Distributed Multicircular Circumnavigation Control for UAVs with Desired Angular Spacing

This paper addresses a multicircular circumnavigation control for UAVs with desired angular spacing around a nonstationary target.By defining a coordinated error relative to neighboring angular spacing,under the premise that target information is perfectly accessible by all nodes,a centralized circular enclosing control strategy is derived for multiple UAVs connected by an undirected graph to allow for formation behaviors concerning the moving target.Besides,to avoid the requirement of target’s states being accessible for each UAV,fixed-time distributed observers are introduced to acquire the state estimates in a fixed-time sense,and the upper boundary of settling time can be determined offline irrespective of initial properties,greatly releasing the burdensome communication traffic.Then,with the aid of fixed-time distributed observers,a distributed circular circumnavigation controller is derived to force all UAVs to collaboratively evolve along the preset circles while keeping a desired angular spacing.It is inferred from Lyapunov stability that all errors are demonstrated to be convergent.Simulations are offered to verify the utility of proposed protocol.

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

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.

Multi-UAVs Collaborative Path Planning in the Cramped Environment

Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles(UAVs)in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search(ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes.The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.

Combat situation suppression of multiple UAVs based on spatiotemporal cooperative path planning

Aiming at the suppression of enemy air defense(SEAD)task under the complex and complicated combat sce-nario,the spatiotemporal cooperative path planning methods are studied in this paper.The major research contents include opti-mal path points generation,path smoothing and cooperative rendezvous.In the path points generation part,the path points availability testing algorithm and the path segments availability testing algorithm are designed,on this foundation,the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path.In the path smoothing part,taking ter-minal attack angle constraint and maneuverability constraint into consideration,the Dubins curve is introduced to smooth the path segments.In cooperative rendezvous part,we take esti-mated time of arrival requirement constraint and flight speed range constraint into consideration,the speed control strategy and flight path control strategy are introduced,further,the decoupling scheme of the circling maneuver and detouring maneuver is designed,in this case,the maneuver ways,maneu-ver point,maneuver times,maneuver path and flight speed are determined.Finally,the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles(UAVs)is effectively real-ized,in this way,the combat situation suppression against the enemy can be realized in SEAD scenarios.

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

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.

Cooperative trajectory optimization of UAVs in approaching stage using feedback guidance methods

This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles.Based on the optimal trajectory trends and feedback guidance methods,a new cooperative control algorithm is proposed to optimize trajectories of multi-UAVs for target tracking in approaching stage.Based on UAV kinematics and sensor performance models,optimal trajectory trends of UAVs are analyzed theoretically.Then,feedback guidance methods are proposed under the optimal observation trends of UAVs in the approaching target stage,producing trajectories with far less computational complexity and performance very close to the best-known trajectories.Next,the sufficient condition for the UAV to form the optimal observation configuration by the feedback guidance method is presented,which guarantees that the proposed method can optimize the observation trajectory of the UAV in approaching stage.Finally,the feedback guidance method is numerically simulated.Simulation results demonstrate that the estimation performance of the feedback guidance method is superior to the Lyapunov guidance vector field(LGVF)method and verify the effectiveness of the proposed method.Additionally,compared with the receding horizon optimization(RHO)method,the proposed method has the same optimization ability as the RHO method and better real-time performance.

VTOL UAVs for Forest Monitoring and Management via Remote Sensing: A Case Study in Khao Yai National Park, Thailand

The utilization of UAVs (Unmanned Aerial Vehicles) has experienced a remarkable upsurge in various industries, including forestry. Their capacity to expeditiously and effectively cover large tracts of land has resulted in their widespread adoption as a valuable forest management and monitoring tool. The versatility of UAVs extends to their capability to perform quick and efficient surveys of large areas, inventory of tree species, and monitoring of forest health. This research paper reports on the successful utilization of VTOL (Vertical Takeoff and Landing) UAV that was designed and built at the IESSD (Institute of Earth Science and Sustainable Development) located in the AAA (Asia Aviation Academy) at KMITL (King Mongkut’s Institute of Technology Ladkrabang) Prince of Chumphon Campus, Thailand. The VTOL UAV is employed for resource and environmental missions, as well as forest monitoring by using remote sensing technology. VTOL UAVs are used for aerial surveillance to conduct air photography, data collection, and processing for resource and environmental missions. This research paper presents a comprehensive analysis of the areas at risk of deforestation and forest encroachment in a particular region of Khao Yai National Park in Thailand, highlighting the potential for the resulting photographs to inform evidence-based decision-making and facilitate sustainable forest management practices. This study offers recommendations to develop VTOL UAVs remote sensing capabilities and mitigate deforestation and forest encroachment in Khao Yai National Park.

A binary gridding path-planning method for plant-protecting UAVs on irregular fields

The use of plant-protecting unmanned aerial vehicles(UAVs)for pesticide spraying is an essential operation in modern agriculture.The balance between reducing pesticide consumption and energy consumption is a significant focus of current research in the path-planning of plant-protecting UAVs.In this study,we proposed a binarization multi-objective model for the irregular field area,specifically an improved non-dominated sorting genetic algorithm–II based on the knee point and plane measurement(KPPM-NSGA-ii).The binarization multi-objective model is applied to convex polygons,concave polygons and fields with complex terrain.The experiments demonstrated that the proposed KPPM-NSGA-ii can obtain better results than the unplanned path method whether the optimization of pesticide consumption or energy consumption is preferred.Hence,the proposed algorithm can save energy and pesticide usage and improve the efficiency in practical applications.

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks onWireless Sensor Networks(WSNs).Jamming is a type of Denial of Service(DoS)attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly.To defend and prevent such attacks,the first step is to detect them.The current detection approaches use centralized techniques to detect jamming,where each node collects information and forwards it to the base station.As a result,overhead and communication costs increased.In this work,we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer.As a result,we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks.Furthermore,we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB.With a detection accuracy of 99.7%for the k-nearest neighbors(KNN)algorithm and average testing accuracy of 99.9%,the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

基于排队论的UAVS通信网切换方案分析

针对UAVS通信网切换管理中的信道分配问题,提出了保护切换信道与切换呼叫按照先入先出队列排队相结合的方案(GCM-FIFO),利用Markov链对该方案进行了建模,通过数学推导分析了系统的性能,并就等待队列容量对切换性能的影响进行了讨论和仿真.结果表明:通过实施GCM-FIFO方案,UAVS通信网获得了较好的切换性能,而且随着队列容量的增加,切换呼叫失败的概率呈下降趋势,但是当队列容量超过某个值后,切换呼叫失败的概率趋于恒定,因而应根据系统相关参数,利用仿真结果选择合适的队列容量.

MUAVs系统的混合组织模型

分析多UAV系统的协作控制以及2种基本的组织模型及其优缺点,结合大规模UAVs(MUAVs)系统协作的复杂特征,提出适用于MUAVs系统的几种分级混合网络组织模型以及具有自适应能力的动态杂类混合组织模型。研究结果表明,该方法可有效降低MUAVs系统管理和通信复杂度,提高系统的可靠性与鲁棒性。

An adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs

To overcome nonlinear and 6-DOF(degrees of freedom)under-actuated problems for the attitude and position of quadrotor UAVs,an adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs is proposed,in which an adaptive law is designed to online estimate the parameter variations and the upper bound of external disturbances and the assessments is utilized to compensate the backstepping sliding mode control.In addition,the tracking error of the design method is shown to asymptotically converge to zero by using Lyapunov theory.Finally,based on the numerical simulation of quadrotor UAVs using the setting parameters,the results show that the proposed control approach can stabilize the attitude and has hover flight capabilities under the parameter perturbations and external disturbances.

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

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.g.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.

The use of UAVs for landslide disaster risk research and disaster risk management: a literature review

On a global scale,from 2005 to 2019,there were 275 high-magnitude,low-frequency disasters that involved 14,172 fatalities and four million affected people.Similar patterns have taken place during longer periods of time in recent decades.This paper aims to analyse the contribution of the international landslide research community to disaster risk reduction and disaster risk management in reference to the use of Unmanned Aerial Vehicles(UAVs)in a literature review.The first section notes the relevance of disaster risk research contributions for the implementation of initiatives and strategies concerning disaster risk management.The second section highlights background information and current applications of drones in the field of hazards and risk.The methodology,which included a systematic peer review of journals in the ISI Web of Science and SCOPUS,was presented in the third section,where the results include analyses of the considered data.This study concludes that most current scholarly efforts remain rooted in hazards and post-disaster evaluation and response.Future landslide disaster risk research should be transdisciplinary in order to strengthen participation of the various relevant stakeholders in contributing to integrated disaster risk management at local,subnational,national,regional and global levels.

Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles(UAVs).First,the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers.Next,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance.In order to validate the effectiveness of the proposed algorithm,experiments are conducted on four datasets.For the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test set.With regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 mAP.For vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 mAP.For unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of 48.75.Compared with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more competitive.The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.

Coalition Formation for Multiple UAVs Cooperative Search and Attack with Communication Constraints in Unknown Environment

A coalition formation algorithm is presented with limited communication ranges and delays in unknown environment,for the performance of multiple heterogeneous unmanned aerial vehicles(UAVs)in cooperative search and attack missions.The mathematic model of coalition formation is built on basis of the minimum attacking time and the minimum coalition size with satisfying resources and simultaneous strikes requirements.A communication protocol based on maximum number of hops is developed to determine the potential coalition members in dynamic network.A multistage sub-optimal coalition formation algorithm(MSOCFA)with polynomial time is established.The performances of MSOCFA and particle swarm optimization(PSO)algorithms are compared in terms of complexity,mission performance and computational time.A complex scenario is deployed to illustrate how the coalitions are formed and validate the feasibility of the MSOCFA.The effect of communication constraints(hop delay and max-hops)on mission performance is studied.The results show that it is beneficial to determine potential coalition members in a wide and deep range over the network in the presence of less delay.However,when the delays are significant,it is more advantageous to determine coalitions from among the immediate neighbors.

非合作大型UAVs飞行冲突预测方法研究

为降低非合作大型无人机(UAVs)之间的碰撞风险,首先在分析大型UAVs运行方式的基础上,提出基于UAVs运行管理平台的非合作大型UAVs飞行冲突预测机制和程序;然后建立基于UAVs飞行状态数据的飞行冲突预测模型,分别预测水平和垂直2个方向上的飞行冲突,进而判断是否存在飞行冲突,并分析冲突告警阈值,防止UAVs地面站过晚收到告警信息;最后利用3架UAVs实时飞行状态数据实例分析模型并验证该模型的可行性。结果表明:所提的冲突预测机制和程序可以实现非合作大型UAVs之间的冲突预测和预警;冲突预测模型可以准确判断是否存在飞行冲突并计算飞行冲突发生的时间。

随机网格回归Monte Carlo双UAVs最优目标协调跟踪

针对多个无人机(unmanned aerial vehicle,UAV)执行基于视觉的目标跟踪的最佳协调问题,提高不可预知的地面目标的最佳结合点的视觉测量效果,提出了一种基于随机网格回归Monte Carlo的UAV最优目标跟踪策略。首先,通过无人机动力学和目标动力学分析,获得双UAV情况下的随机最优协调控制目标;其次,针对提出的控制目标,引入Monte Carlo求解方案,同时为解决标准Monte Carlo方案中存在的状态空间维度较高,计算复杂且精度不高的问题,利用随机网格方式构建回归Monte Carlo方案,实现UAV的最优协调控制;最后,通过仿真实验验证了所提方法的有效性。

Contour Based Path Planning with B-Spline Trajectory Generation for Unmanned Aerial Vehicles (UAVs) over Hostile Terrain

This research focuses on trajectory generation algorithms that take into account the stealthiness of autonomous UAVs;generating stealthy paths through a region laden with enemy radars. The algorithm is employed to estimate the risk cost of the navigational space and generate an optimized path based on the user-specified threshold altitude value. Thus the generated path is represented with a set of low-radar risk waypoints being the coordinates of its control points. The radar-aware path planner is then approximated using cubic B-splines by considering the least radar risk to the destination. Simulated results are presented, illustrating the potential benefits of such algorithms.

Optimal search for moving targets with sensing capabilities using multiple UAVs

This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.