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Elliptical encirclement control capable of reinforcing performances for uavs around a dynamic target

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Defence Technology（防务技术） 2024年第2期32卷 104-119页

作者： Fei Zhang Xingling Shao Yi Xia Wendong Zhang National Key Laboratory for Electronic Measurement Technology School of Instrument and ElectronicsNorth University of ChinaTaiyuan030051China

Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(uavs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.

关键词： Elliptical encirclement Reinforced performances Wind perturbations uavs

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/uavs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or uavs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer uavs

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稀疏奖励下基于课程学习的无人机空战仿真

系统仿真学报 2024年第6期36卷 1452-1467页

作者：祝靖宇张宏立匡敏驰史恒朱纪洪乔直周文卿新疆大学电气工程学院新疆乌鲁木齐830000 清华大学精密仪器系北京100084 清华大学计算机科学技术系北京100084

针对传统强化学习在空战环境下探索能力差和奖励稀疏的问题,提出了一种基于课程学习的分布式近端策略优化(curriculum learning distributed proximal policy optimization,CLDPPO)强化学习算法。嵌入包含专家经验知识的奖励函数,设计... 详细信息

针对传统强化学习在空战环境下探索能力差和奖励稀疏的问题,提出了一种基于课程学习的分布式近端策略优化(curriculum learning distributed proximal policy optimization,CLDPPO)强化学习算法。嵌入包含专家经验知识的奖励函数,设计了离散化的动作空间,构建了局部观测与全局观测分离的演员评论家网络。通过为无人机制定进攻、防御以及综合课程,让无人机从基本课程由浅入深开始学习作战技能,阶段性提升无人机作战能力。实验结果表明:以课程学习方式训练的无人机能以一定的优势击败专家系统和主流强化学习算法,同时具有空战战术的自我学习能力,有效改善稀疏奖励的问题。

关键词： uavs 空战稀疏奖励课程学习分布式近端策略优化

DQN-Based Proactive Trajectory Planning of uavs in Multi-Access Edge Computing

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Computers, Materials & Continua 2023年第3期74卷 4685-4702页

作者： Adil Khan Jinling Zhang Shabeer Ahmad Saifullah Memon Babar Hayat Ahsan Rafiq School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Department of Automation Chongqing University of Posts and TelecommunicationsChongqing400065China

The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless connectivity.It also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays,Unmanned Aerial Vehicles(uavs)are a significant part of the mobile network due to their continuously growing use in various applications.For better coverage,cost-effective,and seamless service connectivity and provisioning,uavs have emerged as the best choice for telco operators.uavs can be used as flying base stations,edge servers,and relay nodes in mobile networks.On the other side,Multi-access EdgeComputing(MEC)technology also emerged in the 5G network to provide a better quality of experience(QoE)to users with different QoS requirements.However,uavs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing,path planning,optimization,QoS assurance,mobilitymanagement,etc.The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging.So,an automated Artificial Intelligence(AI)enabled QoSaware solution is needed for trajectory planning and optimization.Therefore,this work introduces a well-designed AI and MEC-enabled architecture for a uavs-assisted future network.It has an efficient Deep Reinforcement Learning(DRL)algorithm for real-time and proactive trajectory planning and optimization.It also fulfills QoS-aware service provisioning.A greedypolicy approach is used to maximize the long-term reward for serving more users withQoS.Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

关键词： Multi-access edge computing uavs trajectory planning QoS assurance reinforcement learning deep Q network

A review of rule-based collision avoidance technology for autonomous UAV

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中国科学:技术科学英文版 2023年第9期66卷 2481-2499页

作者： HU JinWen WANG Teng ZHANG HaoZhe PAN Quan ZHANG JianDong XU Zhao School of Automation Northwestern Polytechnical UniversityXi’an 710072China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China

The rapid increase in the number of unmanned aerial vehicles(uavs) poses a threat to the safety of personnel, ground facilities,and aircraft. Therefore, as the basis of UAV safety protection, requirements on collision avoidance technology are getting higher in the mean time. Although plenty of research on UAV collision avoidance systems and methods exists, there is still a lack of integrated view of the UAV safety in the current civil airspace. The reason behind this is that the collision avoidance is always treated as a simple and basic behavior of autonomous robots, wherein a UAV is considered not so much different from other unmanned moving vehicles except its dynamic model. Regulations and rules that are specific to the uavs under the background of airspace safety are not emphasized and put into consideration in the system and method design. This review paper serves as a guide for developing the UAV collision avoidance technology by summarizing the existing regulations and rules related to UAV collision avoidance, discussing the mathematical formulation of the rule-based collision avoidance methods, and testing and benchmarking those methods.

关键词： uavs review collision avoidance rule-based avoidance

Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

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Computers, Materials & Continua 2023年第5期75卷 2727-2740页

作者： Maryam Alhassan Abdul Raouf Khan Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversityAlahssa31982Kingdom of Saudi Arabia

Preserving privacy is imperative in the new unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)architecture to ensure that sensitive information is protected and kept secure throughout the communication.Simultaneously,efficiency must be considered while developing such a privacy-preserving scheme because the devices involved in these architectures are resource constrained.This study proposes a lightweight and efficient authentication scheme for theUAV-assistedMECenvironment.The proposed scheme is a hardware-based password-less authentication mechanism that is based on the fact that temporal and memory-related efficiency can be significantly improved while maintaining the data security by adopting a hardwarebased solution with a simple implementation.The proposed scheme works in four stages:system initialization,EU registration,EU authentication,and session establishment.It is implemented as a single hardware chip comprising registers and XOR gates,and it can run the entire process in one clock cycle.Consequently,the proposed scheme has significantly higher efficiency in terms of runtime and memory consumption compared to other prevalent methods in the area.Simulations are conducted to evaluate the proposed authentication algorithm.The results show that the scheme has an average execution time of 0.986 ms and consumes average memory of 34 KB.The hardware execution time is approximately 0.39 ns,which is a significantly less than the prevalent schemes,whose execution times range in milliseconds.Furthermore,the security of the proposed scheme is examined,and it is resistant to brute-force attacks.Around 1.158×10^(77) trials are required to overcome the system’s security,which is not feasible using fastest available processors.

关键词： MEC uavs drones security efficiency authentication hardware-based solution password-less authentication

Resource Management in UAV Enabled MEC Networks

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Computers, Materials & Continua 2023年第3期74卷 4847-4860页

作者： Muhammad Abrar Ziyad M.Almohaimeed Ushna Ajmal Rizwan Akram Rooha Masroor Muhammad Majid Hussain Bahauddin Zakariya University Department of Electrical EngineeringMultan60000Pakistan Department of Electrical Engineering College of EngineeringQassim UniversityBuraidah51452Saudi Arabia COMSATS University WAH Campus Islamabad47040Pakistan Department of Electrical and Electronics Engineering University of South WalesPontypirddCF371DLUK

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things(IoT)devices to nearby mobile edge servers,thereby lowering energy consumption and response time for ground mobile users or IoT devices.Integration of Unmanned Aerial Vehicles(uavs)and the mobile edge computing(MEC)server will significantly benefit small,battery-powered,and energy-constrained devices in 5G and future wireless networks.We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator(OI),the computational capacity(CC),the power consumption,the time duration,and the optimal location planning simultaneously.It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users(MUs)locally.This paper utilizes the k-means clustering algorithm,the interior point method,and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem.According to simulation results,both local and offloading schemes give optimal solution.

关键词： Mobile edge computing internet of things uavs ground mobile users

RO-SLAM: A Robust SLAM for Unmanned Aerial Vehicles in a Dynamic Environment

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Computer Systems Science & Engineering 2023年第11期47卷 2275-2291页

作者： Jingtong Peng Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai200120China

When applied to Unmanned Aerial Vehicles(uavs),existing Simultaneous Localization and Mapping(SLAM)algorithms are constrained by several factors,notably the interference of dynamic outdoor objects,the limited computing performance of uavs,and the holes caused by dynamic objects removal in the map.We proposed a new SLAM system for uavs in dynamic environments to solve these problems based on ORB-SLAM2.We have improved the Pyramid Scene Parsing Network(PSPNet)using Depthwise Separable Convolution to reduce the model parameters.We also incorporated an auxiliary loss function to supervise the hidden layer to enhance accuracy.Then we used the improved PSPNet to detect whether there is a movable object in the scene.If there is a movable object,its feature points will be removed in the tracking thread,and the removed feature points will not participate in the pose estimation of the camera.In addition,we proposed a filling method based on Generative Adversarial Networks(GANs)for the holes caused by dynamic object removal in the map,which employs a new auxiliary descriptor to assist GANs in restoring static scenes based on semantic information.The proposed system is evaluated on the TUM dataset,and the results indicate that the proposed method performs better than DynaSLAM and DS-SLAM on the TUM dataset.We experimented on the Cityscapes dataset,the improved PSPNet achieving an Intersection Over Union(IOU)of 0.812.

关键词： uavs SLAM semantic segmentation dynamic points remove GANs

A Software-in-the-Loop Implementation of Adaptive Formation Control for Fixed-Wing uavs

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1230-1239页

作者： Jun Yang Ximan Wang Simone Baldi Satish Singh Stefano Fari Systems Engineering Research Institute China State Shipbuilding CorporationBeijing 100094China Delft Center for Systems and Control (DCSC) TU Delft2628CD DelftThe Netherlands School of Mathematics Southeast University211189 NanjingChinaand also with DCSCTU Delft2628CD DelftThe Netherlands German Aerospace Center (DLR) Institute of Space SystemsD-28359 BremenGermanyand was with Politecnico di MilanoItaly and also with DCSCTU DelftThe Netherlands

This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(uavs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission(e.***.depending on the payload). Modeling and autopilot design for such autonomous fixed-wing uavs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduP ilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of uavs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.

关键词： ArduPilot adaptive formation control Fixed-wing uavs software-in-the-loop simulations