A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments
Why this work is in the frame
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Bibliographic record
Abstract
This article presents a novel knowledge-based genetic algorithm (GA) to generate a collision-free path in complex environments. The proposed algorithm infuses specific domain knowledge into robot path planning through the development of five problem-specific operators that integrate a local search technique to improve efficiency. In addition, the proposed algorithm introduces a unique and straightforward representation of the robot path and an effective method for evaluating the path quality and accurately detecting collisions. The proposed algorithm is capable of finding optimal or suboptimal robot paths in both static and dynamic environments. Simulation and experimental studies are conducted to showcase the effectiveness and efficiency of the proposed algorithm. Furthermore, a comparative study is performed to highlight the indispensable role of specialized genetic operators within the proposed algorithm in solving the path planning problem.
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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 | 0.001 | 0.001 |
| 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