Deep Reinforcement Learning for UAV Wireless Charging and Trajectory Planning: A Review
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) have become essential for various applications, including surveillance, logistics, and disaster response. However, their limited battery capacity and frequent need for recharging disrupt mission efficiency, especially in critical operations. Dynamic Wireless Charging (DWC), which provides in-flight charging autonomously, offers a promising solution to extend operational time. Deep Reinforcement Learning (DRL) enhances this approach by optimizing energy management, charging strategies, and trajectory planning based on real-time conditions. This paper explores the integration of DRL, DWC, and emerging technologies like 6G communications and IoT, aiming to improve UAV energy efficiency and mission reliability. It reviews current research on DRL for UAV charging and trajectory planning, discusses limitations in existing studies, and proposes future research directions, including the integration of laser beaming and hybrid DRL models. The goal is to create more autonomous, sustainable, and efficient UAV systems for diverse applications in intelligent transportation systems (ITS).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".