AN IMPROVED ANT COLONY SYSTEM ALGORITHM FOR ROBOT PATH PLANNING AND PERFORMANCE ANALYSIS
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Bibliographic record
Abstract
An improved ant colony system (ACS) algorithm to solve the mobile robot path planning problem is presented. In the algorithm, a new heuristic operator is adopted to achieve a balance between population diversity and the convergence rate. It complements the algorithm to avoid running into the local optimum and to improve the solution quality. A heuristic path selection strategy is proposed to guide the algorithm to fast convergence. We adopt the MAKLINK graph and grids to establish the environment model, and the simulation research indicates that the proposed algorithm is effect. It can improve the solution quality and has better performance in search efficiency compared with other path planning methods. We also analyse the performance of the modified ACS algorithm and demonstrate that the novel algorithm can obtain the optimal solution for mobile robot path planning problems with faster convergence speed and better solution quality under different complex environments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it