基于Xscale与FPGA的微小型飞行器控制系统的硬件设计

以微小型飞行器为控制对象,设计了一种基于Xscale FPGA的双芯片微小型数字控制系统.该系统用基于Xscale架构的微处理器处理导航算法和控制算法,用FPGA处理外部信号核心.选择嵌入式Linux作为软件平台,完成了Bootloader设计、嵌入式Linux的裁减和主要器件FPGA的驱动设计.针对FPGA所需处理的信号,设计了用于A/D采样的硬件电路,采用硬件描述语言对电路模块进行了软件设计.实验测试结果表明,该系统具有较高的集成度和较好的实时性.     

采用多重启发蚁群优化算法的无人机航迹规划

为解决复杂环境下的无人机航迹规划问题,提出了一种多重启发蚁群优化算法.该算法综合考虑无人机当前位置与待选位置之间的距离和威胁分布,以及待选位置与目标位置之间的距离和威胁分布,将这些已知信息构造为蚂蚁状态转移的多重启发信息,指导蚂蚁的搜索行为.文中对多重启发蚁群优化算法的收敛性进行了分析,并针对航迹不可行和任务区域内存在...     

无人机与车辆协助下的分布式多任务边缘计算卸载算法

为满足密集分布用户的服务质量和体验质量,建立了车辆和无人机协助下的多接入边缘计算卸载模型,车辆、无人机和路边单元作为边缘节点,将不同的计算任务按需卸载到不同类型的边缘节点进行处理.设计了基于分布式匹配-贪婪算法的终端用户任务卸载方案以最小化系统功耗,并研究了用户数量和延迟容忍对卸载方案的影响.仿真结果表明:该方案在满足...     

Scale Enhancement Pyramid Network for Small Object Detection from UAV Images

Object detection is challenging in large-scale images captured by unmanned aerial vehicles (UAVs), especially when detecting small objects with significant scale variation. Most solutions employ the fusion of different scale features by building multi-scale feature pyramids to ensure that the detail and semantic information are abundant. Although feature fusion benefits object detection, it still requires the long-range dependencies information necessary for small objects with significant scale variation detection. We propose a simple yet effective scale enhancement pyramid network (SEPNet) to address these problems. A SEPNet consists of a context enhancement module (CEM) and feature alignment module (FAM). Technically, the CEM combines multi-scale atrous convolution and multi-branch grouped convolution to model global relationships. Additionally, it enhances object feature representation, preventing features with lost spatial information from flowing into the feature pyramid network (FPN). The FAM adaptively learns offsets of pixels to preserve feature consistency. The FAM aims to adjust the location of sampling points in the convolutional kernel, effectively alleviating information conflict caused by the fusion of adjacent features. Results indicate that the SEPNet achieves an AP score of 18.9% on VisDrone, which is 7.1% higher than the AP score of state-of-the-art detectors RetinaNet achieves an AP score of 81.5% on PASCAL VOC.     

Aerial Oxygen-Driven Selenocyclization of O-Vinylanilides Mediated by Coupled Fe3+/Fe2+ and I2/I− Redox Cycles

In the past decade, selenocyclization has been extensively exploited for the preparation of a wide range of selenylated heterocycles with versatile activities. Previously, selenium electrophile-based and FeCl₃-promoted methods were employed for the synthesis of selenylated benzoxazines. However, these methods are limited by starting material availability and low atomic economy, respectively. Inspired by the recent catalytic selenocyclization approaches based on distinctive pathways, we rationally constructed an efficient and greener double-redox catalytic system for the access to diverse selenylated benzoxazines. The coupling of I₂/I⁻ and Fe³⁺/Fe²⁺ catalytic redox cycles enables aerial O₂ to act as the driving force to promote the selenocyclization. Control and test redox experiments confirmed the roles of each component in the catalytic system, and a PhSeI-based pathway is proposed for the selenocyclization process.     

自动空中加油技术研究进展与关键问题

自动空中加油是空中加油的高级实现形式,其应用可极大拓展无人驾驶飞行器的效能,近几年正成为国外航空领域新兴的研究内容之一。在分析自动空中加油特点的基础上,对现有的两种空中加油方式作了比较。根据国外有关自动空中加油研究的最新进展,从传感器选择、加油状态下的控制器设计、仿真与测试3方面详细论述了其基本原理与技术问题,指出了在研究自动空中加油技术时,所需解决的传感器精度、会合对接策略、协同操作、命令与通信、导航算法等关键技术问题。最后针对其研究现状与现有技术能力,指明了近期可初步开发利用并有望实现的技术。     

多机协同自主任务规划系统研究综述

针对多机协同自主任务规划显著提升无人机任务感知效能、提高任务执行能力的现状,对其关键技术进行阐述,主要包括知识表示、通感一体化、任务调度及航迹规划等技术。针对知识表示技术,阐述基于任务环境上下文感知的知识库构建方法,进而对多域融合知识图谱构建、动态知识图谱更新与共享方法进行分析总结;针对通感一体化技术,分析了环境适变、灵活可扩的多机协同通感一体化架构,进而揭示多机协同通感一体化理论能限,阐述面向任务差异性需求的资源共享方法;针对任务调度及航迹规划技术,阐述资源适配的多机协同自主任务调度方案,并对基于动力学模型的航迹控制策略及不完美环境下的鲁棒控制机制进行分析总结。     

基于SCMA的无人机中继辅助上行通信方案

为提高蜂窝通信系统的上行总吞吐量,在满足用户服务质量需求的前提下,提出了一种基于稀疏码多址接入(sparse code multiple access,SCMA)的无人机中继辅助上行通信方案,通过部署无人机增强网络覆盖,并利用SCMA提高系统传输性能。根据用户的分布和概率视距传输模型,通过交替优化码本、功率和无人机坐标,确定无人机的3维部署位置;进行资源优化后,根据信道状态信息,对子载波进行配对并迭代码本、功率,以获得优化的码本和功率分配方案。仿真结果表明,提出的方案能够有效提高上行吞吐量,并满足用户的服务质量需求。     

2021年无人机热点回眸

随着人工智能技术的发展以及机载任务平台性能的提升,2021年无人机技术与应用呈现新的发展态势.从无人机政策法规、自主控制、反无人机、应用领域等多方面回眸了2021年无人机的技术研究热点,分析了无人机技术领域未来的发展动向.人工智能、视觉导航等技术使无人机综合能力更加强大,也使其应用领域向更宽、更深、更综合方向拓展,尤其...     

Integration of multi access edge computing with unmanned aerial vehicles: Current techniques,open issues and research directions

During the last decade, research and development in the field of multi access edge computing (MEC) has rapidly risen to prominence. One of the factors propelling MEC’s evolution is the ability to deploy edge servers capable of providing both communication and computational services in close proximity to the mobile user terminal. MEC has been regarded as a potentially transformative technique for fifth-generation (5G) and beyond 5G (B5G) wireless communication systems, as well as a possible complement to traditional cloud computing. Additionally, unmanned aerial vehicles (UAVs) integrated with MEC will play a critical role by introducing an additional mobility based computational layer to provide more secure, efficient and faster services. UAV enabled MEC offers seamless connectivity, fulfilling the promise of 5G’s ubiquitous connectivity. Due to the enormous interest in UAV enabled MEC, there has been a tremendous increase in the number of published research articles in this domain; however, the research area still lacks a systematic study and categorization. We present a systematic literature review (SLR) on UAV enabled MEC, examining and analyzing data on the current state of the art using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To streamline our assessment, this study analyzes several research papers carefully selected through a multi-stage process satisfying the eligibility criteria defined in the paper. One of the SLR’s primary contributions is to broadly classify the research in the UAV enabled MEC domain into different categories including energy efficiency, resource allocation, security, architecture, and latency. We have identified key findings, technology, and pros and cons for the selected articles under each category. Additionally, we discuss the key open issues related to scalability and fairness, resource allocation and offloading optimization, service delivery with a focus on quality of experience (QoE) and quality of service (QoS), and standardization. Finally, we discuss several future research directions that would address the aforementioned issues and emerging use cases for UAV enabled MEC.