A Multi-Objective Approach for Unmanned Aerial Vehicle Mapping
Bibliographic record
Abstract
Many commercial applications require aerial mapping with multiple UAVs. Mapping is a mission planning problem which requires meeting a set of constraints while optimizing key factors that may conflict with each other, such as fuel/battery consumption, make-span, and the associated risks. Solving this Multi-Objective Optimization (MOO) will therefore result in a set of trade-offs (Pareto optimal solutions) that will be supplied to a decision-maker. Given that the Pareto set can be of a very large size, we propose a Multi-criteria Decision Making (MCDM) system that relies on user’s preferences to bring down this set to a manageable size. More precisely, the proposed system captures user’s qualitative preferences and uses them through the Fuzzy Vikor to filter and rank Pareto optimal solutions. The designed system is able to work with both or either fixed-wing and multi-rotor UAVs. To evaluate the performance of our system, we conducted a set of experimental simulations considering several scenarios. The findings show that fixed-wing UAVs have higher energy consumption and mission time than multi-rotors due to Dubin’s turns, assuming both types have the same charging/fueling endurance and the same velocity. Lastly, it is found that heterogeneity will not always lead to a better mission duration than homogeneous UAV fleets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".