The paper introduces a three‑step framework for deploying multi‑UAV networks in threat‑prone environments. First, a threat‑aware K‑means algorithm determines the minimal number of UAVs and safe initial positions. Second, an optimal matching stage assigns UAVs to these positions to reduce energy use. Third, a threat‑aware multi‑agent twin delayed deep deterministic policy gradient (MATD3) algorithm dynamically optimizes UAV trajectories, power, and user associations, achieving zero safety violations and superior energy efficiency compared to other learning methods and heuristic baselines.
By Faisal Al-Kamali, Hussein A. Ammar, Francois Chan, James H. Bayes, Yasser Gadallah, Mohamed H. Ahmed
arXiv:2608. 04590v1 Announce Type: new Abstract: The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity.
By Xiao Wang, Shun-Ren Yang
AERIS is an offline policy improvement framework for multi-UAV integrated sensing and communication (ISAC) that learns from fixed flight logs using centralized training and decentralized execution. It introduces STAR-CRDT, an offline multi-agent RL algorithm that rectifies local actions and distills trusted improvements into decentralized actors, providing an offline-support policy improvement guarantee. Experiments demonstrate that STAR-CRDT boosts the main ISAC objective return by 29.3% and improves communication sum rate, sensing pass rate, and sensing margin while reducing collision-risk events by 54.2%.
By Ziyuan Wang (Steven), Yifan Sui (Steven), Wei Wei (Steven), Wenjie Xin (Steven), Zekai Zhang (Steven), Xiangwang Hou (Steven), Xiao-Ping (Steven), Zhang
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.
By Jian Kim, Ki-Il Kim
The paper introduces a hierarchical hybrid architecture combining large language models (LLMs) and multi-agent reinforcement learning (MARL) to manage heterogeneous unmanned aerial systems in low‑altitude wireless networks (LAWNs). An outer LLM‑driven loop interprets service requirements and operator intent to reconfigure objectives and resource priorities, while an inner MARL loop executes decentralized policies under the updated game. A logistics‑monitoring case study demonstrates the framework’s ability to coordinate diverse services and adapt to changing conditions without retraining the MARL policies.
By Nguyen Duc Minh Quang, Chang Liu, Shuangyang Li, Derrick Wing Kwan Ng
arXiv:2606. 01478v1 Announce Type: cross Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms.
By Martin Schuck, Marcel P. Rath, Yufei Hua, AbhisheK Goudar, SiQi Zhou, Angela P. Schoellig
arXiv:2606. 31073v1 Announce Type: new Abstract: Large language models (LLMs) provide a promising interface for high-level robotic task planning, but their use in multi-UAV collaboration remains difficult to evaluate systematically.
By Sheng Zhang, Qinglin Li, Yuechao Zang, Xueqin Huang, Yijia Fu, Cheng Zhu
arXiv:2608. 01128v1 Announce Type: cross Abstract: In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.
By Yixin Zhang, Zhuohui Yao, Wenchi Cheng, Walid Saad
arXiv:2606. 14219v1 Announce Type: cross Abstract: Agentic AI can support unmanned aerial vehicle (UAV) autonomy by providing high-level recovery reasoning when local waypoint- or setpoint-based execution encounters blocked passages, repeated no-progress behavior, or mission-level ambiguity.
By Taewoo Park, Kyeonghyun Yoo, Seunghyun Yoo, Hwangnam Kim
The paper introduces a Physics‑Informed Multi‑Agent Coordination framework that embeds calibrated BCMP queueing topologies into a decentralized multi‑agent reinforcement learning system for hospital patient flow. It formulates the problem as a Decentralized Partially Observable Markov Decision Process with coupled resource constraints, enabling departmental agents to negotiate patient routing and service scaling while exchanging localized action fingerprints to handle non‑stationarity. Empirical tests on MIMIC‑IV data show the approach reduces cumulative system delay compared to static Markovian models, heuristic dispatching, and independent multi‑agent baselines, all while preserving clinical safety constraints.
By Guoqing Zhang, Rafik Hadfi, Takayuki Ito
arXiv:2607. 20547v1 Announce Type: new Abstract: Safe Advanced Air Mobility operations require aircraft to maintain separation when surveillance information is noisy, delayed, incomplete, or temporarily unavailable.
By Esrat Farhana Dulia, Syed Arbab Mohd Shihab, Caleb Adams, Ruben Del Rosario
arXiv:2610.09226v1 Announce Type: cross
Abstract: Data collection using Unmanned Aerial Vehicles (UAVs) is challenging when LoRa IoT Devices (IoTDs) duty-cycle to conserve battery. Under energy const...
By Naresh Babu Kakarla, V. Mahendran