Swarm robots, inspired by the emergence of animal herds, are robots that assemble a large number of modules and self-organize themselves to form specific morphologies and exhibit specific functions. These modular robots perform relatively simple actions and controls, and create macroscopic morphologies and functions through the interaction of a large number of modular robots. This research focuses on such self-organizing robots or swarm robots. The proposed algorithm is a model that applies the Turing pattern, one of the self-organization models, to make a group of modules accumulate and stay within a certain region. The proposed method utilizes the area within the spots of the Turing pattern as the aggregation region of the modules. Furthermore, it considers the value corresponding to the concentration distribution within the spotted pattern of the Turing pattern model (referred to as the potential value in this research), identifies the center of the region (spotted pattern), and makes it the center of the module group. By controlling the modules in the direction of the higher potential value, it succeeds in maintaining the shape of the module group as a whole while moving. The algorithm was validated using a two-dimensional simulation model. The unit module robot was assumed to have the following properties: 1) limited self-drive, 2) no module identifier, 3) information exchange only with adjacent modules, 4) no coordinate system, and 5) only simple arithmetic and memory functions. Using these modules, the devised algorithm was able to achieve not only the creation of static forms but also the realization of the following movements: 1) modules accumulate and grow, 2) modules move to the light source, 3) exit the gap while maintaining its shape, and 4) self-replication.

翻译：受动物群体涌现现象启发的集群机器人，是指通过组装大量模块并使其自组织形成特定形态并展现特定功能的机器人。这些模块化机器人执行相对简单的动作与控制，并通过大量模块机器人间的相互作用创造出宏观的形态与功能。本研究聚焦于此类自组织机器人或集群机器人。所提出的算法应用了自组织模型之一的图灵斑图模型，使模块群能够在特定区域内聚集并驻留。该方法利用图灵斑图中的斑点区域作为模块的聚集区，并通过分析图灵斑图模型中斑点图案对应的浓度分布值（本研究中称为势能值）来识别区域（斑点图案）中心，并将其设定为模块群的中心。通过控制模块向势能值更高的方向移动，该算法成功实现了模块群在移动过程中整体形态的维持。研究使用二维仿真模型对算法进行了验证。假设单元模块机器人具有以下特性：1）有限的自主驱动能力，2）无模块标识符，3）仅与相邻模块进行信息交换，4）无坐标系，5）仅具备简单算术与记忆功能。基于这些模块，所设计的算法不仅能够实现静态形态的构建，还能完成以下运动：1）模块聚集与生长，2）模块向光源移动，3）保持形态穿越缝隙，4）自我复制。