邻域交互结构优化的多智能体快速蜂拥控制算法

针对多智能体系统在动态演化过程中容易出现的"局部聚集"现象,融 合复杂网络中的拓扑结构优化理论与多智能体系统协调蜂拥控制研究,提出了一种基 于邻域交互结构优化的多智能体快速蜂拥控制算法.该算法首先从宏观上分析多智 能体的局部聚集现象,利用社团划分算法将局部相对密集的多个智能体聚类成一个 社团,整个多智能体系统可以划分成多个相对稀疏的社团,并为每个社团选择度 最大的个体作为信息智能体,该个体可以获知虚拟领导者信息;随后从多智能体 系统中不同社团相邻个体间的局部交互结构入手,取消社团间相邻个体的交 互作用,设计仅依赖于社团内部邻居个体交互作用的蜂拥控制律;理论分 析表明,只要每个社团存在一个信息智能体,在虚拟领导者的引导作用下,整个多 智能体系统就可以实现收敛的蜂拥控制行为;仿真实验也证实了对多智 能体系统进行邻域交互结构优化可以有效提高整个系统的收敛速度.     

多智能体多目标跟踪算法研究

研究了多智能体同时跟踪多个目标的群集算法,采用智能体的编号对目标数求余数的方法来确定各个智能体的跟踪目标。对于势场函数,根据智能体目标是否相同建立了两种势场函数,并在此基础上确定动态系统的控制输入,使相同目标的智能体在跟踪目标的过程中形成群集,而不同目标的智能体相互分离;用李亚普诺夫稳定性理论分析了算法的收敛性。仿真结果表明,所提算法可有效解决多目标跟踪问题。     

一种目标监测的移动传感器网络覆盖分布式优化算法

针对移动传感器网络中目标监测的节点部署问题,为保证在无覆盖漏洞的同时减少覆盖冗余,以六边形棋盘结构(HTL)为网络的目标部署结构,提出一种基于群集控制的分布式部署算法.该方法只需目标的相对方向和邻居节点的相对位置、速度信息,可不依赖于通信.仿真结果表明,所提出的算法对静止和运动目标均有效,与基于虚拟力的算法相比所需信息更少,部署更均匀,对HTL的逼近效果更好,覆盖更优.     

Flocking for multi-agent systems with optimally rigid topology based on information weighted Kalman consensus filter

This paper investigates the leader-follower flocking problem of multi-agent systems. The leader with input noise is estimated by a proposed continuous-time information weighted Kalman consensus filter (IWKCF) for agents. A novel distributed flocking algorithm based on the IWKCF is further presented to make agents achieve flocking to the leader. It is shown that the proposed flocking algorithm based on the continuous-time IWKCF is asymptotically stable. Applying the topology optimization scheme, the communication complexity of system topologies of multi-agent systems is effectively reduced. Finally, simulations are provided to demonstrate the effectiveness of the proposed results.     

Dynamic target tracking and observing in a mobile sensor network

This paper presents novel approaches to (1) the problem of flocking control of a mobile sensor network to track and observe a moving target and (2) the problem of sensor splitting/merging to track and observe multiple targets in a dynamic fashion. First, to deal with complex environments when the mobile sensor network has to pass through a narrow space among obstacles, we propose an adaptive flocking control algorithm in which each sensor can cooperatively learn the network’s parameters to decide the network size in a decentralized fashion so that the connectivity, tracking performance and formation can be improved. Second, for multiple dynamic target tracking, a seed growing graph partition (SGGP) algorithm is proposed to solve the splitting/merging problem. To validate the adaptive flocking control we tested it and compared it with the regular flocking control algorithm. For multiple dynamic target tracking, to demonstrate the benefit of the SGGP algorithm in terms of total energy and time consumption when sensors split, we compared it with the random selection (RS) algorithm. Several experimental tests validate our theoretical results.     

融合路径跟踪模式的多移动机器人有序化群集运动控制

针对多移动机器人群集运动的实现及其避障问题，提出了一类融合路径跟踪模式的有序化群集运动控制算法．该算法将有leader的群集运动模式与队列运动模式相结合，实现了多机器人系统快速聚合行为与有效避障行为的统一．移动机器人群仿真实验验证了该方法的有效性．     

基于局部估计的功率驱动多智能体网络牵制蜂拥控制算法

针对复杂功率驱动系统控制中现有牵制控制策略在保持拓扑结构连通性和降低功率消耗方面存在的不足,提出一种基于局部估计的功率驱动多智能体网络的牵制蜂拥控制算法。该算法首先利用幂迭代一致估计算法动态估计多智能体网络的代数连通度;然后根据代数连通度的局部估计值及最大功率约束自适应调整个体发射半径,以保持多智能体网络在演化过程中拓扑结构的连通性,并有效降低功率消耗;最后通过仿真实验验证了所提出控制算法的有效性。     

Flocking shape analysis of multi-agent systems

In this paper,we consider the shape control in flocking behavior of a multi-agent system with a virtual leader.Besides the traditional flocking control terms,which include a gradient-based term,a velocity consensus term and a navigational feed-back in general,a new piecewise smooth neighbor-based local controller is added to regulate the configuration to the desired flocking shape.All agent velocities approach the desired velocity asymptotically,while collisions among agents can be avoided.Furthermore,based...     

Robust integral of sign of error-based distributed flocking control of double-integrator multi-agent systems with a varying virtual leader

This article investigates the flocking control problem of double-integrator multi-agent systems with a virtual leader subject to unknown external disturbances. A robust integral of sign of error (RISE) based control method is leveraged to design a distributed flocking controller with advantages of zero initial input value and continuous control input. By means of a new second-order differential virtual potential field function, and the navigational feedback from a virtual leader, the proposed flocking controller assures agents of velocity consensus with the virtual leader and a quasi -lattice formation within a circular neighborhood centered on the virtual leader. Moreover, this algorithm guarantees collision avoidance and connectivity preservation of a proximity-induced communication topology. Numerical simulations of the algorithm are provided to illustrate the effectiveness of the proposed flocking algorithm.     

Finite-time circle surrounding control for multi-agent systems

In this paper, a finite-time circle surrounding control problem for multi-agent systems is investigated. The objective is to make all the surrounding agents eventually distribute on a circle uniformly and all the target agents surrounded by this circle in the finite time. Both a distributed estimator and a distributed control protocol are presented to achieve such a goal. Firstly, an estimator of the targets’ geometric center is used to estimate the center of targets. Secondly, a distributed control protocol is proposed for each surrounding agent by using the value of the estimator to guarantee all the surrounding agents uniformly distribute on a circle and all the target agents are surrounded by this circle in the finite time. Then it is proved that the control goal can be achieved in the finite time. Finally, a simulation example is given to show the effectiveness of the proposed method.     

Multi-target consensus circle pursuit for multi-agent systems via a distributed multi-flocking method

This paper studies the multi-target consensus pursuit problem of multi-agent systems. For solving the problem, a distributed multi-flocking method is designed based on the partial information exchange, which is employed to realise the pursuit of multi-target and the uniform distribution of the number of pursuing agents with the dynamic target. Combining with the proposed circle formation control strategy, agents can adaptively choose the target to form the different circle formation groups accomplishing a multi-target pursuit. The speed state of pursuing agents in each group converges to the same value. A Lyapunov approach is utilised to analyse the stability of multi-agent systems. In addition, a sufficient condition is given for achieving the dynamic target consensus pursuit, and which is then analysed. Finally, simulation results verify the effectiveness of the proposed approaches.     

融合沿墙行为的多移动机器人有序化多模态群集运动控制

一类基于势场原理的群集控制理论正逐步应用于多agent(智能体)/机器人稳定协同运动中.针对群集运动系统在非规则障碍物环境中运行时易出现的局部极小问题,引入基于行为的机器人学理念,构成多移动机器人多模态群集控制系统.在此框架内,仿生的动物沿端行为与有序化群集运动控制策略相融合,实现了多移动机器人系统快速聚合行为与高效避障行为的统一.移动机器人仿真实验验证了该方法的有效性.     

Flocking of Multi-Agents With a Virtual Leader

All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multi-agent systems. Under these assumptions, Olfati-Saber in a recent IEEE Transactions on Automatic Control paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multi-agent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the Olfati-Saber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the Olfati-Saber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given.      

基于蜂拥控制的移动传感器网络目标跟踪算法

针对移动传感器网络中的目标跟踪问题, 以及现有控制策略在保持网络拓扑结构连通性和降低能量消耗方面存在的不足, 提出一种基于蜂拥控制的移动传感器网络目标跟踪算法. 首先, 利用网络中部分节点检测目标, 并使用卡尔曼一致性滤波算法估计目标的状态, 在获得比较精确的估计状态的同时降低能量消耗; 然后, 在蜂拥控制下传感器网络始终保持拓扑结构连通性和目标对网络可见, 同时避免节点之间发生碰撞. 仿真结果验证了所提出算法的有效性.

     

基于社区划分的多智能体网络快速蜂拥控制

针对复杂网络社区特性对多智能体系统协同控制效率的影响,面向具有ER(Erdos-renyi)网络或BA (Barabasi-albert)网络性质的多智能体系统,提出一种基于社区划分的快速蜂拥控制算法.该算法充分考虑社区内个体的相对密集特性,通过在社区间引入虚拟领导者作用,避免系统在演化过程中因通信受限而导致的“分块”现象,可有效提高系统拓扑的代数连通度.仿真结果表明:具有相应性质的多智能体系统蜂拥行为的收敛速度与ER和BA网络的平均度以及BA网络度分布的幂指数正相关;优化社区个数有利于提高蜂拥收敛速度.     

Flocking of Multi-agent Systems with Unknown Nonlinear Dynamics and Heterogeneous Virtual Leader

This paper investigates the flocking control of multi-agent systems with unknown nonlinear dynamics while the virtual leader information is heterogeneous. The uncertain nonlinearity in the virtual leader information is considered, and the weaker constraint on the velocity information measurements is assumed. In addition, a bounded assumption on the unknown nonlinear dynamics is also considered. It is weaker than the Lipschitz condition adopted in the most flocking control methods. To avoid fragmentation, we construct a new potential function based on the penalty idea when the initial network is disconnected. A dynamical control law including a adjust parameter is designed to achieve the stable flocking. It is proven that the velocities of all agents approach to consensus and no collision happens between the mobile agents. Finally, several simulations verify the effectiveness of the new design, and indicate that the proposed method has high convergence and the broader applicability in practical applications with more stringent restrictions.

     

Coverage tracking control for multiple cooperative agents

In this article, we consider coverage problem of a moving target by multiple mobile agents. The mobile agents with global sensing areas and limited actuation region are designed to track down a target with estimation error with the help of flocking and coverage. In order to capture the target, its location and velocity are first estimated. With observer-based estimation combined with flocking control, respectively, the group of agents can catch the target by a coverage tracking policy in finite time.     

An overview on optimal flocking

The decentralized aggregate motion of many individual robots is known as robotic flocking. The study of robotic flocking has received considerable attention in the past twenty years. As we begin to deploy flocking control algorithms on physical multi-agent and swarm systems, there is an increasing necessity for rigorous promises on safety and performance. In this paper, we present an overview the literature focusing on optimization approaches to achieve flocking behavior that provide strong safety guarantees. We separate the literature into cluster and line flocking, and categorize cluster flocking with respect to the system-level objective, which may be realized by a reactive or planning control algorithm. We also categorize the line flocking literature by the energy-saving mechanism that is exploited by the agents. We present several approaches aimed at minimizing the communication and computational requirements in real systems via neighbor filtering and event-driven planning, and conclude with our perspective on the outlook and future research direction of optimal flocking as a field.     

Multi-agent flocking under topological interactions

In this paper, we consider a multi-agent system consisting of mobile agents with second-order dynamics. The communication network is determined by the so-called topological interaction rule: agents interact with a fixed number of their closest neighbors. This rule comes from observations of real flock of starlings. The non-symmetry of the interactions adds to the difficulty of the analysis. The goal of this paper is to determine practical conditions (on the initial positions and velocities of agents) ensuring that the agents asymptotically agree on a common velocity (i.e. a flocking behavior is achieved). For this purpose, we define a notion of hierarchical structure in the interaction graph which allows to establish such conditions, building upon previous work on multi-agent systems with switching communication networks. Though conservative, our approach gives conditions that can be verified a priori. Our result is illustrated through simulations.     

基于自适应滑模的不确定Euler-Lagrange多智能体系统抗扰动蜂拥控制

针对具有参数不确定性和未知外部扰动的Euler-Lagrange多智能体系统,设计一种基于自适应滑模控制的分布式蜂拥算法.该算法使用自适应滑模控制和自适应控制律分别补偿未知的外部扰动与模型中可线性参数化回归的不确定项,从而在实现蜂拥控制的同时,避免智能体对外部扰动先验知识的要求.理论分析表明,在多智能体达成蜂拥的同时,算法保证滑模的自适应增益有界.此外,所提出的算法同时考虑虚拟领导者追踪与基于目标区域的跟踪问题,并给出碰撞避免的条件.最后,通过算例仿真验证所提出算法的有效性.