Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams.

This paper considers a heterogeneous team of cooperating unmanned aerial vehicles (UAVs) drawn from several distinct classes and engaged in a search and action mission over a spatially extended battlefield with targets of several types. During the mission, the UAVs seek to confirm and verifiably destroy suspected targets and discover, confirm, and verifiably destroy unknown targets. The locations of some (or all) targets are unknown a priori, requiring them to be located using cooperative search. In addition, the tasks to be performed at each target location by the team of cooperative UAVs need to be coordinated. The tasks must, therefore, be allocated to UAVs in real time as they arise, while ensuring that appropriate vehicles are assigned to each task. Each class of UAVs has its own sensing and attack capabilities, so the need for appropriate assignment is paramount. In this paper, an extensive dynamic model that captures the stochastic nature of the cooperative search and task assignment problems is developed, and algorithms for achieving a high level of performance are designed. The paper focuses on investigating the value of predictive task assignment as a function of the number of unknown targets and number of UAVs. In particular, it is shown that there is a tradeoff between search and task response in the context of prediction. Based on the results, a hybrid algorithm for switching the use of prediction is proposed, which balances the search and task response. The performance of the proposed algorithms is evaluated through Monte Carlo simulations.     

Cooperative search stategies of multi-UAVs for random targets

针对目标在空间上随机均匀分布,在时间上动态随机产生的搜索环境,提出一种基于质心 V 图划分(CVP)的无人机搜索决策方法对多架无人机进行搜索空间分配.首先建立了 CVP 数学模型,在此基础上提出了基于 CVP 的目标分配算法,并证明了算法的收敛性;最后进行了仿真实验,仿真结果表明所提出的 CVP 策略能有效进行随机目标搜索,且算法具有良好的自适应能力.     

Search Strategies for Multiple UAV Search and Destroy Missions

Multiple UAVs are deployed to carry out a search and destroy mission in a bounded region. The UAVs have limited sensor range and can carry limited resources which reduce with use. The UAVs perform a search task to detect targets. When a target is detected which requires different type and quantities of resources to completely destroy, then a team of UAVs called as a coalition is formed to attack the target. The coalition members have to modify their route to attack the target, in the process, the search task is affected, as search and destroy tasks are coupled. The performance of the mission is a function of the search and the task allocation strategies. Therefore, for a given task allocation strategy, we need to devise search strategies that are efficient. In this paper, we propose three different search strategies namely; random search strategy, lanes based search strategy and grid based search strategy and analyze their performance through Monte-Carlo simulations. The results show that the grid based search strategy performs the best but with high information overhead.     

针对运动目标的多无人机协同鸽群优化搜索方法

针对多无人机协同运动目标搜索问题,本文设计了改进鸽群优化算法的协同搜索决策.首先,基于运动目标的独立性,建立了服从正态分布的目标概率信息图模型;为了提高环境中目标存在的确定度,建立了搜索环境的确定度信息图.其次,通过建立的吸引和排斥数字信息素图,引导无人机向未搜索区域飞行,减少重复搜索概率,提高协同目标搜索效率,并基于传统的鸽群算法,通过加入速度更新修正机制和精英代机制对其进行改进.然后,结合环境中目标的存在概率信息以及无人机搜索目标的探测信息,使用改进鸽群优化算法,规划无人机的最优搜索飞行路径.并设计避碰机制,以有效防止无人机搜索过程中的碰撞.最后,通过比较仿真实验验证了改进鸽群优化算法对运动目标协同搜索的有效性.     

Response Threshold Model Based UAV Search Planning and Task Allocation

This paper addresses a search planning and task allocation problem for a Unmanned Aerial Vehicle (UAV) team that performs a search and destroy mission in an environment where targets with different values move around. The UAVs are heterogeneous having different sensing and attack capabilities, and carry limited amount of munitions. The objective of the mission is to find targets and eliminate them as quickly as possible considering the values of the targets. In this context, there are two distinct issues that need to be addressed simultaneously: search planning and task allocation. The search plan generates an efficient search path for each UAV to facilitate a fast target detection. The task allocation assigns UAVs attack tasks over detected targets such that each UAV’s attack capability is respected. We model these two issues in one framework and propose a distributed approach that utilizes a probabilistic decision making mechanism based on response threshold model. The proposed approach accounts for natural uncertainties in the environment, and provides flexibility, resulting in efficient exploration in the environment and effective allocation of attack tasks. The approach is evaluated in simulation experiments in comparison with other methods, of which results show that our approach outperforms the other methods.     

针对动态目标的多无人机协同组合差分进化搜索方法

针对多无人机动态目标协同搜索问题,提出一种组合差分进化无人机协同搜索航迹规划方法.建立动态目标协同搜索环境信息图模型及无人机运动模型.基于改进差分蝙蝠算法和自适应差分进化算法,设计基于种群数量自适应分配的组合框架,将差分进化算法中的变异、交叉和选择机制引入蝙蝠算法,构建组合差分进化算法的协同搜索算法,并对无人机动态目标协同搜索的航迹进行优化.针对待搜索目标轨迹随机多变且具有规避侦察特性的现实场景,建立可回访数字信息图和自适应目标搜索增益函数,从而提高无人机对动态目标的捕获能力.最后,通过仿真结果验证所提出的无人机动态目标协同搜索算法的有效性.     

大规模固定翼无人机集群编队控制方法

针对大规模固定翼无人机集群的编队控制问题,提出一种分层分组控制方案.首先,设计一种分布式的无人机集群分层分组控制架构,将集群内所有无人机分成若干独立且不相交的群组,并在群组内分别形成“长机层”和“僚机层”;其次,对各群组内的长机设计协同路径跟随控制律,使长机收敛到各自期望路径上的虚拟目标点,并通过对各虚拟目标点的协调控制实现长机的协同,进而实现各群组间的协同;然后,对各组的僚机设计控制律以跟随其所在群组的长机,使其与长机保持期望的相对位置且朝向一致.设计的大规模集群编队控制律考虑了固定翼无人机的控制约束和环境中风的影响,并证明了闭环系统的稳定性.100架固定翼无人机集群的全流程数值仿真,验证了所提出控制方法的有效性.     

基于stackelberg多步博弈的无人机协同搜索路径规划

考虑到现有无人机搜索问题研究中无人机、移动目标仅有一方具有远距离探测能力的设定，已经无法体现出战场环境下双方的博弈关系。针对这一不足，基于stackelberg均衡策略，结合多步预测的思想，提出了stackelberg多步博弈策略，实现了无人机、目标都具有远距离探测能力的博弈搜索。通过建立无人机、目标各自的路径收益函数，使双方能够根据不同时刻的博弈状态选择相对应的函数，实现无人机的动态路径规划。仿真结果表明所提出策略完全适用于该博弈模型，比贪婪策略具有更高的搜索效率，大大提高了目标捕获率。     

Effectiveness of a Camera as a UAV Mounted Search Sensor for Target Detection: An Experimental Investigation

In this paper, we consider a problem of autonomous search using single or multiple Unmanned Ariel Vehicles (UAVs) mounted with downward-facing cameras. A model of the effectiveness of the search sensor, camera, in this case, is essential for developing strategies for optimal deployment and path planning of UAVs for efficient search. The probability of detection of a target of interest as a function of its distance from the point directly below the camera is used to model the search effectiveness. We carried out experiments and obtained a search effectiveness model for a camera in the laboratory environment using ArUco markers and triangular shapes as targets.

     

A mission management system for complex aerial logistics by multiple unmanned aerial vehicles in MBZIRC 2017

In this study, we present a system that manages multiple unmanned aerial vehicles (UAVs) for a search, pickup, and drop mission in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). Three UAVs picked up and dropped 23 circular and rectangular targets into a designated drop box. To control the operation of three UAVs flying over an arena of 90 × 60 m, we designed and integrated a set of technologies into our system: airspace allocation, communication framework among UAVs, anticollision based on geofencing, and a token‐based prioritization for coordination. The proposed UAV system uses a single GPS and its error of a few meters is solved by means of the following component technologies: (a) flight path generator based on one reference point, (b) vision‐based redefinition of a reference point for GPS correction, and (c) calibration of flight path to update the reference point. The pickup‐and‐drop mission is conducted via color‐ and shape‐based vision processing and a magnetic gripper to pickup and drop‐off the targets. Our proposed system is able to successfully manage three UAVs, recognize targets on the ground, and drop the targets into a drop box in the drop zone. Finally, we achieved fourth place among 18 teams in Challenge 3.     

Cooperative air and ground surveillance

Unmanned aerial vehicles (UAV) can be used to cover large areas searching for targets. However, sensors on UAVs are typically limited in their accuracy of localization of targets on the ground. On the other hand, unmanned ground vehicles (UGV) can be deployed to accurately locate ground targets, but they have the disadvantage of not being able to move rapidly or see through such obstacles as buildings or fences. In this paper, we describe how we can exploit this synergy by creating a seamless network of UAVs and UGVs. The keys to this are our framework and algorithms for search and localization, which are easily scalable to large numbers of UAVs and UGVs and are transparent to the specificity of individual platforms. We describe our experimental testbed, the framework and algorithms, and some results.     

未知环境中无人机协同搜索信息融合方法研究

针对未知环境中多无人机协同搜索的信息融合问题进行研究,建立了环境模型及无人机搜索模型,在此基础上,提出基于D-S证据理论的无人机协同搜索信息融合方法。在协同搜索中运用该方法不仅能够快速发现目标,并能有效识别不同性质的目标。将该方法与传统的贝叶斯融合方法分别应用于多无人机协同搜索决策中,通过比较仿真结果验证了D-S信息融合方法的有效性及优越性。     

面向环境探测的多智能体自组织目标搜索算法

针对在复杂非结构化环境下如何协调多个无人机发现静态或动态目标的问题,建立了自组织目标搜索算法框架。结合磁探仪等效平均探测宽度模型,受昆虫协调方式和鸟群效应的生物机制启发,提出了基于仿生集群算法的无人机集群分布式目标搜索模型;采用改进的自适应差分进化算法帮助无人机集群模型在环境中平衡勘探和探索,实现无人机群体的协同搜索优化。该自组织目标搜索算法旨在以最短时间实现跟踪目标数量的最大化。基于仿真平台的实验测试了该策略的性能,验证了算法对具有未知目标的非结构化复杂环境的适用性。     

基于纳什均衡博弈的多无人机对地攻击目标分配方法

研究一种新的多无人机对地攻击目标分配问题.该问题中攻击方试图通过无人机击毁防御方的高价值目标,防御方试图通过发射拦截导弹对无人机进行拦截,但攻防双方无法事先观察到对方实际采取的目标分配方案.通过分析防御方的拦截导弹目标分配方案对攻击方收益的影响,将问题构建为一个零和矩阵博弈模型,模型的策略空间随无人机、高价值目标、拦截导弹数量的增加呈爆炸式增长.鉴于此,现有算法难以在有效时间内对其进行求解,提出一种基于两阶段邻域搜索的改进Double Oracle (DO-TSNS)算法.实验结果表明,相较于DO、UWMA和DO-NS算法, DO-TSNS算法能够更有效地求解考虑防御方具有拦截行为的多无人机对地攻击目标分配问题.     

Multiple UAV Coalitions for a Search and Prosecute Mission

Unmanned aerial vehicles (UAVs) have the potential to carry resources in support of search and prosecute operations. Often to completely prosecute a target, UAVs may have to simultaneously attack the target with various resources with different capacities. However, the UAVs are capable of carrying only limited resources in small quantities, hence, a group of UAVs (coalition) needs to be assigned that satisfies the target resource requirement. The assigned coalition must be such that it minimizes the target prosecution delay and the size of the coalition. The problem of forming coalitions is computationally intensive due to the combinatorial nature of the problem, but for real-time applications computationally cheap solutions are required. In this paper, we propose decentralized sub-optimal (polynomial time) and decentralized optimal coalition formation algorithms that generate coalitions for a single target with low computational complexity. We compare the performance of the proposed algorithms to that of a global optimal solution for which we need to solve a centralized combinatorial optimization problem. This problem is computationally intensive because the solution has to (a) provide a coalition for each target, (b) design a sequence in which targets need to be prosecuted, and (c) take into account reduction of UAV resources with usage. To solve this problem we use the Particle Swarm Optimization (PSO) technique. Through simulations, we study the performance of the proposed algorithms in terms of mission performance, complexity of the algorithms and the time taken to form the coalition. The simulation results show that the solution provided by the proposed algorithms is close to the global optimal solution and requires far less computational resources.     

Ground Target Detection Using Cooperative Unmanned Aerial Systems

In this paper, we present a comparative study that evaluates the merit of cooperative unmanned aerial sensor systems against that of a single system with equivalent capabilities. The motivation for our study stems from the current lack of theoretical and empirical work that shows the effectiveness of multiple cooperative unmanned vehicles (UAVs) over the proponents of a single, sophisticated UAV. Using a case study of searching and detecting ground targets with both electro-optical (EO) and infrared (IR) signatures, we quantify the advantage of multiple cooperative UAVs over a single UAV with equivalent sensing capabilities. Simulation results that support the use of cooperative systems over a single system are included.     

基于自适应变异的多目标鸽群优化的无人机目标搜索

无人机在搜索任务中起着关键的作用,它能够在复杂环境中寻找到目标.无人机搜索问题是一个相对复杂的多约束条件下的多目标优化问题.大多数搜索算法不能满足搜索过程中高效率和低功耗的要求.本文所采用的目标搜索方法是一种基于Agent路由和光传感器的解耦滚动时域方法.为了优化目标搜索方法的参数,本文提出一种基于Agent路由和光传感器的自适应变异多目标鸽群优化(AMMOPIO)算法.利用自适应飞行机制可以获得较好的鸽群分布,种群具有多样性和收敛性.利用变异机制简化了鸽群优化算法中的模型,提高了搜索效率.实验仿真结果验证了所提出的AMMOPIO算法在目标搜索问题中的可行性和有效性.     

An Empirical Evaluation of Co-ordination Strategies for an AUV and UAV

We propose a framework for cooperative search using a combination of an unmanned aerial vehicle (UAV) and an autonomous underwater vehicle (AUV). Such a combination allows search platforms to adapt to changes in both mission objectives and environmental parameters. We propose three strategies for coordination between an UAV and AUV to maximize the area explored while minimizing the idle time of the UAV and AUV. We evaluate the efficacy of these strategies while varying the speed, communication range and the number of targets. Preliminary results suggest the feasibility of our approach to combine UAVs and AUVs for effectively searching a given area.     

UAVs in Urban Operations: Target Interception and Containment

In future urban warfare scenarios, the expectations placed on unmanned aerial vehicles (UAVs) in terms of autonomy, reliability and cooperation will be significantly increased. In this paper, a novel algorithm is developed which enables a swarm UAVs to contain and intercept multiple evading targets. In particular, the algorithm allows a swarm of UAVs to intercept significantly faster evading targets if there are more UAVs than targets. Several numerical experiments are conducted, in which four pursuers attempt to intercept one or two faster evaders. The algorithm is very effective at containing the targets; however, as the number of evading targets increases the speed of the evaders must be reduced to ensure capture. Finally, a scenario in which there are more evading targets than pursuers is considered, and, given that the evaders were slower than the pursers, containment and capture was successfully accomplished.     

Multi-UAV Cooperation and Control for Load Transportation and Deployment

This paper deals with the cooperation and control of multiple UAVs with sensing and actuation capabilities. An architecture to perform cooperative missions with a multi-UAV platform is presented. The interactions between UAVs are not only information exchanges but also physical couplings required to cooperate in the joint transportation of a single load. Then, the paper also presents the control system for the transportation of a slung load by means of one or several helicopters. Experimental results of the load transportation system with one and three helicopters are shown. On the other hand, the UAVs considered in the platform can also deploy small objects, such as sensor nodes, on different locations if it is required. This feature along with the whole platform architecture are illustrated in the paper with a real multi-UAV mission for the deployment of sensor nodes to repair the connectivity of a wireless sensor network.