The paper addresses lifelong aerial autonomy by treating visual place recognition (VPR) as a mission‑based domain‑incremental learning problem. It introduces a heterogeneous memory framework that first trains on a static satellite exemplar memory and then uses a bounded replay buffer to retain selected airborne observations across missions. The proposed DBS‑Hybrid replay strategy, which blends prototype‑based diversity trimming with representative‑first feature‑space coverage, outperforms baseline methods in accuracy, generalization, and knowledge retention across multiple UAV missions.
By Xingyu Shao, Zhiqiang Yan, Liangzheng Sun, Mengfan He, Chao Chen, Jinhui Zhang, Chunyu Li, Ziyang Meng
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.
By Xiucheng Wang, Zhenye Chen, Nan Cheng, Zhisheng Yin, Xuemin Shen
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
The paper discusses how Diffusion Models (DMs) can improve decision-making and digital modeling for Uncrewed Aerial Vehicles (UAVs). It highlights the limitations of Reinforcement Learning (RL) and Digital Twin (DT) approaches, noting that DMs learn underlying probability distributions and generate realistic patterns, thereby addressing data scarcity and enhancing modeling accuracy. Simulation results demonstrate DMs’ effectiveness in estimating neighbor velocities for a four‑UAV swarm coordination task using Deep Reinforcement Learning.
By Yousef Emami, Hao Zhou, Luis Almeida, Kai Li
The paper introduces WONDER, a radio world‑model‑based negotiation framework designed to optimize multi‑UAV coverage for rapid post‑disaster wireless restoration. WONDER employs a Joint‑Embedding Predictive Architecture to forecast the incremental radio impact of candidate UAV trajectories and uses multi‑round negotiation to sequentially commit trajectories while updating the context. Experiments in the RadioDynamics simulation environment demonstrate that WONDER outperforms six other methods, achieving a balanced score of 0.870 and a 0.162 coverage advantage over STACCA while preserving full UAV connectivity.
By Jiahao Huang, Rongpeng Li, Zhifeng Zhao, Guoru Ding, Honggang Zhang
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