Research on Operation of UAVs in Non-isolated Airspace

In order to explore the safe operation of UAVs in non-segregated airspace, a collision risk model for cylindrical UAVs based on conflict areas was constructed and the risk of conflict between manned and unmanned aerial vehicles was researched. According to the results of risk analysis, a strategy for solving the conflict of aircraft is proposed, and the risk assessment experiment of unmanned aerial vehicle (UAV) in non-isolated airspace conflict is carried out. The results show that under the experimental conditions, large unmanned aerial vehicles equipped with ADS-B, TCAS and other airborne sensing systems will indeed interfere with other aircraft in airspace when they enter non-isolated airspace. Especially when the number of aircraft in airspace is large, the automatic avoidance system of UAV will increase the avoidance time and trigger the safety alarm, but the safety level is still acceptable. This indicates that it is relatively safe for UAVs to enter non-isolated airspace under limited conditions. The results can be used as a reference for the safe operation of unmanned aerial vehicle (UAV) in non-isolated airspace.     

Optimum design of three-dimensional behavioural decentralized controller for UAV formation flight

This article proposes a behavioural decentralized approach that allows an unmanned aerial vehicle (UAV) formation flight to carry out a waypoint-passing mission effectively. The objective of the proposed controller is to make each UAV in the formation fly through predefined waypoints while maintaining its distance from other UAVs. To perform these two tasks, which can conflict with each other, coupled dynamics of UAVs is considered; this combines the dynamics of all the members in the formation. To apply the behavioural decentralized controller on the basis of the coupled dynamics, a feedback linearization technique with a diffeomorphic transfer map is derived for a three-dimensional UAV kinematics model. A behavioural approach in which the control input is decided by the relative weight of each UAV's desired behaviour is considered, so that the UAVs can react promptly in various situations. Optimization techniques of the gain matrices are also performed to improve the performance of the formation flight using eigen-structure analysis and Cholesky decomposition. To verify the performance of the proposed controller, numerical simulation is performed for a waypoint-passing mission of multiple UAVs.     

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 considerable 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.     

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel Energy Aware Data Collection with Routing Planning for 6G-enabled UAV communication (EADCRP-6G) technique. The goal of the proposed EADCRP-6G technique is to conduct energy-efficient cluster-based data collection and optimal route planning for 6G-enabled UAV networks. EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) and organize these clusters. Besides, Artificial Fish Swarm-based Route Planning (AFSRP) technique is applied to choose an optimum set of routes for UAV communication in 6G networks. In order to validated whether the proposed EADCRP-6G technique enhances the performance, a series of simulations was performed and the outcomes were investigated under different dimensions. The experimental results showcase that the proposed model outperformed all other existing models under different evaluation parameters.     

Grasp Planning and Visual Servoing for an Outdoors Aerial Dual Manipulator

This paper describes a system for grasping known objects with unmanned aerial vehicles (UAVs) provided with dual manipulators using an RGB-D camera. Aerial manipulation remains a very challenging task. This paper covers three principal aspects for this task: object detection and pose estimation, grasp planning, and in-flight grasp execution. First, an artificial neural network (ANN) is used to obtain clues regarding the object’s position. Next, an alignment algorithm is used to obtain the object’s six-dimensional (6D) pose, which is filtered with an extended Kalman filter. A three-dimensional (3D) model of the object is then used to estimate an arranged list of good grasps for the aerial manipulator. The results from the detection algorithm—that is, the object’s pose—are used to update the trajectories of the arms toward the object. If the target poses are not reachable due to the UAV’s oscillations, the algorithm switches to the next feasible grasp. This paper introduces the overall methodology, and provides the experimental results of both simulation and real experiments for each module, in addition to a video showing the results.     

Trajectory optimization for unmanned aerial vehicle formation reconfiguration

The results of trajectory optimization to reconfigure unmanned aerial vehicle (UAV) formation in the event of a critical failure are presented. The formation reconfiguration process includes two distinct manoeuvres: an escape manoeuvre for a malfunctioning UAV and a replacement movement for an alternative UAV related to its position. This article deals with both manoeuvres but focuses more on the replacement movement. The trajectory optimization problem of the escape manoeuvre is formulated as a minimum-time problem to reduce the possibility of collisions resulting from a failure, whereas the problem of the replacement movement is formulated as a final-time fixed minimum-fuel problem to secure the durability of the group of UAVs. These problems are solved by means of sequential quadratic programming. To evaluate the performance of the optimization results, fuel consumption for the replacement movement is considered and optimization of a three-phase reference trajectory is performed. The results show that the trajectory optimization reduces the fuel consumption and saves time.     

Efficient unmanned aerial vehicle formation rendezvous trajectory planning using Dubins path and sequential convex programming

Trajectory planning of formation rendezvous of multiple unmanned aerial vehicles (UAVs) is formulated as a mixed-integer optimal control problem, and an efficient hierarchical planning approach based on the Dubins path and sequential convex programming is proposed. The proposed method includes the assignment of rendezvous points (high level) and generation of cooperative trajectories (low level). At the high level, the assignment of rendezvous points to UAVs is optimized to minimize the total length of Dubins-path-based approximate trajectories. The assignment results determine the geometric relations between the UAVs’ goals, which are used as equality constraints for generating trajectories. At the low level, trajectory generation is treated as a non-convex optimal control problem, which is transformed to a non-convex parameter optimization and then solved via sequentially performing convex optimization. Numerical experiments demonstrate that the proposed method can generate feasible trajectories and can outperform a typical nonlinear programming method in terms of efficiency.     

Governance assessment of UAV implementation in Kenyan land administration system

The ‘fit-for-purpose’ (FFP) approach was developed to respond to the challenges that the creation of a sustainable land administration system (LAS) faces. FFP has proved its value in integrating technological innovations such as unmanned aerial vehicles (UAVs) in LAS. Technological innovations need to consider the governance context when being implemented. Understanding the relevance of the FFP approach and acknowledging the importance of the governance context, this research applies the ‘Fit-for-purpose governance assessment framework’ (FGAF). FGAF helps to understand the challenges that the implementation of UAVs can face in the LAS in Kenya. This governance assessment is based on 16 semi-structured in-depth interviews, three one-day workshops with local stakeholders and UAVs pilot studies to test the technology. The findings suggest that there are major and minor challenges concerning different attributes of FFP qualities. The governance context across counties is fragmented with a high degree of uncertainty and uneven capacity conditions. There are overlapping responsibilities and trust issues among the different actors. Participation of private and non-governmental actors is limited. Yet, steps have been taken toward the adoption of innovative approaches. Although financial resources are limited, the diversity of local and international stakeholders can work as a leverage point to support the implementation of UAVs in the mid and long-term.     

Analysis of Wireless Backhaul Networks Based on Aerial Platform Technology for 6G Systems

As next generation communication technologies emerge, new high data rate applications and high-definition large-screen video streaming have become very popular. As a result, network traffic has been increasing so much that existing backhaul networks soon will not be able to support all traffic demands. To support these needs in future 6G mobile systems, the establishment of an additional backhaul wireless network is considered essential. As one of the solutions, a wireless backhaul network based on an aerial platform has been proposed. In order to explore the potential of aerial platforms as wireless backhaul networks, in this paper, the categories for wireless backhaul networks based on aerial platforms are investigated. This paper includes a survey of the definitions and characteristics of low altitude platforms (LAPs) and high altitude platforms (HAPs), as well as channel models according to the atmosphere. For wireless backhaul network designs based on aerial platforms, altitude and platform selection options, deployment options, energy issues, and security based on target location and performance were considered in the analysis and investigation.     

基于传染-免疫机制的无人机集群协同探测与跟踪

目标跟踪是无人机集群作战中的一个重要问题.为使无人机集群在进行目标跟踪的过程中快速做出响应,提出一种基于传染-免疫仿生模型的无人机集群协同探测及跟踪策略.通过对传染机制和免疫机制的生物机理进行分析,建立基于传染-免疫机制的仿生模型,该模型包括直接传染、交叉传染和免疫修复3个子过程,将其映射到无人机集群目标跟踪中,实现数...     

Efficient UAV Communications: Recent Trends and Challenges

Unmanned Ariel Vehicles (UAVs) are flying objects whose trajectory can be remotely controlled. UAVs have lot of potential applications in the areas of wireless communications, internet of things, security, traffic management, monitoring, and smart surveying. By enabling reliable communication between UAVs and ground nodes, emergency notifications can be efficiently and quickly disseminated to a wider area. UAVs can gather data from remote areas, industrial units, and emergency scenarios without human involvement. UAVs can support ubiquitous connectivity, green communications, and intelligent wireless resource management. To efficiently use UAVs for all these applications, important challenges need to be investigated. In this paper, we first present a detailed classification of UAVs based on factors such as their size, communication range, weight, and flight altitude. We also explain the hardware system configuration and uses of these UAVs. We present a brief overview of recent work done related to three major challenges in UAVs. These challenges include trajectory control, energy efficiency and resource allocation. We also present three open challenges and future opportunities for efficient UAV communications. These include use of learning algorithms for resource allocation and energy efficiency in UAVs, intelligent surfaces-based communications for enhanced reliability in UAVs, and security algorithms to combat malicious attacks against UAVs.     

Cultivated Land Monitoring System Based on Dynamic Wake-Up UAV and Wireless of Distributed Storage

The drawbacks of the traditional multi-hop structure of wireless sensor networks (WSNS) are susceptible to reflection, scattering and diffraction, serious attenuation of signal intensity and phase deviation due to various obstacles in cultivated land environment and mountains. In this paper, the wireless signal intensity is measured and curve fitting is done respectively in both open environment and obstacle-environment with both sending and receiving height of 0 m, 1 m, and 2 m. It is found out that packet loss probability is too high when an obstacle exists. In such a case, unmanned aerial vehicle (UAV) is introduced to activate terrestrial nodes and operation mechanism of distributed storage. Experimental results show that both ground-to-air barrier-free communication and communication with obstacles can greatly reduce the energy consumption of each node and packet loss probability, thus greatly improving the reliability of UAV-WSN system.     

Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of UAVs. The DEVIPSK (differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means) is proposed to solve the model. In DEVIPSK, the population is initialized by K-Means to obtain better initial positions of UAVs. Besides, considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm, a mutation strategy pool is used to update the positions of UAVs. The experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system.     

基于地面站辅助的无人机自主架线系统

将无人机（unmanned aerial vehicle, UAV）技术用于实现电力线展放正逐步成为电力行业发展的主要趋势,研究无人机自主架线技术能够有效提高作业效率、降低施工成本和保障工人安全。但现阶段无人机架线技术面临的问题主要有：1）大多数无人机依赖人工操控或地面站发布航点控制,智能化水平低,长期作业会给电力工人带来较强负荷;2）无人机缺乏自主避障能力且感知能力不足,电线、电杆等障碍物会对其造成安全隐患。为解决上述问题,首先,构建了无人机自主架线系统的硬件框架和以ROS（robot operating system,机器人操作系统）为基础的模块化软件框架,并在此基础上实现了架线任务规划算法和架线弓检测算法等,使无人机具备稳定的自主架线能力。然后,利用碰撞检测方法构建了无人机碰撞模型,并提出了无人机路径规划算法,同时引入地面站辅助避障策略,以有效提高无人机的安全性。实验结果表明：所设计的无人机自主架线系统的软、硬件框架合理,架线任务规划算法能帮助无人机高效稳定地完成自主架线任务;地面站能够实时监控无人机状态,并在必要时及时辅助无人机避障,最大程度地保证了无人机的安全。所设计系统安全可靠,可满足实际电力架线作业的需求。     

统计过程控制在无人机故障预报系统中的应用

无人机故障预报是无人机故障检测的重要组成部分之一,它能有效地减小无人机的故障发生率。利用统计过程控制(SPC)理论,从数理统计的角度对某无人机的测试数据进行分析,并根据常见的五种故障数据,设计了一套无人机故障预报系统,同时给出了系统的工作原理和具体的实现过程。本系统有助于技术人员发现无人机的潜在故障并判断检测设备是否虚警,具有良好的使用性能和价值。     

Fused Deposition Modeling for Unmanned Aerial Vehicles (UAVs): A Review

Additive Manufacturing (AM) is a game changing production technology for aerospace applications. Fused deposition modeling is one of the most widely used AM technologies and recently has gained much attention in the advancement of many products. This paper introduces an extensive review of fused deposition modeling and its application in the development of high performance unmanned aerial vehicles. The process methodology, materials, post processing, and properties of its products are discussed in details. Successful examples of using this technology for making functional, lightweight, and high endurance unmanned aerial vehicles are also highlighted. In addition, major opportunities, limitations, and outlook of fused deposition modeling are also explored. The paper shows that the emerge of fused deposition modeling as a robust technique for unmanned aerial vehicles represents a good opportunity to produce compact, strong, lightweight structures, and functional parts with embedded electronic.

     

基于插补导航的全自主旋翼机设计与控制

为了解决传统旋翼无人机因手动遥操作而导致飞行不稳定的问题,研究了基于插补导航的全自主旋翼机的设计与控制方法。首先,根据导航系统和地面站,获得当前无人机与目标点之间的位置姿态关系;然后基于插补控制算法,利用机载控制器输出相应的能够控制无人机稳定飞行的脉冲宽度信号,实现脱离手动遥操作的四旋翼无人机全自主飞行控制。最后,通过全自主插补导航飞行仿真与实验,验证了该全自主无人机设计的可行性。结果表明,该全自主控制架构能够有效地模拟遥操作信号,实现无人机的全自主飞行控制,且具有更高的稳定性。     

Bio-inspired wing morphing for unmanned aerial vehicles using intelligent materials

Biologically inspired engineering or Biomimicry is the practice of developing designs and technologies inspired by nature. This conscious use of examples from nature is a form of applied case-based reasoning thus treating nature itself as a database of solutions that have survived for millions of years-survival of the fittest. Inspired by nature, we have developed aircraft wings that imitate the amazing flight of birds. This bio-inspired effort, which is the result of a collaborative research program with Defence Science Organisation National Laboratories of Singapore, is concerned with the design and development of a novel wing prototype for unmanned aerial vehicles (UAVs) that morphs seamlessly without the use of complex hydraulics and/or servo motors. The novel design, selected from a number of designs, is characterised by a high degree of flight adaptability, enhanced manoeuvrability and improved performance with a limited added weight. These characteristics were attained through the use of shape memory alloys as actuators in an antagonistic fashion. Unlike compliant actuators that require continued input of thermal energy, antagonistic setup does not suffer from this difficulty. This is because they require the thermal energy to deform the wing but not to maintain its morphed shape. Structural analysis based upon safety factors specified by FAR23 standards and aerodynamic analysis using FLUENT were conducted on the novel designs to validate their suitability as viable wings for UAVs. In addition, conditioning of the shape memory actuators was conducted using a specially designed circuitry that imposes the appropriate heating and cooling cycles at set periodic times. The outcome of this study is manifest in the new designs that satisfy the missions of different UAVs.     

Gull-inspired joint-driven wing morphing allows adaptive longitudinal flight control

Birds dynamically adapt to disparate flight behaviours and unpredictable environments by actively manipulating their skeletal joints to change their wing shape. This in-flight adaptability has inspired many unmanned aerial vehicle (UAV) wings, which predominately morph within a single geometric plane. By contrast, avian joint-driven wing morphing produces a diverse set of non-planar wing shapes. Here, we investigated if joint-driven wing morphing is desirable for UAVs by quantifying the longitudinal aerodynamic characteristics of gull-inspired wing-body configurations. We used a numerical lifting-line algorithm (MachUpX) to determine the aerodynamic loads across the range of motion of the elbow and wrist, which was validated with wind tunnel tests using three-dimensional printed wing-body models. We found that joint-driven wing morphing effectively controls lift, pitching moment and static margin, but other mechanisms are required to trim. Within the range of wing extension capability, specific paths of joint motion (trajectories) permit distinct longitudinal flight control strategies. We identified two unique trajectories that decoupled stability from lift and pitching moment generation. Further, extension along the trajectory inherent to the musculoskeletal linkage system produced the largest changes to the investigated aerodynamic properties. Collectively, our results show that gull-inspired joint-driven wing morphing allows adaptive longitudinal flight control and could promote multifunctional UAV designs.     

多体组合式无人机飞行力学稳定性分析及增稳控制研究

多体组合式无人机是一种新概念飞行器,由多个小型无人机以翼尖铰接连接组成,该概念飞行器能够融合小型无人机与高空长航时无人机两类飞行平台的性能和任务优势,发展潜力巨大。基于Newton-Euler方程及升力线方法建立多体组合式无人机飞行力学模型,针对允许相对滚转运动自由度的双机组合式无人机系统进行配平及稳定性分析。结果表明,不同于传统构型飞行器,多体组合式无人机系统具有不稳定复合运动飞行模态,该复合模态由相对滚转运动主导,在无控状态下该构型飞行器无法稳定飞行。在完成动力学建模的基础上,基于PID控制方法,为每个单体飞行器单独设计增稳控制回路以达到增稳控制目的,仿真结果表明该控制思路有效,可以快速镇定发散的飞行力学系统。