arXiv Machine Learning
FLoRa: Flight-Assisted Data Collection from Duty Cycling LoRa Nodes under Energy Constraints
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arXiv AI
Aug 18
An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing
arXiv:2605. 13221v2 Announce Type: replace Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC).
arXiv AI
Aug 18
LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset
arXiv:2608. 15175v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous navigation in complex outdoor environments, where dynamic conditions and mission requirements require intelligent adaptive decision-making.
arXiv AI
3d ago
From Comparison to Selection: Enhancing Cross-Scale Generalization of Pointer Networks Trained with Reinforcement Learning for Capacitated Drone Delivery Routing
arXiv:2610.27936v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) enable fast, flexible last-mile and emergency delivery, but their routing remains challenging as the number of clients...
arXiv Machine Learning
1d ago
Adaptive Deadline- and Energy-Aware Caching Algorithms in Dynamic Multi-UAV Edge Networks
The paper proposes two caching strategies for multi‑UAV edge networks that jointly consider content deadlines, UAV communication energy, and energy imbalance. The first, DE‑UBAC, uses a rule‑based hierarchical approach with deadline prioritization and cooperative placement, but its fixed thresholds limit adaptability. The second, GRU‑DEPPO, integrates GRU‑based temporal representations with a centralized PPO actor‑critic, achieving a 61% lower deadline‑miss rate and 40% reduced energy imbalance compared to DE‑UBAC.
arXiv Machine Learning
Aug 21
Learn for Variation: Efficient AAV Trajectory Learning through a Differentiable Wireless World Model
arXiv:2603. 18853v3 Announce Type: replace-cross Abstract: Autonomous aerial vehicles (AAVs) enable data collection for sixth-generation Internet-of-Things networks, but their trajectories couple nonlinear wireless rates with long-horizon service progress.
arXiv AI
Jun 19
Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning
arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.