The Impact of Airspace Discretization on the Energy Consumption of Autonomous Unmanned Aerial Vehicles (Drones)
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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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".