FLOCKING OF A THERMODYNAMIC CUCKER-SMALE MODEL WITH LOCAL VELOCITY INTERACTIONS

In this paper, we study the flocking behavior of a thermodynamic Cucker–Smale model with local velocity interactions. Using the spectral gap of a connected stochastic matrix, together with an elaborate estimate on perturbations of a linearized system, we provide a sufficient framework in terms of initial data and model parameters to guarantee flocking. Moreover, it is shown that the system achieves a consensus at an exponential rate.

具有虚拟领导的Flocking聚类算法

该文提出一种改进的带虚拟领导的Flocking模型,并基于此模型开发了一种数据聚类算法。在此算法中,数据集中的数据点被考虑为可以在空间中移动的Agent,并且根据改进的模型,生成有权无向图。然后从数据集中选定一组虚拟领导,每个数据点与其中γ个虚拟领导建立连接。所有与这个数据点有连接的邻居,都通过一个势函数产生场,对这个数据点进行作用,此数据点将沿着所有场矢量叠加的方向移动一段距离。算法中,虚拟领导的加入有效减少了数据点,特别是邻居较少的数据点向某个中心收敛的时间。在所有数据点不断受到作用而移动的过程中,同类的数据点就会逐渐地聚集到一起,而不同类的数据点则相互远离,最后自动形成聚类。此算法的实验结果表明,数据点能合理有效地被聚类,并且算法具有较快的收敛速度,同时,与其他算法对比也验证了此算法的有效性。

基于加权准邻域和多虚拟领航者的Flocking算法

为解决Flocking模型中势函数没有考虑角度信息的问题,建立了基于加权准邻域势函数以及多虚拟领航的Flocking运动模型。首先,考虑运动的方向性,不同角度的邻居对智能体的影响不同,根据邻域角度信息设计基于加权势函数的控制器。然后,对单虚拟领航者的群智能体运动和两组群智能体受各自领航者引导混杂后又分离的运动过程进行了仿真。最后,通过误差分析证明所建立的Flocking运动模型符合群智能体的运动规律。

一种基于混合智能体系统的Flocking算法

对由多个具有二次积分动态、通信半径不同的智能体组成的混合群体跟随领航者取得flocking运动进行研究。针对不同智能体的通信半径存在个体差异这一现象,提出一种分散控制算法。该控制策略能够保证混合智能群体跟踪一个速度固定的虚拟领航者,形成紧凑、无碰撞的队列且各智能体的速度最终收敛于领航者的速度。最后给出了算法可行性的理论分析,并针对20个具有不同通信半径的智能体跟随一个速度固定的虚拟领航者的情形给出了计算机仿真,验证了算法的可行性。

基于flocking的多智能体群集与避障算法研究与仿真

多智能体运动规划问题逐渐成为智能体中研究的热点问题。多智能体运动规划主要包括路径规划、编队控制和避障避碰三种控制行为。主要介绍了基于分布式flocking的群集控制模式,实现了多智能体系统的集结与共识,使多智能体在运动过程中保持一致性,并为系统提供冗余性和结构的灵活性;同时对多智能体的避障控制和目标点控制开展了进一步的研究,解决群集避障问题,在复杂环境下成功抵达任务目标区域;最后通过仿真验证了该算法的合理性和有效性。

Stable Flocking Motion of Mobile Agents Following a Leader in Fixed and Switching Networks

Multiple mobile agents with double integrator dynamics, following a leader to achieve a flocking motion formation, are studied in this paper. A class of local control laws for a group of mobile agents is proposed. From a theoretical proof, the following conclusions are reached： （i） agents globally align their velocity vectors with a leader, （ii） they converge their velocities to the leaders velocity, （iii） collisions among interconnected agents are avoided, and （iv） agent＇s artificial potential functions are minimized. We model the interaction and/or communication relationship between agents by algebraic graph theory. Stability analysis is achieved by using classical Lyapunov theory in a fixed network topology, and differential inclusions and nonsmooth analysis in a switching network topology respectively. Simulation examples are provided.

群机器人Flocking行为研究综述

主要回顾了群机器人自组织flocking行为的研究。首先,介绍群机器人学的含义及关键特征;然后,指出flocking行为在生物学上及机器人学上的意义,之后对目前的研究现状进行介绍。最后,按照以下的三个分类对群机器人flocking行为进行总结:行为控制,硬件设备及交流策略。

一种带有变速虚拟领航者的flocking算法

对由多个具有不同通信半径的智能体组成的混合群体跟随领航者取得flocking运动进行研究。针对不同智能体的通信半径存在个体差异这一现象,提出一种分散控制算法。该控制策略能够保证混合智能群体跟踪一个速度变化的虚拟领航者,形成flocking运动。在最后给出计算机仿真。

Multiagent flocking with formation in a constrained environment

We consider the problem of controlling a group of mobile agents to form a designated formation while flocking within a constrained environment. We first propose a potential field based method to drive the agents to move in connection with their neighbors, and regulate their relative positions to achieve the specific formation. The communication topology is preserved during the motion. We then extend the method to flocking with environmental constraints. Stability properties are analyzed to guarantee that all agents eventually form the desired formation while flocking, and flock safely without collision with the environment boundary. We verify our algorithm through simulations on a group of agents performing maximum coverage flocking and traveling through an unknown constrained environment.

EXISTENCE AND UNIQUENESS OF ENTROPY SOLUTION TO PRESSURELESS EULER SYSTEM WITH A FLOCKING DISSIPATION

We study the existence and uniqueness problem for the nonhomogeneous pressureless Euler system with the initial density being a Radon measure. Our uniqueness result is obtained in the same space as the existence theorem. Besides, by counterexample we prove that Huang-Wang’s energy condition is also necessary for our nonhomogeneous system.

Flocking for swarm systems with fixed topology in a changing environment

This paper is mainly devoted to the flocking of a class of swarm with fixed topology in a changing environment. Firstly, the controller for each agent is proposed by employing the error terms between the state of the agent and the average state of its neighbors. Secondly, a sufficient condition for the swarm to achieve flocking is presented under assumptions that the gradient of the environment is bounded and the initial position graph is connected. Thirdly, as the environment is a plane, it is further proved that the velocity of each agent finally converges to the velocity of the swarm center although not one agent knows where the center of the group is. Finally, numerical examples are included to illustrate the obtained results.

基于Flocking的车辆编队分布式控制

完成基于Flocking的车辆编队模型的分布式控制。首先建立基于Hebb规则的Flocking运动模型,其次引入路线和面积势能控制完成控制器设计,然后以直线运动为例仿真验证基于Flocking的车辆编队模型,最后进行误差分析。建立的Flocking运动模型可以实现车辆编队的分布式控制。

Flocking for Leader-Following Multi-Agent Systems with Time-Varying Delay

This paper investigates the flocking problem in multi-agent system with time-varying delay and a virtual leader. Each agent here is subject to nonlinear dynamics. For the system, the corresponding algorithm with time-varying delay is proposed. Under the assumption that the initial network is connected, it is proved that the distance between agents is in the desired distance. The theoretical deduction shows that the stable flocking motion is achieved.

Flocking of multi-robot systems with connectivity maintenance on directed graphs

Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields （APF） which could guarantee the connectivity maintenance, col ision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that al robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.

Flocking behavior with multiple leaders and global trajectory

Aiming at the group of autonomous agents consisting of multiple leader agents and multiple follower ones,a flocking behavior method with multiple leaders and a global trajectory was proposed.In this flocking method,the group leaders can attain the information of the global trajectory,while each follower can communicate with its neighbors and corresponding leader but does not have global knowledge.Being to a distributed control method,the proposed method firstly sets a movable imaginary point on the global trajectory to ensure that the center and average velocity of the leader agents satisfy the constraints of the global trajectory.Secondly,a two-stage strategy was proposed to make the whole group satisfy the constraints of the global trajectory.Moreover,the distance between the center of the group and the desired trajectory was analyzed in detail according to the number ratio of the followers to the leaders.In this way,on one hand,the agents of the group emerge a basic flocking behavior; on the other hand,the center of the group satisfies the constraints of global trajectory.Simulation results demonstrate the effectiveness of the proposed method.

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

This paper presents a novel flocking algorithm based on a memory-enhanced disturbance observer.To compensate for external disturbances,a filtered regressor for the double integrator model subject to external disturbances is designed to extract the disturbance information.With the filtered regressor method,the algorithm has the advantage of eliminating the need for acceleration information,thus reducing the sensor requirements in applications.Using the information obtained from the filtered regressor,a batch of stored data is used to design an adaptive disturbance observer,ensuring that the estimated values of the parameters of the disturbance system equation and the initial value converge to their actual values.The result is that the flocking algorithm can compensate for external disturbances and drive agents to achieve the desired collective behavior,including virtual leader tracking,inter-distance keeping,and collision avoidance.Numerical simulations verify the effectiveness of the algorithm proposed in the present study.

Multi-boid Flocking with Formation Control Using Local Sensing and Communication

Behavior-based flocking has got remarkable attention in the recent past. The flocking algorithms can have inherent properties like organizing,healing and re-configuring for a distributed system. In this research we presented the emergent flocking behavior-based control. We defined the basis behavior and with variety of combination, and obtained a complex group behavior flocking. Unlike classical flocking, we implemented additional rules obstacle avoidance,formation and seek target which results in V-formation flocking while avoiding obstacles. We performed the visual simulation of our flocking algorithm using MATLAB. The results concluded that the multi-boid flock could successfully navigate to the target while avoiding collisions. This can be applied to areas where we need to maximize the coverage of sensors or minimize the risk of combative attack,both in military and civilian scenarios.

基于Shape Theory和Flocking的多智能体编队机动控制

多智能体编队机动控制技术是多智能体控制的一个重要子任务。该文提出了一种基于Shape Theory和Flocking的多智能体编队机动控制算法,该算法能够控制一组智能体在保持目标队形的情况下,顺利避开障碍物并到达目标位置。该算法根据可行路径设置相应的路径点和限制区域,使用基于Shape Theory的编队队形控制算法在每个限制区域中计算出满足目标队形约束的目标编队,再使用Flocking算法使智能体依次奔向这些目标位置。最后,通过仿真实验验证了该算法的有效性。

Flocking Control of a Group of Agents Using a Fuzzy-Logic-Based Attractive/Repulsive Function

In this study, a novel procedure is presented for control and analysis of a group of autonomous agents with point mass dynamics achieving flocking motion by using a fuzzy-logic-based attractive/repulsive function. Two cooperative control laws are proposed for a group of autonomous agents to achieve flocking formations related to two different centers (mass center and geometric center) of the flock. The first one is designed for flocking motion guided at mass center and the other for geometric center. A virtual agent is introduced to represent a group objective for tracking purposes. Smooth graph Laplacian is introduced to overcome the difficulties in theoretical analysis. A new fuzzy-logic-based attractive/repulsive function is proposed for separation and cohesion control among agents. The theoretical results are presented to indicate the stability (separation, collision avoidance and velocity matching) of the control systems. Finally, simulation example is demonstrated to validate the theoretical results.

Flocking fragmentation formulation for a multi-robot system under multi-hop and lossy ad hoc networks

We investigate the impact of network topology characteristics on focking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network,including the network's hop count features and information's successful transmission probability(STP).Specifically,we first propose a distributed communication calculation execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system,where focking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables.Then,we develop a fragmentation prediction model(FPM)to formulate the impact of hop count features on fragmentation for specific focking scenarios.This model identifies the critical system and network features that are associated with fragmentation.Further considering general focking scenarios affected by both hop count features and STP,we formulate the flocking fragmentation probability(FFP)by a data fitting model based on the back propagation neural network,whose input is extracted from the FPM.The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena.Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation,and several guidelines for constructing the multi-robot ad hoc network are concluded.