ant colony algorithm and the dynamic window approach
Bibliographic record
Abstract
To address the challenges of insufficient navigation accuracy, low real-time performance, non-smooth paths, and excessive turning points in orchard mobile robots, this study proposes an integrated path planning method combining an improved Ant Colony Optimization (ACO) algorithm and the Dynamic Window Approach (DWA), supported by LiDAR-based Simultaneous Localization and Mapping (SLAM) for map construction. Firstly, the heuristic function, pheromone update strategy, and redundant node removal are optimized to enhance the efficiency of global path planning. Subsequently, the improved ACO is integrated with DWA to achieve local dynamic obstacle avoidance and further improve path smoothness. Finally, comparative experiments are conducted in both static and dynamic orchard environments among the Sparrow Search Algorithm (SSA), the A* algorithm, conventional ACO, and the proposed approach. Simulation results demonstrate that the proposed method reduces the number of turning points and path length by up to 4.29%, and decreases runtime by up to 12.95%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".