Survey on Modeling Method of Spatial Cooperative Behavior of Swarm Robots

doi:10.3778/j.issn.1673-9418.2201057

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1706-1726.DOI: 10.3778/j.issn.1673-9418.2201057

• Surveys and Frontiers • Previous Articles Next Articles

Survey on Modeling Method of Spatial Cooperative Behavior of Swarm Robots

ZHAO Yue, SHEN Bo(), WU Wenliang, ZHOU Xingshe

School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710129, China

Received:2022-01-18 Revised:2022-04-01 Online:2022-08-01 Published:2022-08-19
About author:ZHAO Yue, born in 1986, Ph.D. candidate. Her research interest is the evaluation of swarm intelligence.
SHEN Bo, born in 1985, Ph.D., associate professor. His research interests include cyber-physical systems, Internet of things, etc.
WU Wenliang, born in 1989, Ph.D. candidate. His research interest is the evaluation of swarm intelligence.
ZHOU Xingshe, born in 1955, professor，Ph.D. supervisor. His research interests include embedded computing and distributed computing, cyber-physical systems, etc.
Supported by:
the National Science and Technology Innovation Special Zone Project;the National Natural Science Foundation of China(61902295)

集群机器人空间协作行为模型构建方法综述

赵月, 沈博(), 武文亮, 周兴社

西北工业大学计算机学院，西安 710129

通讯作者: +E-mail: shen@nwpu.edu.cn.
作者简介:赵月（1986—），女，博士研究生，主要研究方向为群体智能评价。
沈博（1985—），男，博士，副教授，主要研究方向为信息物理融合系统、物联网等。
武文亮（1989—），男，博士研究生，主要研究方向为群体智能评价。
周兴社（1955—），男，教授，博士生导师，主要研究方向为嵌入式计算与分布计算、信息物理融合系统等。
基金资助:
国家部委科技创新特区计划课题基金;国家自然科学基金(61902295)

Abstract

Abstract:

Swarm robots are composed of a certain number of physical robots. Through interaction and cooperation, individuals in this kind of system can sprout robust, scalable and flexible swarm intelligence behavior. Behavior modeling of swarm robots is one of the basic methods for scientific analysis of such systems, and it is also an important basis for its capability evaluation. The research and analysis of behavior model of swarm robots is helpful to understand its behavior mechanism. Firstly, this paper summarizes the main characteristics of swarm robots, and lists and summarizes several typical spatial cooperation behaviors of swarm robots. On this basis, the spatial cooperation behavior of swarm robots is classified, and the modeling methods of spatial cooperation behavior of cluster robots are summarized and expounded, including graph theory, bionic model, dynamic model and learning model. Various modeling methods are analyzed and compared from the multi-dimensional aspects of method overview, application examples and applicable scenes. Finally, the problems of spatial cooperative behavior modeling of swarm robots to be further studied are put forward, and possible research directions in the future are prospected, so as to better support the evaluation and optimization design of swarm robots. The purpose of this paper is to enable relevant researchers to comprehensively and systematically understand the spatial cooperative behavior modeling method of swarm robots.

Key words: swarm robots, swarm behavior, modeling method, bionic model, learning model

摘要：

集群机器人是由一定数量的物理机器人组成，该系统中的个体通过交互与协作，可涌现出鲁棒、可扩展、灵活的群体智能行为。集群机器人行为建模是科学分析此类系统的基本方法之一，也是其能力评估的重要基础，研究与分析集群机器人行为模型有助于理解其行为机理。首先对集群机器人的主要特性进行总结，列举了几种典型的集群机器人空间协作行为;在此基础上，对集群机器人空间协作行为进行分类，重点总结和阐述了集群机器人空间协作行为的建模方法，包括图论、仿生模型、动力学模型以及学习模型，从方法概述、应用实例与适用场景等多维度对各类建模方法进行了分析与比较。最后提出了集群机器人空间协作行为建模有待进一步深化研究的问题，并对其未来可能的研究方向进行了展望，以更好地支撑集群机器人系统评估与优化设计。该研究旨在使相关研究人员全面系统地理解集群机器人空间协作行为建模方法。

关键词: 集群机器人, 群体行为, 建模方法, 仿生模型, 学习模型

CLC Number:

TP242

ZHAO Yue, SHEN Bo, WU Wenliang, ZHOU Xingshe. Survey on Modeling Method of Spatial Cooperative Behavior of Swarm Robots[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1706-1726.

赵月, 沈博, 武文亮, 周兴社. 集群机器人空间协作行为模型构建方法综述[J]. 计算机科学与探索, 2022, 16(8): 1706-1726.

Figures/Tables 14

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模型	模型优点	适用场景
ACO	正反馈机制，搜索分布式，易于寻找到全局最优解	收敛速度慢，不恰当的初始参数会减弱算法的寻优能力
PCO	优化速度快，简单易实现	易早熟收敛，局部寻优能力差，对于离散的优化问题处理不佳
SA	局部搜索能力强，计算过程简单，可用于求解复杂的非线性优化问题	全局搜索能力差，容易受到参数的影响，收敛速度慢，执行时间长

模型	模型优点	适用场景
ACO	正反馈机制，搜索分布式，易于寻找到全局最优解	收敛速度慢，不恰当的初始参数会减弱算法的寻优能力
PCO	优化速度快，简单易实现	易早熟收敛，局部寻优能力差，对于离散的优化问题处理不佳
SA	局部搜索能力强，计算过程简单，可用于求解复杂的非线性优化问题	全局搜索能力差，容易受到参数的影响，收敛速度慢，执行时间长

模型	建模维度	模型粒度	模型特点
Vicsek	二维	局部	简单、有效、自驱动
C-S	二维	全局	基于Vicsek，算法扩展丰富
Couzin	二维	全局	规则和算法简单

模型	建模维度	模型粒度	模型特点
Vicsek	二维	局部	简单、有效、自驱动
C-S	二维	全局	基于Vicsek，算法扩展丰富
Couzin	二维	全局	规则和算法简单

方程	模型粒度	模型特点
速率方程	全局	可描述平均量，以数学的角度，以统计方法表述宏观行为
扩散方程	全局	由于方程的非线性项，可使集群形成丰富的图案
Langevin和Fokker- Planck方程	全局	可对空间进行建模

Survey on Modeling Method of Spatial Cooperative Behavior of Swarm Robots

集群机器人空间协作行为模型构建方法综述

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建模方法		模型粒度	适用场景	模型特点	建模难度
图论	图	宏观	编队、围捕、避障、导航、覆盖等	已开发出众多完善算法，可用已有算法对集群机器人建模	中
	有限状态机	宏观/微观	聚集、搜索、编队等	简单易用，状态间的关系直观明了，适用于规模小的集群	低
	马尔可夫链	宏观	聚集、覆盖、避障等	可为不确定性提供理论支撑，需要了解集群状态变化的各种概率	中
仿生模型	ACO、PCO、SA	宏观	导航、编队、集群运动等	建立符合实际问题的仿生规则难度较高，但是解决问题较一般的搜索方法更高效	高
仿生模型	蜜蜂聚集	宏观	聚集	无位置通信内存要求，可用于异构集群	低
动力学模型	人工势场	宏观	覆盖、分散、编队等	在聚集上有良好的稳定性，实时性强	中
	SCA模型	微观	群集、编队等	基于定义的模型自驱动，简单有效	低
	微分方程	宏观	导航、编队、围捕、集群运动等	理论上可对任何行为进行建模，但是求解难度高	高
学习模型	人工进化	宏观/微观	编队、分散、导航等	对环境的适应性高，但是无法保证可扩展性	中
学习模型	强化学习	宏观/微观	群集、导航、编队等	适用于有全局目标、环境复杂、智能水平要求高的场景	高