ARTIFICIAL IMMUNE NETWORK-BASED MULTI-ROBOT FORMATION PATH PLANNING WITH OBSTACLE AVOIDANCE
Why this work is in the frame
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Bibliographic record
Abstract
Artificial immune network algorithm (AINA) combined with position tracking control method is used for multi-robot formation path planning. The proposed algorithm avoids obstacles and recovers formation for follower robot after passing around obstacles. Different methods are adopted to calculate the steering direction and the linear velocity of the follower robot. Steering direction of the follower robot is computed with AINA. AINA has abilities of selfrecognition and diversity, and solves the problems of local minima and immature convergence. The optimal steering direction selected with AINA quickly tends towards the steering direction of leader robot, and successfully avoids obstacles. The linear velocity of follower robot is computed with position tracking control method.
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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 | 0.001 |
| 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