Aggregation, a widely observed behavior in social insects, is the gathering of individuals on any location or on a cue. The former being called the self-organized aggregation, and the latter being called the cue-based aggregation. One of the fascinating examples of cue-based aggregation is the thermotactic behavior of young honeybees. Young honeybees aggregate on optimal temperature zones in the hive using a simple set of behaviors. The state-of-the-art cue-based aggregation method BEECLUST was derived based on these behaviors. The BEECLUST method is a very simple, yet a very capable method that has favorable characteristics such as robustness to noise and simplicity to apply. However, the BEECLUST method does not perform well in low robot densities. In this article, inspired by the navigation techniques used by ants and bees, a self-adaptive landmark-based aggregation method is proposed. In this method, robots use landmarks in the environment to locate the cue once they “learn” the relative position of the cue with respect to the landmark. With the introduction of an error threshold parameter, the method also becomes adaptive to changes in the environment. Through systematic experiments in kinematic and realistic simulators with different parameters, robot densities, and cue sizes, it was observed that using the information of the environment makes the proposed method to show better performance than the BEECLUST in all the settings, including low robot density, high noise, and dynamic conditions.

聚集是社交昆虫中一种广泛观察到的行为，是个体在任何位置或某个提示上的聚集。前者称为自组织聚合，后者称为基于提示的聚合。基于提示的聚集的引人入胜的例子之一是年轻蜜蜂的热战术行为。幼蜜蜂通过一组简单的行为聚集在蜂巢中的最佳温度区域。基于这些行为，得出了基于最新提示的聚合方法BEECLUST。BEECLUST方法是一种非常简单但功能强大的方法，具有良好的特性，例如对噪声的鲁棒性和易于应用。但是，BEECLUST方法在低机器人密度下效果不佳。在本文中，受蚂蚁和蜜蜂使用的导航技术的启发，提出了一种基于自适应地标的聚合方法。在这种方法中，机器人一旦“学习”提示相对于地标的相对位置，便会使用环境中的地标来定位提示。通过引入错误阈值参数，该方法还可以适应环境的变化。通过在具有不同参数，机器人密度和提示大小的运动学和现实模拟器中进行系统性实验，可以观察到，使用环境信息使所提出的方法在所有设置（包括低机器人密度）下均表现出比BEECLUST更好的性能，高噪音和动态条件。机器人一旦“学习”提示相对于地标的相对位置，便会使用环境中的地标来定位提示。通过引入错误阈值参数，该方法还可以适应环境的变化。通过在具有不同参数，机器人密度和提示大小的运动学和现实模拟器中进行系统性实验，可以观察到，使用环境信息使所提出的方法在所有设置（包括低机器人密度）下均表现出比BEECLUST更好的性能，高噪音和动态条件。机器人一旦“学习”提示相对于地标的相对位置，便会使用环境中的地标来定位提示。通过引入错误阈值参数，该方法还可以适应环境的变化。通过在具有不同参数，机器人密度和提示尺寸的运动学和现实模拟器中进行系统性实验，可以观察到，使用环境信息使所提出的方法在所有设置（包括低机器人密度）下均表现出比BEECLUST更好的性能，高噪音和动态条件。