Multiple Waypoint Path Planning for a Mobile Robot using Genetic Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
This investigation developed a MATLAB program, based on genetic algorithms that generated an optimal (shortest distance) path plan for a mobile robot to visit all of the specified waypoints without colliding with the known obstacles. The designed genetic algorithm path planner was shown to accomplish this task and produce superior results when compared against a full search path planner. Next, it was shown that the choice of search parameters for the genetic algorithm effected the time to execute the search and the quality of the solution (length of the chosen path). Having proven the genetic algorithm path planner in simulation, the genetic algorithm path planner then successfully guided an actual X80 mobile robot to all its waypoints without colliding with any obstacles in a test environment
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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 | 0.001 | 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 it