Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments

Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.

Joint Subcarrier and Power Allocation for Multi-UAV Systems

This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.

Dynamic Multi-team Antagonistic Games Model with Incomplete Information and Its Application to Multi-UAV

At present, the studies on multi-team antagonistic games (MTAGs) are still in the early stage, because this complicated problem involves not only incompleteness of information and conflict of interests, but also selection of antagonistic targets. Therefore, based on the previous researches, a new framework is proposed in this paper, which is dynamic multi-team antagonistic games with incomplete information (DMTAGII) model. For this model, the corresponding concept of perfect Bayesian Nash equilibrium (PBNE) is established and the existence of PBNE is also proved. Besides, an interactive iteration algorithm is introduced according to the idea of the best response for solving the equilibrium. Then, the scenario of multiple unmanned aerial vehicles (UAVs) against multiple military targets is studied to solve the problems of tactical decision making based on the DMTAGII model. In the process of modeling, the specific expressions of strategy, status and payoff functions of the games are considered, and the strategy is coded to match the structure of genetic algorithm so that the PBNE can be solved by combining the genetic algorithm and the interactive iteration algorithm. Finally, through the simulation the feasibility and effectiveness of the DMTAGII model are verified. Meanwhile, the calculated equilibrium strategies are also found to be realistic, which can provide certain references for improving the autonomous ability of UAV systems. © 2014 Chinese Association of Automation.

通信约束下UAV集群协同拦截任务分配算法

针对多无人机协同拦截多机动目标的任务分配问题,同时考虑到真实战场环境中存在的通信约束以及探测范围约束条件,本文提出了分步一致性拍卖算法(SCBAA)。首先,对真实战场环境中存在的通信约束以及探测范围约束等问题进行了描述分析,构建了多无人机协同拦截任务分配模型,设计了综合效能函数以及相应约束条件。其次,为解决多无人机协同打击单一目标的不平衡任务分配以及冲突消解问题,将原任务分配过程分为主要任务分配以及次要任务分配两部分,通过多次拍卖以及冲突消解实现多无人机对单一目标的任务分配。仿真结果表明,该算法可有效解决通信约束条件下的分布式多无人机协同拦截问题,并适应动态环境中任务分配对实时性的要求。

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.

Path planning method for controlling multi-UAVs to reach multi-waypoints simultaneously under the view of visual navigation

Abstract： There is a high demand for unmanned aerial vehicle （UAV） flight stability when using vi- sion as a detection method for navigation control. To meet such demand, a new path planning meth- od for controlling multi-UAVs is studied to reach multi-waypoints simultaneously under the view of visual navigation technology. A model based on the stable-shortest pythagorean-hodograph （PH） curve is established, which could not only satisfy the demands of visual navigation and control law, but also be easy to compute. Based on the model, a planning algorithm to guide multi-UAVs to reach multi-waypoints at the same time without collisions is developed. The simulation results show that the paths have shorter distance and smaller curvature than traditional methods, which could help to avoid collisions.

融合危险度模糊分级的UAV动态避障方法

针对无人机(UAV)在多障碍环境中面对运动障碍的反应式避障问题,为了在反应避障决策时能够朝向周围障碍更远、更少的方向进行避障,提出了一种融合障碍危险度模糊分级的三维速度障碍避障方法。通过三维速度障碍法,给出探测域内各运动障碍的碰撞预测。选取坐标系三轴方向作为相互独立的可用避障加速度方向,在速度及加速度限制下求解三轴方向上避障加速度方向以及数值区间。通过模糊算法,给出了周围其他静止障碍的危险分级方法,提出优化评价函数对计算各方向上的最优加速度,给出优化避障指令。仿真结果表明,针对复杂环境,UAV在反应避障的同时考虑了其他障碍的影响,相比原算法拥有了更高的避障成功率和更少的避障次数。

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。

一种三维多UAV协同航迹规划的空间模糊文化算法

针对多无人机在三维环境下航迹规划搜索空间大、多机协同困难等问题,提出一种基于空间模糊表示和差分进化相结合的文化算法.该方法首先用模糊集合表示三维空间网格点,提高关键路径点的被关注度;然后组合空间模糊信息、历史信息和协同信息成为文化算法的信念空间,用以剪枝规划的搜索空间;在文化算法的种群空间则利用差分进化生成满足多机协同约束的优解,并用差分获得的未知领域知识扩展信念空间,保证进化种群的多样性;最后,通过共享信息促进知识的积累和修正搜索的方向.仿真实验表明,该方法提高了关键路径点选取的效率,能够探索空间中更多的未知区域,避免求解陷入局部最优,更符合多机协同的需求,有助于快速规划出多条可行的协同航迹.

多UAV协同路径规划研究

将多UAV(Unmanned Aerial Vehicle无人飞行器)协同路径规划分为单机路径规划与多机协同。多机协同在单机路径规划基础上考虑各机的飞行时间、战术攻击策略等。先为每一UAV规划可飞航路,然后以攻击时间为协同变量,对规划路径进行调整,在部分UAV的路径中加入附加机动并(或)调整飞行速度和高度,实现多UAV的协同。仿真试验验证算法有效。

基于动态数据驱动的多UAV实时任务规划

针对在实时动态条件下多UAV任务规划问题,提出了基于动态数据驱动的多UAV实时任务规划仿真平台,主要包括基于MultiUAV2的真实UAV群仿真平台和基于Multi-Agent的预测仿真平台两个部分。采用了A*算法对真实系统工作流进行探索,在此基础之上构建了多UAV合成工作流模型,然后针对动态数据注入运行仿真的问题,研究了传感器任务重置及传感器的预处理方法。最后,通过一个仿真实例验证了提出方法的可行性和有效性。

复杂多约束UAVs协同目标分配的一种统一建模方法

多无人机协同目标分配(Multi-UAVs cooperative target assignment problem,MUCTAP)是一个条件复杂的多模型、多约束组合优化问题,很难用一致的方法获得可行的解.为解决该问题,本文对各种目标分配模型统一建模,设计了一致的模型处理方法;针对三维环境特点,提出利用空间垂直切面计算无人机估计航程代价的方法,并利用航程代价矩阵优化目标分配算法;同时加入协同约束关系计算,提高分配算法的准确性.仿真实验验证了该方法能够处理多种情况的目标分配问题,具有较高的通用性和准确性,能够有效完成多机协同目标分配任务.

基于EMRP算法的多UAV协同航迹规划

为解决作战环境中的多无人机协同航迹规划问题,提出一种基于层次分解策略的航迹规划方法。通过骨架化算法生成规划空间,利用基于进化计算的多航迹规划(EMRP)算法为各UAV找到K条备用航迹,实现了利用EMRP算法与数学形态学相结合解决多UAV协同航迹规划问题,并对生成的初始航迹进行平滑处理,得到满足UAV机动要求的可行航迹。然后建立协同模型,为各无人机规划出既能满足时间协同要求,又能满足整体代价最优的可行航迹。仿真表明了该种方法的可行性。

一种基于PSO的多UAV协同航迹规划方法

本文针对多无人飞行器(UAV)协同执行任务的应用场景,提出了一种综合考虑任务分配和航迹规划因素的航迹规划算法。该算法借鉴微粒群算法(PSO)的思想,采用新的编码方式和优化策略。仿真实验验证了算法的有效性。

多通道航磁同步数据采集系统的研制

针对无人机多传感器矢量磁测补偿系统,研制了一套小体积、低功耗的多通道数据同步采集系统。该系统采用模拟采集和控制部分分开设计的方法。模拟采集部分基于STM32处理器STM32F103C8T6,集成了9通道24位高精度的模数转换芯片ADS1255、倾角和温度传感器,能够实现9通道数据的同步采集、飞行载体的倾角数据和温度数据的获取。控制部分基于32位ARM低功耗处理器S3C2416和嵌入式LINUX系统,完成了数据实时采集、SD卡存储、GNSS同步校时和网络通信等功能,并具有功耗低、体积小的特点,满足了机载数据采集系统特定的应用需求。

基于SAS算法的三维多UAV协同航迹规划方法

在实际应用中航迹规划着重考虑的是规划出的航迹应具有良好的可靠性能,针对多UAV协同航迹规划有多个约束条件且环境复杂、规划耗时长等问题,使用智能算法解决多UAV协同航迹规划问题无法确保规划航迹的可靠性能。因此,提出了一种启发式SAS算法,用以解决此问题。仿真结果证明,使用该算法规划出的航迹具有较高的可靠性。

基于Agent组织的多UAV协调模型

无人驾驶飞机(UAV)系统的协调问题是UAV研究领域的一个重要问题。研究了适应于UAV系统的Agent结构模型以及组织模型,提出了一种基于Agent组织的多UAV协调模型并给出了其数学化表述,分析了多UAV协调模型中任务协调、角色协调以及动作协调等三个阶段,并对提出的模型进行了仿真。实验表明,模型具有较好的适应性和稳定性。

基于UAV/VISSIM的城市交通控制评价与优化

针对当前我国城市交通控制对静动态道路交通信息一体化采集存在的难点,设计运用低空多旋翼无人机(multi-rotor UAV)加载高清摄像机及其相应的视频分析系统采集交通控制区域内交通控制评价与优化所需数据,在此基础上利用VISSIM软件进行城市交通控制效果的评价与配时方案优化,并以深圳市沙河西路一西丽湖路交叉口为例进行验证。结果表明,基于UAV/VISSIM的城市交通控制评价与优化方案具有可行性,优化后该路口的服务水平由目前的E级提升至C级,有效提高城市交通控制评价和整体工作效率。

多UAV集结任务的分布式协同与优化控制

基于一致性理论的多无人机分布式协同控制已广泛运用于无人机作战中,通过一致性控制算法实现状态一致完成协同需求。建立了集结问题的数学模型,基于协调变量和协调函数的分解策略进行求解。为实现协同控制的最优性,改进了平均一致性控制算法,采用Hamilton-Jacobi-Bellman方程给出基本优化一致性控制算法。在控制算法中引入过去状态差值,提高控制算法的动态响应性和能量最优性;同时采用遗传算法优化代价函数的加权矩阵,进一步提高控制算法的动态响应性和能量最优性,缩短了任务执行时间。理论分析和仿真实验验证了方法的有效性和可行性。

快速一致性控制算法下的多UAV分布式协同控制

多无人机系统中,系统状态的一致性是实现多无人机分布式协同控制的基础,一致性控制算法是多无人机系统实现状态一致的有效方法。通信约束条件下,无人机平台基于局部交互信息,通过一致性控制算法,控制系统状态演化一致。为解决改变通信拓扑结构和多跳路由通信的不可操作性问题,引入状态差值和预测状态,设计新的快速一致性控制算法。给出新算法收敛性和快速性的相关定理,并进行了严格的数学证明,在分布式协同控制结构下实现多无人机快速任务协同。理论分析和仿真实验验证了算法的可行性和有效性。