OPTIMASI PENCAPAIAN TARGET PADA SIMULASI PERENCANAAN JALUR ROBOT BERGERAK DI LINGKUNGAN DINAMIS
Bibliographic record
Abstract
Robot bergerak harus memiliki perencanaan jalur yang baik ketika menjalankan navigasi di lingkungan. Penelitian yang terakhir menangani permasalahan simulasi perencanaan jalur di lingkungan dengan kehadiran rintangan dan target yang bergerak. Teknik penghindaran rintangan dalam penelitian tersebut cukup baik namun jalur pencapaian target masih belum optimal. Penelitian ini memperbaiki algoritma Q-learning pada penelitian sebelumnya dengan menggunakan konsep Ant Colony. Pendekatan metode yang dilakukan bertujuan untuk mengoptimalkan pencapaian target. Prediksi pergerakan rintangan dan target juga digunakan untuk meningkatkan efektifitas pencapaian target. Hasil evaluasi uji coba berdasarkan jumlah skenario pelatihan, menunjukkan angka kegagalan metode yang diusulkan lebih kecil 22% dibandingkan metode sebelumnya. Sedangkan berdasarkan jumlah rintangan yang digunakan, angka kegagalan metode yang diusulkan lebih kecil 11% daripada metode sebelumnya. Hasil evaluasi waktu pencapaian target menunjukkan metode yang diusulkan rata-rata mempu mencapai target lebih lama 0,9 detik dari metode sebelumnya. Metode yang diusulkan mampu mencapai target lebih cepat pada pola pergerakan target yang linear.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".