The convergence of drone delivery systems, virtual worlds, and blockchain has transformed logistics and supply chain management, providing a fast, and environmentally friendly alternative to traditional ground transportation methods;Provide users with a real-world experience, virtual service providers need to collect up-to-the-minute delivery information from edge devices. To address this challenge, 1) a reinforcement learning approach is introduced to enable drones with fast training capabilities and the ability to autonomously adapt to new virtual scenarios for effective resource allocation.2) A semantic communication framework for meta-universes is proposed, which utilizes the extraction of semantic information to reduce the communication cost and incentivize the transmission of information for meta-universe services.3) In order to ensure that user information security, a lightweight authentication and key agreement scheme is designed between the drone and the user by introducing blockchain technology. In our experiments, the drone adaptation performance is improved by about 35\%, and the local offloading rate can reach 90\% with the increase of the number of base stations. The semantic communication system proposed in this paper is compared with the Cross Entropy baseline model. Introducing blockchain technology the throughput of the transaction is maintained at a stable value with different number of drones.
Perception-aware Exploration for Consumer-grade UAVs
Autonomous Intelligent Systems group, Computer Science Institute VI – Intelligent Systems and Robotics, University of Bonn（波恩大学计算机科学研究所VI – 智能系统与机器人学自主智能系统组）
Perception-aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments
University of Toronto（多伦多大学）
基于标签的视觉惯性定位是一种轻量级的方法，用于实现低成本无人机（UAV）在室内施工环境中的自主数据汇集任务。然而，找到最佳标签配置（即，数量、规模和位置）仍然具有挑战性。本文提出了一种基于感知遗传算法的标签布局规划器（PGA-TaPP），以确定最佳的标签配置使用4D-BIM，考虑项目进度，安全要求，和无人机的本地化。所提出的方法提供了一个4D的标签放置计划，通过最大限度地提高在用户指定的感兴趣区域（ROI）的本地化，同时限制安装成本。可定位性使用Fisher信息矩阵（Fisher information matrix）进行量化，并封装在可导航的网格中。实验结果表明，我们的方法在寻找一个最佳的4D标签放置计划的鲁棒定位的无人机在建设中的室内场地的有效性。
HEPP: Hyper-efficient Perception and Planning for High-speed Obstacle Avoidance of UAVs
Department of Mechanical Engineering, The University of Hong Kong（机械工程系，香港大学）；Northwestern Polytechnical University（西北工业大学）；Harbin Institute of Technology（哈尔滨工业大学）
Learning Transferable UAV for Forest Visual Perception
Beijing National Research Center for Information Science and Technology (BNRist)（北京国家信息科学技术研究中心（BNRist））；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systems
College of Informatics, Harbin Institute of Technology（信息学院，哈尔滨工业大学）；Key Laboratory of Forest and Grassland Fire Risk Prevention, Ministry of Emergency Management, China Fire and Rescue Institute（森林和草原火灾风险预防重点实验室，应急管理部，中国消防救援学院）；Southern University of Science and Technology（南方科技大学）
Terrain Perception for Agricultural UAVs in Complex Farmland via Rotating mmWave Radar
School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机科学学院）；TopXGun Robotics（TopXGun机器人）；CARDC；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子信息工程学院）
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西大学智能组网与场景系统重点实验室（桂林电子科技大学信息与通信学院））；Research Institute of Highway Ministry of Transport（交通运输部公路科学研究院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Architecture and Transportation Engineering, Guilin University of Electronic Technology（桂林电子科技大学建筑与交通工程学院）
Is Intermediate Fusion All You Need for UAV-based Collaborative Perception?
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所认知与决策智能复杂系统重点实验室）
An Event-based Fast Intensity Reconstruction Scheme for UAV Real-time Perception
Hangzhou International Innovation Institute, Beihang University（杭州国际创新院，北京航空航天大学）；School of Aeronautic Science and Engineering, Beihang University（航空科学与工程学院，北京航空航天大学）；Institute of Unmanned System, Beihang University（无人系统研究院，北京航空航天大学）；Tianmushan Laboratory, Xixi Octagon, Yuhang, Hangzhou（天幕实验室，西溪圆，余杭，杭州）
UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarios
Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University（计算机与网络工程系，信息科技学院，阿联酋大学）；Khalifa University of Science and Technology（科技大学）
An Aerial Manipulator for Perception-Driven Flower Targeting Toward Contactless Pollination in Vertical Farming
Department of Computer Science University College London London United Kingdom（计算机科学系伦敦大学学院伦敦英国）；Department of Artificial Intelligence University of Malaya Kuala Lumpur Malaysia（人工智能系马来大学吉隆坡马来西亚）；University College London（伦敦大学学院）；University of Malaya（马来大学）
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
CISTER - Research Centre in Real-Time and Embedded Computing Systems（CISTER实时与嵌入式计算系统研究中心）；The Hong Kong Polytechnic University（香港理工大学）；Commonwealth Scientific and Industrial Research Organization (CSIRO)（联邦科学与工业研究组织（CSIRO））；University of Manitoba（曼尼托巴大学）
Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networks
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
