The Impact of Airspace Discretization on the Energy Consumption of Autonomous Unmanned Aerial Vehicles (Drones)
Why this work is in the frame
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Bibliographic record
Abstract
Promising massive emissions reduction and energy savings, the utilization of autonomous unmanned aerial vehicles (UAVs) in last-mile parcel delivery is continuously expanding. However, the limited UAV range deters their widescale adoption to replace ground modes of transportation. Moreover, real-world data on the impact of different parameters on the operation, emissions, and energy consumption is scarce. This study aims to assess the impact of airspace planning and discretization on the energy consumption of autonomous UAVs. We utilize a novel open-source comprehensive UAV autonomous programming framework and a digital-twin model to simulate real-world three-dimensional operation. The framework integrates airspace policies, UAV kinematics, and autonomy to accurately estimate the operational energy consumption via an experimentally verified energy model. In the simulated case study, airspace is discretized by both a traditional Cartesian method and a novel dynamic 4D discretization (Skyroutes) method. This allows for the comparison of different routing and trajectory planning algorithms for ten missions. The results show a variation in the energy consumption by up to 50%, which demonstrates the criticality of airspace discretization and planning on UAV charging infrastructure design, greenhouse gas emissions reduction, and airspace management.
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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 | 0.000 | 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