Cooperative and Geometric Learning Algorithm (CGLA) for path planning of UAVs with limited information

In this paper, we propose a new learning algorithm, named as the Cooperative and Geometric Learning Algorithm (CGLA), to solve problems of maneuverability, collision avoidance and information sharing in path planning for Unmanned Aerial Vehicles (UAVs). The contributions of CGLA are three folds: (1) CGLA is designed for path planning based on cooperation of multiple UAVs. Technically, CGLA exploits a new defined individual cost matrix, which leads to an efficient path planning algorithm for multiple UAVs. (2) The convergence of the proposed algorithm for calculating the cost matrix is proven theoretically, and the optimal path in terms of path length and risk measure from a starting point to a target point can be calculated in polynomial time. (3) In CGLA, the proposed individual weight matrix can be efficiently calculated and adaptively updated based on the geometric distance and risk information shared among UAVs. Finally, risk evaluation is introduced first time in this paper for UAV navigation and extensive computer simulation results validate the effectiveness and feasibility of CGLA for safe navigation of multiple UAVs.     

基于贝叶斯决策的无人机飞行路径自动规划方法

传统无人机飞行路径自动规划方法无法获取全部障碍物信号,使无人机飞行不能达到避障效果,导致飞行路线规划效果较差;为此提出基于贝叶斯决策的无人机飞行路径自动规划方法;无人机飞行路径自动规划硬件模块包含自动规划模块、动画演示模块、地图导航模块和数据导出模块,自动规划模块负责控制无人机飞行;动画演示模块使用240PRO型号的LEWITT声卡,为展示飞机飞行路线提供声音;LS-TM8N地图导航模块通过串口将射频信号发送到天线的输入端,再由数据导出模块导出并保存相关数据;基于贝叶斯决策原理,结合贝叶斯元胞蚁群算法,计算贝叶斯先验概率和后验概率,规划无人机飞行路径,获取最优路径;实验结果表明,该方法遇到静态障碍物捕获的避障信号在-28～30mV范围内波动,动态障碍物捕获的避障信号在-27～30 mV范围内波动,与实际障碍物信号波动范围一致,避障效果较优.     

Energy efficient secure data collection with path-constrained mobile sink in duty-cycled unattended wireless sensor network

This paper addresses the problem of secure data transmission and balanced energy consumption in an unattended wireless sensor network (UWSN) comprising of multiple static source nodes and a mobile sink in the presence of adversaries. The proposed system comprises of three phases: the identification of data collection points (convex nodes), path planning by the mobile sink, and secure data transmission. An energy-aware convex hull algorithm is used for the identification of data collection points for data transmission to the mobile sink. Data transmission from sensor nodes to the nearest data collection point is performed using multihop communication and from sensor nodes to the mobile sink in a single hop. Data are securely transmitted through an elliptic curve cryptography based ElGamal scheme for message authentication. A data packet is associated with a digital signature. The variation in a digital signature and threshold energy obtained using support vector machine is used to determine the presence of malicious nodes in the network. The performance of the proposed system is evaluated using Cooja simulator by Contiki for various node counts under a static sink and mobile sink, with different threat scenarios. The results indicate that the proposed system is resilient against threats and provides satisfactory performance     

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.     

基于多智能体强化学习的无人机群室内辅助救援

本文主要研究了在室内场景中使用多台无人机设备对受害者进行合作搜索的问题.在室内场景中,依赖全球定位系统获取受害者位置信息可能是不可靠的.为此,本文提出一种基于多智能体强化学习(MARL)方案,该方案着重对无人机团队辅助救援时的路径规划问题进行研究.相比于传统方案,所提方案在大型室内救援场景中更具优势,例如部署多台救援无...     

Real-time Implementation and Validation of a New Hierarchical Path Planning Scheme of UAVs via Hardware-in-the-Loop Simulation

We present a real-time hardware-in-the-loop simulation environment for the validation of a new hierarchical path planning and control algorithm for a small fixed-wing unmanned aerial vehicle (UAV). The complete control algorithm is validated through on-board, real-time implementation on a small autopilot having limited computational resources. We present two distinct real-time software frameworks for implementing the overall control architecture, including path planning, path smoothing, and path following. We emphasize, in particular, the use of a real-time kernel, which is shown to be an effective and robust way to accomplish real-time operation of small UAVs under non-trivial scenarios. By seamless integration of the whole control hierarchy using the real-time kernel, we demonstrate the soundness of the approach. The UAV equipped with a small autopilot, despite its limited computational resources, manages to accomplish sophisticated unsupervised navigation to the target, while autonomously avoiding obstacles.     

基于能量水平的多Sink节点传感器网络路由算法

单Sink节点传感器网络存在着部分关键路径上节点能量消耗过快、路由选择算法单一以及Sink节点失效等问题.首先提出了多Sink节点传感器网络数据收集的系统框架;给出了拓扑发现和维护策略;然后提出了基于最小能量消耗路由算法.在分析了该算法的不足后提出了基于能量水平的路由算法,按照计算得到的能量水平选择最优的路径进行数据传送.实验证明,基于能量水平的路由算法比基于最小能量消耗路由算法能更有效提高传感器网络的使用寿命.     

多无人机空中加油的最优会合航路规划

为了空中加油能面向多架无人机,本文提出了空中加油的三维最优会合航路规划算法.多架无人机分布在不同区域,需要加油机沿预定的规划航路飞行会合,以完成空中加油任务.由于加油机可同时服务的受油机数量有限,需要寻找最优分配策略将无人机预分配至不同加油区域与之会合.本文首先根据加、受油机在各加油区域的最短会合时间,将最优分配问题建模为整数线性规划问题,求解得到加油机与各无人机的最优会合点.随后,本文提出了三维空间Dubins路径延长算法,保证各无人机按照分配结果与加油机同时到达会合点.最后,分别针对二维和三维多架无人机空中加油任务进行仿真.仿真结果表明本文提出的最优会合航路规划算法得到的Dubins航路,可以保证空中加油会合任务在最短时间内完成.     

Joint optimization of UAV position and user grouping for UAV-assisted hybrid NOMA systems

This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non-orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non-convexity, calling for an effective solution. To address this issue, a two-stage approach is proposed. In the first stage, a particle swarm optimization algorithm is employed to optimize the UAV positions without considering user grouping. With the UAV positions optimized, a game theory-based approach is utilized in the second stage to optimize user grouping and improve the sum rate of the hybrid NOMA system. Simulation results demonstrate that the proposed two-stage method achieves solutions close to the global optimum of the original problem. By optimizing the positions of UAVs and user groups, the sum rate can be effectively improved. Additionally, optimizing the deployment of UAVs ensures better fairness in providing communication services to multiple users.     

头脑风暴优化算法求解带转角能耗多无人机路径规划问题

多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption, MUPP-AEC)模型,该模型考虑了无人机在加速、减速、匀速、转角等飞行条件下的能耗差异。针对MUPP-AEC的特点,提出目标空间聚类离散头脑风暴优化算法(discrete brain storm optimization algorithm in objective space, DBSO-OS)。该算法采用个体空间整数编码和带2-opt的分阶段贪婪法解码策略,并对扰动算子和个体更新算子进行了离散化定义。个体更新算子中采用了混合随机反转变换和部分匹配变换的生成策略。实验结果表明:DBSO-OS能有效地求解MUPP-AEC;所提离散头脑风暴算子在全局收敛能力、求解精度和稳定性等方面均优于传统头脑风暴算子;在中小规模测试算例和较大规模测试算例的测试中,DBSO-OS优于对比算法。     

多约束条件下无人机航迹规划

针对多约束条件下的无人机航迹快速规划问题,建立了导航精度约束下无人机航迹规划模型,并设计了“基于Dijkstra算法的航迹规划法”求解模型。通过校正策略优选、校正方案优选和O-D邻接矩阵处理方式,简化搜索路径,降低计算量,提高执行效率,从而实现对传统Dijkstra算法的改进。在满足导航精度约束条件的前提下,以航迹长度最短和经过校正点数量最少为研究目标进行仿真实验,并将所得结果与传统Dijkstra算法和遗传算法所得结果分别进行对比,发现此算法在精度与复杂度方面均优于传统算法和遗传算法。此结果表明,导航精度约束下无人机航迹规划模型和“基于Dijkstra算法的航迹规划法”在解决多约束下无人机航迹规划问题方面具有一定的正确性、有效性和先进性。     

A Framework for Using Unmanned Aerial Vehicles for Data Collection in Linear Wireless Sensor Networks

The wireless sensor network (WSN) technology have been evolving very quickly in recent years. Sensors are constantly increasing in sensing, processing, storage, and communication capabilities. In many WSNs that are used in environmental, commercial and military applications, the sensors are lined linearly due to the linear nature of the structure or area that is being monitored making a special class of these networks; We defined these in a previous paper as Linear Sensor Networks (LSNs), and provided a classification of the different types of LSNs. A pure multihop approach to route the data all the way along the linear network (e.g. oil, gas and water pipeline monitoring, border monitoring, road-side monitoring, etc.), which can extend for hundreds or even thousands of kilometers can be very costly from an energy dissipation point of view. In order to significantly reduce the energy consumption used in data transmission and extend the network lifetime, we present a framework for monitoring linear infrastructures using LSNs where data collection and transmission is done using Unmanned Aerial Vehicles (UAVs). The system defines four types of nodes, which include: sensor nodes (SNs), relay nodes (RNs), UAVs, and sinks. The SNs use a classic WSN multihop routing approach to transmit their data to the nearest RN, which acts as a cluster head for its surrounding SNs. Then, a UAV moves back and forth along the linear network and transport the data that is collected by the RNs to the sinks located at both ends of the LSN. We name this network architecture a UAV-based LSNs (ULSNs). This approach leads to considerable savings in node energy consumption, due to a significant reduction of the transmission ranges of the SN and RN nodes and the use of a one-hop transmission to communicate the data from the RNs to the UAV. Furthermore, the strategy provides for reduced interference between the RNs that can be caused by hidden terminal and collision problems, that would be expected if a pure multihop approach is used at the RN level. In addition, three different UAV movement approaches are presented, simulated, and analyzed in order to measure system performance under various network conditions.     

Comprehensive survey of UAVs communication networks

Recently, technologies related to Unmanned Aerial Vehicle (UAV) are growing rapidly particularly sensors, networking, and processing technologies. Accordingly, governments and industry have heavily invested in the studies of UAVs and improvingtheir performances for reliable and secure deployments. The design methods and the investigation of UAVs systems have progressed from mono-UAV uses to multi-UAVs and cooperative UAVs systems that need a high level of coordination and collaboration to perform tasks which require new networking models, approaches, and mechanisms for highly mobile nodes involving many complex parameters and constraints. In this context, this paper provides more details and offers a thorough investigation concerning UAV communication protocols, networking systems, architectures, and applications. In addition, we discuss UAV solutions as well as highlighting important technical challenges and open research issues requiring further studies and R&D work.     

基于改进A*算法的无人机避障路径规划

近年来,物流行业的飞速发展,运输是物流的重要环节之一,根据数据显示,运输的成本占据整个物流成本的50%以上.无人机的使用有效的控制了运输成本,合理规划物流无人机的飞行路线,也起着至关重要的作用.在物流无人机的航迹规划中,必须保证无人机飞行过程中能够准确避开禁飞区.本文基于A^*算法,结合多种类型的禁飞区,设计出一种改进算法,能够找到任意两客户点间无人机避障飞行的最优路线.仿真结果表明,本文所设计的算法能够有效解决多类型禁飞区并存的无人机避障路径规划问题.     

Time situate recurrence estimation technique for efficient data collection in war field sensor network

In a war field sensor network, the data collection process is based on the energy that exists in the sink node as well as in intermediate nodes. Since sensor nodes are typically much dense, data collected by sensor nodes have considerable redundancy. An effective data collection approach is developed to eliminate redundancy, reduce the number of broadcasts, and to save energy. We have deployed the new source-aware method to collect the data in a fast and efficient manner. The source- aware is needed at every node for sink conformation, which is used to find the correct next sink neighbor in the network. We propose a time situate recurrence estimation procedure (TSRE) with the support of uncertain rule sets to collect the data efficiently. This strategy follows the set of guidelines in which every node assigns different esteem for the configuration of the data collection and this range of esteems specify the feasible advantages of the data. Also, the strategy performs a time situate recurrence estimation procedure to complete the interruption identification framework with the assistance of a received sink pattern. This method recognizes the interruption effectively and produces favorable outcomes and also find a separate path in the network. In these ways all source nodes will assign each neighbor for data collection. In this network, every source will use node sink for data transmission in the system. Based on the received sink pattern, this approach improves the data collection efficiency of the task or the application being executed and reduces the energy consumption in the network. The novelty of this approach is verified by comparison with the existing method which shows enhancement in the throughput efficiency, data collection efficiency and delay minimization of the overall network.     

Evolutionary algorithm based offline/online path planner for UAV navigation

An evolutionary algorithm based framework, a combination of modified breeder genetic algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an offline/online path planner for unmanned aerial vehicles (UAVs) autonomous navigation. The path planner calculates a curved path line with desired characteristics in a three-dimensional (3-D) rough terrain environment, represented using B-spline curves, with the coordinates of its control points being the evolutionary algorithm artificial chromosome genes. Given a 3-D rough environment and assuming flight envelope restrictions, two problems are solved: i) UAV navigation using an offline planner in a known environment, and, ii) UAV navigation using an online planner in a completely unknown environment. The offline planner produces a single B-Spline curve that connects the starting and target points with a predefined initial direction. The online planner, based on the offline one, is given on-board radar readings which gradually produces a smooth 3-D trajectory aiming at reaching a predetermined target in an unknown environment; the produced trajectory consists of smaller B-spline curves smoothly connected with each other. Both planners have been tested under different scenarios, and they have been proven effective in guiding an UAV to its final destination, providing near-optimal curved paths quickly and efficiently.     

多无人机模糊多目标分布式地面目标协同追踪

针对城市环境中多约束条件下多无人机协同追踪地面目标问题,综合考虑具有不同重要性等级的多个优化目标,提出了一种基于分布式预测控制的模糊多目标航迹规划方法.首先,考虑城市环境中建筑物对无人机视线遮挡、无人机和传感器能量消耗等因素,分别采用目标覆盖度、控制输入代价和开关量形式传感器能耗等为目标函数,将多无人机协同追踪航迹规划转化为多目标优化问题;然后,基于分布式预测控制框架,利用每架无人机未来有限时域内的预测状态,构建多无人机之间的避碰约束,并结合最小转弯半径等约束,形成分布式协同航迹规划模型;最后,针对多个优化目标的不同重要性等级要求,利用模糊满意优化思想将目标模糊化,并根据更重要目标具有更重要满意度的原则,将优先等级表示为松弛满意度序,通过在线求解得到有限时域内每架无人机的局部航迹;与传统多目标加权算法仿真结果对比,验证了所提方法的有效性,充分说明了该方法能够获得同时满足目标优化和重要性等级要求的最优航迹.     

基于Q学习的无人机辅助WSN数据采集轨迹规划

针对无人机辅助采集无线传感器网络数据时各节点数据产生速率随机和汇聚节点状态不一致的场景，提出基于Q学习的非连续无人机轨迹规划算法Q-TDUD，以提高无人机能量效率和数据采集效率。基于各节点在周期内数据产生速率的随机性建立汇聚节点的汇聚延时模型，应用强化学习中的Q学习算法将各汇聚节点的延迟时间和采集链路的上行传输速率归一化到奖励函数中，通过迭代计算得到最佳非连续无人机飞行轨迹。实验结果表明，与TSP-continues、TSP、NJS-continues和NJS算法相比，Q-TDUD算法能够缩短无人机的任务完成时间，提高无人机能效和数据采集效率。     

无线传感器网络节点基于遗传算法的充电路径规划

无线传感器网络应用越来越广泛,为了解决传感器节点的能量问题,将无线充电技术应用到传感器网络中.使用无人机为传感器节点进行无线充电,但是无人机的电池容量有限,合理的规划能够让无人机以最小的充电代价获得最大的网络效用.以最小化无人机能耗为优化目标,对无人机能量消耗进行分析,将优化目标简化成最小化路径距离,并使用遗传算法对无...     

基于Multi-Agent系统的多飞行器协同路径规划方法的研究

无人多飞行器(UAV)协同技术是当前分布式人工智能的一个热点领域,其中一个关键技术在于如何实现多UAV集群根据复杂环境中目标、威胁、地形变化以及各UAV之间的性能约束动态进行实时性航路规划。提出一种基于Multi-agent系统的多UAV对实时动态多目标进行路径规划的方法。其核心是基于Multi-agent系统的decen-tralized控制方案。在Multi-agent平台上,实现了agent对于环境、目标、任务等路劲规划约束条件的建模,同时提出了多agent动态路径规划方法的实现方案。方案使用DisCSP模型框架,将基于真实复杂战场环境的实时路径规划问题所涉及的多复杂限制条件,抽象成Multi-agent系统中的各个约束条件,通过多agent间Dynamic Programming过程求解多UAV实时动态多目标的路径规划和协同任务分配的ABT算法,并实现在动态威胁和地形以及动态目标下具备集群协同能力的多UAV实时仿真系统。