UAV/UGV协同环境下的目标识别与全局路径规划研究

针对单独机器人难以执行复杂环境中任务的问题,Unmanned Air/Ground Vehicle(UAV/UGV)协同系统近年来受到了广泛关注。为了提高执行任务的工作效率,提出一种基于视觉传感器下UAV/UGV协同系统中UAV目标识别下UGV全局路径规划的方法,无人机利用高空视野优势获取目标物与环境信息, SURF算法和图像分割实现环境建模。无人车根据无人机获取的信息,利用优化的A*算法完成全局路径规划,并且在典型搜救场景中进行了仿真验证。实验表明,SURF算法能满足目标识别的精确度、实时性和鲁棒性;并且利用优化的A*算法实现了UGV快速准确的全局路径规划。     

强化学习的地-空异构多智能体协作覆盖研究

以无人机（unmanned aerial vehicle, UAV）和无人车（unmanned ground vehicle, UGV）的异构协作任务为背景,通过UAV和UGV的异构特性互补,为了扩展和改进异构多智能体的动态覆盖问题,提出了一种地-空异构多智能体协作覆盖模型。在覆盖过程中,UAV可以利用速度与观测范围的优势对UGV的行动进行指导;同时考虑智能体的局部观测性与不确定性,以分布式局部可观测马尔可夫（decentralized partially observable Markov decision processes,DEC-POMDPs）为模型搭建覆盖场景,并利用多智能体强化学习算法完成对环境的覆盖。仿真实验表明,UAV与 UGV间的协作加快了团队对环境的覆盖速度,同时强化学习算法也提高了覆盖模型的有效性。     

通信时延下UAV/UGV混合编队控制系统的稳定性分析

针对通信时延下的高维异构无人机(UAV,unmanned aerial vehicle)/无人车(UGV,unmanned ground vehicle)混合编队控制系统,对系统稳定的充分必要条件和准确时延边界的计算方法进行了研究;具体地,为了处置UAV/UGV工作空间、运动学模型的差异,建立考虑异构特性的UAV/UGV混合编队模型;并针对UAV群组、UGV群组,分别设计基于信息一致性的分布式控制器;利用矩阵相似变换,将高维异构的UAV/UGV混合编队控制系统降维拆分为若干等价的低维子系统,极大地降低了稳定性分析的解析难度和运算量;在此基础上,利用辅助特征函数法推导准确的时延边界,得到系统稳定的充要条件;最后通过仿真验证了所提出稳定性分析方法的有效性。     

空地两用机器人

地面无人驾驶车辆(UGV)及无人驾驶飞行器(UAV)一个在地上一个在天上,各有各的用途.目前日本正在研制一种两用机器人,就是要把两者的长处结合起来.形成一种新的机器人.空地两用机器人可用来执行侦察及监视任务,见图.它首先以UAV的形态进入作战地区,这时它可以垂直起飞,飞过一些UGV无法通过的障碍地区,由于它飞行速度快、观察范围广,因而能充分发挥其优势.然后它降落在作战地区前面的地面上,以UGV形态出现.它可隐蔽在有利地形后面,对敌人进行监视及侦察.由于它不象UAV那样会发出很大的噪声,因而不易被敌人发现.要实现上述两用机器人有许多困难,但如果能实现机器人驱动及控制系统的程序化,且对机器人的载重量及飞行时间有一定的限制,这一设想是可以实现的.     

美军进行陆海空联合扫雷演示

1997年8月18日美军进行了第一次“联合扫雷先进方案技术演示”。参加演示的系统有装在无人机(UAV)上的传感器,包括机载远距离雷场探测系统(ASTAMIDS)和海岸战场侦察与分析(COBRA)系统;水陆两用扫雷(JAMC)无人驾驶地面车辆(UGV);越野灵巧地雷清除(ORSMC)UGV。 第二次联合扫雷演示预计在1998年4月举行,届时将会有更多的无人系统出现,特别是海军的海豚半     

复杂环境下多无人机协作式地面移动目标跟踪

针对多无人机(UAV)协同地面移动目标跟踪问题展开研究.提出一种基于主动感知的问题求解框架,建立多UAV协同目标跟踪问题模型;在此基础上,采用分布式无色信息滤波实现目标状态融合估计与预测;然后,基于预测目标状态,结合滚动时域控制与遗传算法设计一种多UAV在线协同航迹规划算法.仿真结果表明:结合预测目标状态在线优化UAV...     

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。     

UGV协同系统研究进展*

无人地面车辆（UGV）在工业自动化、星球探索、灾后救援、智能交通以及军事作战等多任务领域都具有广阔的应用前景。UGV协同系统通过嵌入组织架构、合作策略、交互机制等协同内容,可以达到拓展环境感知范围、提高复杂环境理解适应能力和增强复杂任务工作效能的目的,受到了国内外广泛的关注。从UGV单体/群体体系结构、多重任务协同分配方法以及协同定位、编队、覆盖/探索等几个方面对目前国内外UGV协同工作关键技术进行了总结,给出了一个UGV协同系统的应用实例,并指出了系统发展趋势。     

路网约束下异构机器人系统路径规划方法

由无人机(Unmanned aerial vehicles, UAV)和地面移动机器人组成的异构机器人系统在协作执行任务时,可以充分发挥两类机器人各自的优势.无人机运动灵活,但通常续航能力有限;地面机器人载荷多,适合作为无人机的着陆平台和移动补给站,但运动受路网约束.本文研究这类异构机器人系统协作路径规划问题.为了降低完成任务的时间代价,提出一种由蚁群算法(Ant colony optimization, ACO)和遗传算法(Genetic algorithm, GA)相结合的两步法对地面机器人和无人机的路线进行解耦,同时规划地面机器人和无人机的路线.第1步使用蚁群算法为地面机器人搜索可行路线.第2步对无人机的最优路径建模,采用遗传算法求解并将无人机路径长度返回至第1步中,用于更新路网的信息素参数,从而实现异构协作系统路径的整体优化.另外,为了进一步降低无人机的飞行时间代价,研究了无人机在其续航能力内连续完成多任务的协作路径规划问题.最后,通过大量仿真实验验证了所提方法的有效性.     

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。     

A cooperative pursuit-evasion game in wireless sensor and actor networks

This paper studies the problem of the pursuit-evasion game under the wireless sensor and actor networks (WSANs). In order to plan paths for pursuers to capture an evader in the pursuit-evasion game, a novel multi-step cooperative strategy is presented. Under this strategy, the pursuit-evasion game is studied in two stages. In the first stage we assume that the evader is always static in the workplace, and in the second stage the evader will move once it senses the existence of pursuers. A Daisy-Chain Formation algorithm and a sliding mode-based method are presented to control the pursuit. Based on Lyapunov stability theory, the proposed algorithm is proved to be convergent. At last, simulation results are provided to demonstrate the effectiveness of the proposed method.     

多追捕者-单-逃跑者追逃问题实现成功捕获的约束条件

针对包含有n个追捕者及1个逃跑者的2维平面多机器人追逃问题,对实现成功捕获的约束条件进行了研究.经过理论分析得出:在机器人拥有全局视野的情况下,即使单一逃跑者性能优于每个追捕者,只要满足追捕者与逃跑者的速率比大于sin(π/n),逃跑机器人落在追捕机器人所构成的凸多边形内部且逃跑者和追捕者构成的相邻追-逃阿波罗尼奥斯圆满足两两相交(相切)这2个约束条件,则追捕者通过选择合适的追捕策略就一定可以实现成功抓捕.此外,还给出了在此约束条件下的追捕者和逃跑者的追逃策略.多组仿真实验同样证明了本文提出的约束条件是正确的.     

A Decentralized Fuzzy Learning Algorithm for Pursuit-Evasion Differential Games with Superior Evaders

In this paper, we consider multi-pursuer single-superior-evader pursuit-evasion differential games where the evader has a speed that is similar to or higher than the speed of each pursuer. A new fuzzy reinforcement learning algorithm is proposed in this work. The proposed algorithm uses the well-known Apollonius circle mechanism to define the capture region of the learning pursuer based on its location and the location of the superior evader. The proposed algorithm uses the Apollonius circle with a developed formation control approach in the tuning mechanism of the fuzzy logic controller (FLC) of the learning pursuer so that one or some of the learning pursuers can capture the superior evader. The formation control mechanism used by the proposed algorithm guarantees that the pursuers are distributed around the superior evader in order to avoid collision between pursuers. The formation control mechanism used by the proposed algorithm also makes the Apollonius circles of each two adjacent pursuers intersect or be at least tangent to each other so that the capture of the superior evader can occur. The proposed algorithm is a decentralized algorithm as no communication among the pursuers is required. The only information the proposed algorithm requires is the position and the speed of the superior evader. The proposed algorithm is used to learn different multi-pursuer single-superior-evader pursuit-evasion differential games. The simulation results show the effectiveness of the proposed algorithm.     

Randomized pursuit-evasion in a polygonal environment

This paper contains two main results. First, we revisit the well-known visibility-based pursuit-evasion problem, and show that in contrast to deterministic strategies, a single pursuer can locate an unpredictable evader in any simply connected polygonal environment, using a randomized strategy. The evader can be arbitrarily faster than the pursuer, and it may know the position of the pursuer at all times, but it does not have prior knowledge of the random decisions made by the pursuer. Second, using the randomized algorithm, together with the solution to a problem called the "lion and man problem" as subroutines, we present a strategy for two pursuers (one of which is at least as fast as the evader) to quickly capture an evader in a simply connected polygonal environment. We show how this strategy can be extended to obtain a strategy for a polygonal room with a door, two pursuers who have only line-of-sight communication, and a single pursuer (at the expense of increased capture time).     

Evasion strategies of a three-player lifeline game

This study examines a multi-player pursuit-evasion game, more specifically, a three-player lifeline game in a planar environment, where a single evader is tasked with reaching a lifeline prior to capture. A decomposition method based on an explicit policy is proposed to address the game qualitatively from two main aspects: (1) the evader’s position distribution to guarantee winning the game (i.e., the escape zone), which is based on the premise of knowing the pursuers’ positions initially, and (2) evasion strategies in the escape zone. First, this study decomposes the three-player lifeline game into two two-player sub-games and obtains an analytic expression of the escape zone by constructing a barrier, which is an integration of the solutions of two sub-games. This study then explicitly partitions the escape zone into several regions and derives an evasion strategy for each region. In particular, this study provides a resultant force method for the evader to balance the active goal of reaching the lifeline and the passive goal of avoiding capture. Finally, some examples from a lifeline game involving more than one pursuer are used to verify the effectiveness and scalability of the evasion strategies.     

A cooperative Homicidal Chauffeur game

We address a pursuit-evasion problem involving an unbounded planar environment, a single evader and multiple pursuers moving along curves of bounded curvature. The problem amounts to a multi-agent version of the classic Homicidal Chauffeur problem; we identify parameter ranges in which a single pursuer is not sufficient to guarantee evader capture. We propose a novel multi-phase cooperative strategy in which the pursuers move in specific formations and confine the evader to a bounded region. The proposed strategy is inspired by the hunting and foraging behaviors of various fish species. We characterize the required number of pursuers for which our strategy is guaranteed to lead to confinement.     

Scalable and practical pursuit-evasion with networked robots

In this paper, we consider the design and implementation of practical pursuit-evasion games with networked robots, where a communication network provides sensing-at-a-distance as well as a communication backbone that enables tighter coordination between pursuers. We first develop, using the theory of zero-sum games, an algorithm that computes the minimal completion time strategy for pursuit-evasion when pursuers and evaders have same speed, and when all players make optimal decisions based on complete knowledge. Then, we extend this algorithm to when evader are significantly faster than pursuers. Unfortunately, these algorithms do not scale beyond a small number of robots. To overcome this problem, we design and implement a partition algorithm where pursuers capture evaders by decomposing the game into multiple multi-pursuer single-evader games. We show that the partition algorithm terminates, has bounded capture time, is robust, and is scalable in the number of robots. We then describe the design of a real-world mobile robot-based pursuit evasion game. We validate our algorithms by experiments in a moderate-scale testbed in a challenging office environment. Overall, our work illustrates an innovative interplay between robotics and communication.     

空地协同机器人目标定位方法

针对空地协同机器人中无人机对地面无人车的实时精准定位问题,提出一种红色双圆型定位标记及标记识别与定位方法。引入颜色分割与轮廓提取相结合的方式,减少提取到的轮廓特征数量,排除背景信息干扰以减少误识别;提出一种圆形轮廓快速检测算法,快速识别目标轮廓并准确定位目标像素坐标和方向;基于针孔相机成像模型,根据目标像素坐标和方向,估计出目标在机体坐标系下三维坐标和偏航角。实验结果表明,无人机与地面无人车相对高度1.5 m时,该方法在[x]轴和[y]轴方向定位误差分别为3.9 mm和3.6 mm,每帧图像平均处理耗时为11.6 ms,优于基于核相关滤波的识别定位方法的13.3 mm、14.3 mm和56.3 ms。该方法与无人机控制相结合,可以实现无人机协同跟踪与自主降落功能,提升空地协同机器人作业效率,具有显著的工程意义。     

On Discrete-Time Pursuit-Evasion Games With Sensing Limitations

In this paper, we address discrete-time pursuit-evasion games in the plane where every player has identical sensing and motion ranges restricted to closed disks of given sensing and stepping radii. A single evader is initially located inside a bounded subset of the environment and does not move until detected. We propose a sweep-pursuit-capture pursuer strategy to capture the evader and apply it to two variants of the game. The first involves a single pursuer and an evader in a bounded convex environment, and the second involves multiple pursuers and an evader in a boundaryless environment. In the first game, we give a sufficient condition on the ratio of sensing to stepping radius of the players that guarantees capture. In the second, we determine the minimum probability of capture, which is a function of a novel pursuer formation and independent of the initial evader location. The sweep and pursuit phases reduce both games to previously studied problems with unlimited range sensing, and capture is achieved using available strategies. We obtain novel upper bounds on the capture time and present simulation studies that address the performance of the strategies under sensing errors, different ratios of sensing to stepping radius, greater evader speed, and a different number of pursuers.