Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

Unmanned aerial vehicle based intelligent triage system in mass-casualty incidents using 5G and artificial intelligence

BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.

Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

Flight Control System of Unmanned Aerial Vehicle

To date unmanned aerial system(UAS)technologies have attracted more and more attention from countries in the world.Unmanned aerial vehicles(UAVs)play an important role in reconnaissance,surveillance,and target tracking within military and civil fields.Here one briefly introduces the development of UAVs,and reviews its various subsystems including autopilot,ground station,mission planning and management subsystem,navigation system and so on.Furthermore,an overview is provided for advanced design methods of UAVs control system,including the linear feedback control,adaptive and nonlinear control,and intelligent control techniques.Finally,the future of UAVs flight control techniques is forecasted.

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.

Object Tracking-Based “Follow-Me” Unmanned Aerial Vehicle (UAV) System

The applications of information technology (IT) tools and techniqueshave, over the years, simplified complex problem solving procedures. But thepower of automation is inhibited by the technicality in manning advanced equipment. To this end, tools deliberately combating this inhibition and advancing technological growth are the Unmanned Aerial Vehicles (UAVs). UAVs are rapidlytaking over major industries such as logistics, security, and cinematography.Among others, this is a very efficient way of carrying out missions unconventional to humans. An application area of this technology is the local film industrywhich is not producing quality movies primarily due to the lack of technicalknow-how in utilizing these systems. This study therefore aim to devise an autonomous object tracking UAV system that would eliminate the complex procedureinvolved in stabilizing an aerial camera (aerial bot) midair and promote the creation of quality aerial video shooting. The study adopted Unified Modeling Language (UML) tools in modeling the system’s functionality. The traditionalServer-Client model architecture was adopted. The OpenCV library employedproved highly efficient in aiding the tracking procedure. The system provided ausable web controller which provides easy interaction between the pilot and thedrone. Conclusively, investments in UAVs would enhance creation of quality graphic contents.

Intelligent Greedy Perimeter Stateless Routing Scheme for Unmanned Aerial Vehicles

The dynamic behavior,rapid mobility,abrupt changes in network topology,and numerous other flying constraints in unmanned aerial vehicle(UAV)networks make the design of a routing protocol a challenging task.The data routing for communication between UAVs faces numerous challenges,such as low link quality,data loss,and routing path failure.This work proposes greedy perimeter stateless routing(GPSR)based design and implementation of a new adaptive communication routing protocol technique for UAVs,allowing multiple UAVs to communicate more effectively with each other in a group.Close imitation of the real environment is accomplished by considering UAVs’three-dimensional(3D)mobility in the simulations.The performance of the proposed intelligent greedy perimeter stateless routing(IGPSR)scheme has been evaluated based on end-to-end(E2E)delay,network throughput,and data loss ratio.The adapted scheme displayed on average 40%better results.The scenario has been implemented holistically on the network simulator software NS-3.

Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile users.It encompasses several heterogeneous resource and communication standard in ensuring incessant availability of service.At the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,etc.In UAV networks,energy efficiency and data collection are considered the major process for high quality network communication.But these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted UAVs.These issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G environment.With this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G environment.The proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets minimized.The presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct clusters.The QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of UAVs.The performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

Energy-efficient joint UAV secure communication and 3D trajectory optimization assisted by reconfigurable intelligent surfaces in the presence of eavesdroppers

We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.

IoT-Cloud Empowered Aerial Scene Classification for Unmanned Aerial Vehicles

Recent trends in communication technologies and unmanned aerial vehicles(UAVs)find its application in several areas such as healthcare,surveillance,transportation,etc.Besides,the integration of Internet of things(IoT)with cloud computing environment offers several benefits for the UAV communication.At the same time,aerial scene classification is one of the major research areas in UAV-enabledMEC systems.In UAV aerial imagery,efficient image representation is crucial for the purpose of scene classification.The existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average results.Therefore,the current research work introduces a new DL-enabled aerial scene classificationmodel forUAV-enabledMECsystems.The presented model enables theUAVs to capture aerial imageswhich are then transmitted to MEC for further processing.Next,CapsuleNetwork(CapsNet)-based feature extraction technique is applied to derive a set of useful feature vectors from the aerial image.It is important to have an appropriate hyperparameter tuning strategy,since manual parameter tuning of DL model tend to produce several configuration errors.In order to achieve this and to determine the hyperparameters of CapsNetmodel,Shuffled Shepherd Optimization(SSO)algorithm is implemented.Finally,Backpropagation Neural Network(BPNN)classification model is applied to determine the appropriate class labels of aerial images.The performance of SSO-CapsNet model was validated against two openly-accessible datasets namely,UC Merced(UCM)Land Use dataset andWHU-RS dataset.The proposed SSO-CapsNet model outperformed the existing state-of-the-art methods and achieved maximum accuracy of 0.983,precision of 0.985,recall of 0.982,and F-score of 0.983.

Secrecy Efficiency Maximization in Intelligent Reflective Surfaces Assisted UAV Communications

This paper focuses on the secrecy efficiency maximization in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communication.With the popularization of UAV technology,more and more communication scenarios need UAV support.We consider using IRS to improve the secrecy efficiency.Specifically,IRS and UAV trajectories work together to counter potential eavesdroppers,while balancing the secrecy rate and energy consumption.The original problem is difficult to solve due to the coupling of optimization variables.We first introduce secrecy efficiency as an auxiliary variable and propose relaxation optimization problem,and then prove the equivalence between relaxation problem and the original problem.Then an iterative algorithm is proposed by applying the block coordinate descent(BCD)method and the inner approximationmethod.The simulation results show that the proposed algorithm converges fast and is superior to the existing schemes.In addition,in order to improve the robustness of the algorithm,we also pay attention to the case of obtaining imperfect channel state information(CSI).

基于连续可微采样的无人机多向视点规划

无人机在各种工业基础设施的自主巡检中,需要为大型复杂待巡检对象设计大量的数据采集航点,以获取高质量的全覆盖影像数据。逐点飞行作业能耗高采集效率低,限制了无人机大规模的工业应用。为了优化飞行作业模式,有效提升巡检效率,针对已知待巡检复杂设备结构提出一种全新的多向视点规划算法——连续可微采样的无人机多向视点规划算法(MD-VPP)。该方法在满足全面覆盖巡视目标以及数据采集质量要求的前提下,大幅减少航点数量。首先,将云台俯仰角、偏航角和相机成像面积作为约束条件,构建视点采集连续可微的视图质量目标函数,优化求解候选视点集;然后利用贪心算法求解全覆盖待巡检对象的航点位姿。通过实验对比分析了针对不同巡检对象的航点数量和航点数减少率,相比领域内其他优秀方法,所提方法在确保全覆盖的前提下航点数量至少降低77%,极大地提升了无人机单次作业的数据采集效率。

考虑无线充电的无人机路径在线规划

近年来,无人机在物流、通信、军事任务、灾害救援等领域中展现出了巨大的应用潜力,然而无人机的续航能力是制约其使用的重大因素,在无线充电技术不断突破和发展的背景下,本文基于深度强化学习方法,提出了一种考虑无线充电的无人机路径在线优化方法,通过无线充电技术提高无人机的任务能力.首先,对无人机功耗模型和无线充电模型进行了构建,根据无人机的荷电状态约束,设计了一种基于动态上下文向量的深度神经网络模型,通过编码器和解码器的模型架构,实现无人机路径的直接构造,通过深度强化学习方法对模型进行离线训练,从而应用于考虑无线充电的无人机任务路径在线优化.文本通过与传统优化方法和深度强化学习方法进行实验对比,所提方法在CPU算力和GPU算力下分别实现了4倍以及100倍以上求解速度的提升.

空中智能反射面辅助的无线供能通信网络轨迹优化研究

由于无人机(UAV)良好的机动性、可靠性和快速部署等特性,无人机搭载智能反射面(IRS)可以有效解决复杂无线场景中混合接入点和节点之间由于障碍物遮挡导致信息传输和能量传输效率低的问题。该文提出一种基于时间划分的空中智能反射面辅助无线供能通信网络架构,充分利用空中智能反射面的灵活性提高网络性能。该架构针对每一个时隙,采用先收集能量后传输信息方案实现能量和数据的分时传输。在满足节点能量收集阈值的前提下,建立一个联合空中智能反射面飞行轨迹、节点选择关联变量、时隙分配比率和智能反射面相位的多变量耦合优化问题。采用块坐标下降算法把原始优化问题分解为4个子问题分别进行求解。首先根据波束对齐原理求解出智能反射面最优相位的闭式解,然后通过引入辅助变量并采用连续凸近似方法使非凸问题转变为凸问题,最后利用交替优化算法迭代求解。仿真结果表明,该文提出的联合优化方案具有很好的收敛性能并可以显著提高系统平均吞吐量。

考虑信息年龄的无人机辅助智能交通系统计算卸载优化

该文考虑无人机(UAV)交通监测与移动边缘计算(MEC)技术结合的智能交通系统。为了保障系统中数据时效性并且降低系统能耗,提出计及信息年龄(AoI)的UAV计算卸载优化方法。首先,建立UAV辅助的MEC系统模型,允许MEC服务器缓存常用的应用程序并为UAV提供计算卸载,以支持UAV执行交通监测任务。通过联合优化UAV任务卸载决策、UAV上下行通信带宽分配以及被卸载任务的计算资源分配,最小化所有UAV与MEC服务器的总能耗,同时满足AoI与资源容量等约束条件。其次,系统能耗最小化问题是混合整数非凸优化问题,因此采用离散化和线性化手段,快速获得问题的近似最优解,并设计离散点生成算法来调节近似误差。最后,仿真结果表明,即使对于大型的非凸问题,所提方法也能够快速得到近似最优解,并且可以在不同的任务场景中满足AoI等约束条件,最大限度降低系统能耗。仿真结果验证了所提方法的有效性。

基于微型激光雷达的无人机智能化电力线路巡检技术研究

介绍了一种利用激光探测和测距(LiDAR)进行实时电力线检测的算法,并且对微型激光雷达无人机进行电力线路巡检抽象建模。通过使用平面分析法对巡检现场进行距离的比较来分割点云,并提取一组电力线候选点,这些点被拟合到线段上,线段根据它们的共线性质进一步分组比较。该方法可利用图像的共线特性完成特征检验,并在光线条件差以及线路背景复杂的环境下仍可获得较为可靠的巡检结果。案例中电力线路垂直平均误差最大为0.49 m,其他所有直线的估计平均误差都小于0.07 m,水平拟合时,所有平均误差都在0.14 m以内。验证了所提出基于微型激光雷达的无人机技术进行电力线路智能化巡检方法的有效性与可行性。

基于改进PSO的无人机精细化自主巡检航迹布设优化

提出基于改进PSO的无人机精细化自主巡检航迹布设优化。结合波束法测量地面像控点坐标,通过结构矩阵描述平面误差细化值,完成像控点的布设。采用改进PSO算法计算航迹子路径的变更代价,得到新的路径点,由此实现无人机精细化自主巡检航迹布设优化。实验结果表明,所提方法的无人机精细化自主巡检航迹布设时间仅为39.4 min,说明所提方法能够有效提高无人机精细化自主巡检航迹布设效率,精细化自主巡检航迹布设优化效果更好。

A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.