Multi-Variable Flocking Control for Multi-Agent Systems via a Received Signal Strength Indicator

A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities,motion directions,and positions of agents.A received signal strength indicator (RSSI) is applied as a variable to estimate the inter-distance between agents.A key parameter that contains the local information of agents is defined,and a multi-variable controller is proposed based on the parameter.For the position control of agents,the RSSI is introduced to substitute the distance as a control variable in the systems.The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments,and it can shun the shortage of the limit of sensors.Simulation studies demonstrate the effectiveness of the proposed control approach.     

一种数字角色组群快速模拟方法

动态“组群”是人工智能中角色仿真模拟的重要效果，可应用于计算机（微生物）仿生、大型虚拟场景的多角色模拟等。本文讨论并给出了基于自定义对象的“组群”效果的仿真模拟方法，并给出了该方法的程序效果验证，从而有效应用于多角色并发模拟工作。     

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.     

A fuzzy-rule-based Couzin model

This paper proposes a modified Couzin and velocity-adaptive Couzin flocking model based on fuzzy rules. In the models, agents update their positions through a fuzzy-rule-based decision-making scheme in three perception zones. Stability of the systems are guaranteed. Simulations demonstrate that the convergence probability and relative converging size are both improved.     

丙烯酸酯类植绒粘合剂的合成及应用

采用乳液聚合的方式,以丙烯酸甲酯、丙烯酸乙酯、丙烯酸丁酯、丙烯酸、丙烯腈、苯乙烯、N-羟甲基丙烯酰胺为单体合成了丙烯酸酯植绒粘合剂。对引发剂的分配比例、双滴加温度及时间和追加后保温温度及时间等制备工艺条件进行了研究,确定了最佳制备工艺。所制备的产品具有良好的干摩擦牢度、湿摩擦牢度、抗高温泛黄性、耐沾污性等应用性能。     

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.     

基于移动障碍的MWSN节点协同转向避障方法

针对传统蜂拥控制模型协同避障的研究,笔者曾对其做出了改进,并加入了Steer to Avoid避障法则,通过仿真表明,它能够有效提高避开静止障碍的效率。该模型用于具有移动障碍的环境时,若障碍的运动方向与节点的判断方向同向,可能会与障碍物保持相对静止,从而大幅度降低避障效率。对Steer to Avoid进一步改进,提出一个新的针对移动障碍物的避障模型。当障碍物的运动趋势和节点的Steer to Avoid转向判断相同而且两者的速度较为接近时,节点将向着障碍物运动的相反方向运动。提出了对障碍物的移动预判。仿真实验结果表明,与传统两个模型相比,该模型在平均速率和时间效率上有显著提高,并且适用于避开未知的移动凸形障碍。     

Flocking techniques to naturally support navigation in large and open virtual worlds

This paper proposes and explores the use of flocking techniques to naturally support navigation in large and open virtual environments. The proposed approach comprises both a flocking algorithm (which simulates the behavior of a flock of virtual beings) and a control algorithm (which manages the navigation information and parameterizes the flocking behavior). The model was developed and evaluated through a user study which investigated whether the addition of the proposed model has an effect on the user experience when exploring a virtual environment. In the research the experiment group explored a virtual environment where the proposed flocking model was added to suggest places to visit. The participants were not informed about the guiding abilities of the virtual animals. The control group explored the same virtual environment without the animal flock. The impact of both cases was compared. The results indicate that the proposed model is adequate to suggest places to visit in large and open virtual worlds.     

一种数字角色组群快速模拟方法

动态"组群"是人工智能中角色仿真模拟的重要效果,可应用于计算机(微生物)仿生、大型虚拟场景的多角色模拟等。本文讨论并给出了基于自定义对象的"组群"效果的仿真模拟方法,并给出了该方法的程序效果验证,从而有效应用于多角色并发模拟工作。     

Topology-preserving flocking of nonlinear agents using optimistic planning

We consider the generalized flocking problem in multiagent systems, where the agents must drive a subset of their state variables to common values, while communication is constrained by a proximity relationship in terms of another subset of variables. We build a flocking method for general nonlinear agent dynamics, by using at each agent a near-optimal control technique from artificial intelligence called optimistic planning. By defining the rewards to be optimized in a well-chosen way, the preservation of the interconnection topology is guaranteed, under a controllability assumption. We also give a practical variant of the algorithm that does not require to know the details of this assumption, and show that it works well in experiments on nonlinear agents.