未知动态环境下非完整移动群机器人围捕

针对未知动态障碍物环境下非完整移动群机器人围捕,提出了一种基于简化虚拟受力模型的自组织方法.首先给出了个体机器人的运动方程,然后给出了未知动态环境下目标和动态障碍物的运动模型.通过对复杂环境下围捕行为的分解,抽象出简化虚拟受力模型,基于此受力模型,设计了个体运动控制方法,接着证明了系统的稳定性并给出了参数设置范围.不同情况下的仿真结果表明,本文给出的围捕方法可以使群机器人在未知动态障碍物环境下保持较好的围捕队形,并具有良好的避障性能和灵活性.最后分析了本文与基于松散偏好规则的围捕方法相比的优势.     

群机器人围捕多个入侵者的人工力矩方法

群机器人协作围捕多个入侵者的问题是一个富有理论和工程应用价值的问题.针对这一问题提出了一种基于人工力矩的自组织围捕方法.首先基于同盟、围捕曲线等概念给出了围捕机器人基于局部信息计算与调整吸引点的方法,来引导围捕机器人在向入侵者的运动过程中快速地形成理想的围捕队形.然后分别针对围捕机器人和入侵者设计了人工力矩运动控制器,...     

复杂环境下群机器人自组织协同多目标围捕

针对动态多目标围捕,提出了一种复杂环境下协同自组织多目标围捕方法.首先设计了多目标在复杂环境下的运动模型,然后通过对生物群体围捕行为的研究,构建了多目标简化虚拟受力模型.基于此受力模型和提出的动态多目标自组织任务分配算法,提出了群机器人协同自组织动态多目标围捕算法,这两个算法只需多目标和个体两最近邻位置信息以及个体面向多目标中心方向的两最近邻任务信息,计算简单高效,易于实现.接着获得了系统稳定时参数的设置范围.由仿真可知,所提的方法具有较好的灵活性、可扩展性和鲁棒性.最后给出了所提方法相较于其它方法的优势.     

基于模糊逻辑的多移动机器人自适应协作围捕

提出一种在未知动态环境下实现多移动机器人自适应协作围捕运动目标的整体方案,为成功实现围捕,设计了基于模糊逻辑的钳型夹击策略,模糊规则通过遗传算法学习获得.同时为躲避围捕过程中遇到的动态随机障碍,提出了基于碰撞风险的随机避障策略.围捕机器人的综合行为通过融合避障行为、合围行为和抓捕行为获得在MRS仿真环境下进行了模拟实验...     

动态环境下可扩展移动机器人群体的围捕控制

针对协作追逃问题的环境受限以及围捕者与目标的速度比率受限问题,提出了一种规模可扩展的机器 人群体围捕移动目标的切换式策略,该策略可有效完成动态环境中目标机器人速度无约束的围捕任务,即围捕机器 人通过数目优势进行协作围捕来克服其速度上的劣势以完成对目标机器人的围捕．围捕过程中,考虑了面向目标机 器人的虚拟势点子行为以及与邻居个体的位姿匹配行为（编队子行为）,在距离目标较远时位姿匹配子行为权值大 于虚拟势点子行为权值,而距离目标较近时则以虚拟势点子行为为主．仿真实验证明了所提解决方案的可行性和有 效性．     

感知范围受限的群机器人自主围捕算法

针对在有障碍物场地中感知范围受限的群机器人协同围捕问题,本文首先给出了机器人个体、障碍物、目标的模型,并用数学形式对围捕任务进行描述,在此基础上提出了机器人个体基于简化虚拟速度和基于航向避障的自主围捕控制律.基于简化虚拟速度模型的控制律使得机器人能自主地围捕目标同时保持与同伴的距离避免互撞;基于航向的避障方法提升了个体...     

群机器人自组织围捕多个入侵者的链阵方法

针对群机器人在二维给定区域自组织围捕多个入侵者问题提出了一种链阵方法.链阵中的机器人分为链首和链节,其中链首不但会环绕围捕目标运动而且能也只有它们能发布招募信息,以招募更多的机器人参与围捕同时又能使参与者不致太多.链节机器人则只需简单地跟随leader不停运动以形成链阵.机器人的运动由人工力矩运动控制器驱动,该控制器能使机器人在完成任务的同时而又不会发生碰撞.仿真结果表明,链阵方法可以根据实际需求动态调整链阵中的机器人数,能使围捕不同目标的机器人结成统一的链阵.链阵的不停移动则有利于机器人在数量较少时包围入侵者.     

平行导引律优化的多机器人免疫围捕算法

为了提高多机器人协作围捕效率,提出了基于平行导引律的多机器人免疫网络协作围捕算法。首先,将围捕者的运动策略作为抗体,逃逸者和目标区域的位置信息作为抗原,通过抗体和抗原的刺激和抑制来构建协作围捕免疫网络;然后,基于平行导引律定义了平行导引律调节因子;最后,通过抗体浓度自适应选择来完成围捕任务。数值测试表明,与其他算法相比,免疫围捕算法不仅保证了围捕成功率,而且所需步数平均减少21%,转角平均减少13%;而仿真平台的测试对比结果表明,围捕算法所需时间平均减少7%,转角平均减少24%,从而验证了基于平行导引律的多机器人免疫围捕算法的有效性。     

基于邻域跟随的群集系统分群控制算法

传统基于避撞、组队和聚集规则的个体运动协同算法具有内聚和速度一致趋势,群体在外部信息刺激下难以自发实施分群.为此,提出一种融合了邻域跟随行为的分布式协同控制算法.该算法在短距排斥、长距吸引和速度一致行为的基础上,引入个体对于其感知域内间距变化最快的邻居的跟随运动,并通过对跟随目标的动态更新,实现了外部信息作用下群体的自组织分群行为.仿真实验验证了算法的可行性和分群有效性.     

一种适合MMOG的多NPC协同围捕策略

针对MMOG中多NPC的协同围捕问题,本文提出一种基于运动学和几何学理论的NPC协同围捕策略.采用运动学方法构建个体NPC的运动模型,从而实现个体NPC在游戏中的自主运动控制,通过改变NPC的方向角和使用"正多边形队形法"实现多NPC对目标的靠近和包围.实验结果证明,该策略能使群体NPC成功的围捕目标,满足游戏的实时性、挑战性和真实感的需求.     

Coordination control design of heterogeneous swarm robots by means of task-oriented optimization

This paper is concerned with a framework to design self-organizing, self-reconfigurable robotic systems. We focus our attention on the algorithm of a multi-agent system called Swarm Chemistry, proposed by Sayama (Artif Life 15:105–114, 2009). In this model, a number of agents that have non-uniform kinetic properties coalesce into an excellent diversity of spatial structures and/or emergent behaviors, depending on the kinetic parameters provided. However, such bottom-up nature cannot be easily applied to the conventional and top-down design of artifacts. This paper presents a method of designing heterogeneous robotic swarms and finding solutions through a genetic algorithm. Simulation results with a few simple task examples demonstrate that the proposed framework allows us to acquire appropriate sets of kinetic parameters, i.e. recipes, creating swarm structures to perform a given task more effectively and efficiently. Such autonomous robots can be deployed for the purposes like disaster prevention, geographical survey, and subsea exploration.     

Autonomous Control Strategy of a Swarm System Under Attack Based on Projected View and Light Transmittance

In the study of a visual projection field with swarm movements,an autonomous control strategy is presented in this paper for a swarm system under attack.To ensure a fast swarm dynamic response and stable spatial cohesion in a complex environment,a new hybrid swarm motion model is proposed by introducing global visual projection information to a traditional local interaction mechanism.In the face of attackers,individuals move towards the largest free space according to the projected view of the environment,rather than directly in the opposite direction of the attacker.Moreover,swarm individuals can certainly regroup without dispersion after the attacker leaves.On the other hand,the light transmittance of each individual is defined based on global visual projection information to represent its spatial freedom and relative position in the swarm.Then,an autonomous control strategy with adaptive parameters is proposed according to light transmittance to guide the movement of swarm individuals.The simulation results demonstrate in detail how individuals can avoid attackers safely and reconstruct ordered states of swarm motion.     

Navigation of carlike robots in an extended dynamic environment with swarm avoidance

In this paper, we propose a new solution to the motion planning and control problem for a team of carlike mobile robots traversing in an extended dynamic environment. Motivated by the emerging necessity to avoid or defend against a swarm of autonomous robots, the wide array of obstacles in this dynamic environment for the first time includes a swarm of boids governed separately by a system of ordinary differential equations. The swarm exhibits collective emergent behaviors, whereas the carlike mobile robots safely navigate to designated targets. We present a set of nonlinear continuous controllers for obstacle, collision, and swarm avoidance. The controllers provide a collision‐free trajectory within a constrained workspace cluttered with various fixed and moving obstacles while satisfying the nonholonomic and kinodynamic constraints associated with the vehicular robotic system. An advantage of the proposed method is the ease in deriving the acceleration‐based control laws from the Lyapunov‐based control scheme. The effectiveness of the control laws is demonstrated via computer simulations. The novelty of this paper lies in the simplicity of the controllers and the ease in the treatment of an extended dynamic environment, which includes swarm avoidance.     

A swarm system design based on a modified particle swarm algorithm for a self-organizing scheme

In this paper, an attempt has been made to incorporate some special features in the conventional particle swarm optimization (PSO) technique for decentralized swarm agents. The modified particle swarm algorithm (MPSA) for the self-organization of decentralized swarm agents is proposed and studied. In the MPSA, the update rule of the best agent in a swarm is based on a proportional control concept and the fitness of each agent is evaluated on-line. The virtual zone is developed to avoid conflict among the agents. In this scheme, each agent self-organizes to flock to the best agent in a swarm and migrate to a moving target while avoiding obstacles and collision among agents. Aided by these advantages such as cooperative group behaviors, flexible formation and scalability, the proposed approach enables large-scale swarm agents to distribute themselves optimally for a given task. The simulation results have shown that the proposed scheme effectively constructs a self-organized swarm system with the capability of flocking and migration.     

大规模联合采集智能群体的协调跟踪模型及稳定性分析

An improved cooperative tracking model is proposed, which is based on the local information between mutually observable individuals with global object information, and this model is used for scalable social foraging swarm. In this model, the “follower” individuals in the swarm take the center of the minimal circumcircle decided by the neighbors in the positive visual set of individual as its local object position. We study the stability properties of cooperative tracking behavior of social foraging swarm based on Lyapunov stability theory. Simulations show that the stable cooperative tracking behavior of the global social foraging swarm can be achieved easily, and beautiful scalability emerge from the proposed model for social foraging swarm.     

A new algorithm inspired in the behavior of the social-spider for constrained optimization

During the past decade, solving constrained optimization problems with swarm algorithms has received considerable attention among researchers and practitioners. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO-C) is proposed for solving constrained optimization tasks. The SSO-C algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. For constraint handling, the proposed algorithm incorporates the combination of two different paradigms in order to direct the search towards feasible regions of the search space. In particular, it has been added: (1) a penalty function which introduces a tendency term into the original objective function to penalize constraint violations in order to solve a constrained problem as an unconstrained one; (2) a feasibility criterion to bias the generation of new individuals toward feasible regions increasing also their probability of getting better solutions. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. Simulation and comparisons based on several well-studied benchmarks functions and real-world engineering problems demonstrate the effectiveness, efficiency and stability of the proposed method.     

Swarm stability for high-order linear time-invariant singular multi-agent systems

Swarm-stability and swarm-stabilisation problems for high-order linear time-invariant singular multi-agent systems with directed networks are investigated. First, necessary and sufficient conditions for swarm stability and asymptotic swarm stability are proposed, which are independent of the dimensions of Jordan blocks of the Laplacian matrix of the interaction topology. Then, an approach is given to determine the absolute motion as a whole, and it is shown that the absolute motion is completely determined by initial states if the interaction topology is balanced. Furthermore, an approach is presented to determine gain matrices for asymptotic swarm stabilisation. Moreover, leader-following swarm-stability and swarm-stabilisation problems are investigated. Finally, numerical examples are given to demonstrate theoretical results.