CPP-OTPO: A Cognitive Multi-UAV System for Adaptive Path and Trajectory Optimization
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) have significantly transformed various Search and Rescue (SAR) operations. Effective resource deployment is still difficult in modern SAR missions, especially in complex and unexpected circumstances. Conventional techniques for multi-UAV systems for SAR operations frequently have difficulties responding to changing conditions. This research proposes a unique method of combining 6G communication technology and Deep Reinforcement Learning (DRL) to improve the cognitive capacities of multi-UAV systems for cooperative coverage to enhance autonomous decision-making in challenging environments. The proposed Coverage Path Planning-Online Time-based Policy Optimization (CPP-OTPO) system utilizes DRL to imbue a multi-UAV framework capable of enabling adaptive decision-making based on realtime observations. The integration of 6G communication further enhances connectivity among multi-UAV systems. The system incorporates boundary search techniques in UAV path planning to achieve optimized CPP, emphasizing resilience against navigational uncertainties. Additionally, the proposed OTPO improves the effectiveness of the target search and increases the possibility of detecting targets by guiding UAVs to change their search policies from exploiting to exploring when required. Empirical evaluations demonstrate a remarkable 92.2% operational time efficiency compared to traditional algorithms.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".