Energy-Efficient Multi-UAV Navigation for Cooperative Data Sensing and Transmission
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) hold significant potential for sensing services in a large scope of area, thanks to their wide coverage and adaptable deployment. Considering the complex environment dynamics and limited sensing range, navigating multiple UAVs in a distributed way becomes challenging to implement cooperative data sensing and transmission tasks. In this paper, we optimize the trajectory design of UAVs by jointly considering the collected data volume, geographical fairness and limited energy reserve during their service period. To achieve the long-term serving objective, a memory augmented multi-agent deep reinforcement learning approach is presented to ensure energy-efficient distributed trajectory design with partial observations. Specifically, the intrinsic criterion is developed to enhance UAV spatial exploration when reaching the boundary of explored regions. Then, to address the information loss caused by incomplete observations, the spatial-temporal memory augmented actor-critic architecture is designed to extract historical contextual features for multi-UAV cooperative navigation. Furthermore, the prioritized experience replay mechanism is incorporated to enhance important experience exploitation for UAV collaboration. Extensive simulations using two real-world datasets in Shenzhen and Beijing demonstrate that the proposed method outperforms the state-of-the-art methods in terms of data collection ratio, geographical fairness, and energy consumption ratio.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".