The paper considers a group of animates (bio-inspired abstract models or technical devices) that use the visual analyzer described by the authors earlier. The visual analyzer recognizes the scene observed by the animat, determines the class of the situation and selects the corresponding behavior. The analyzer is based on the" language of poses" , borrowed from some species of ants, which allows them to visually inform other individuals of their condition. The first proposed algorithm describes a visual memory mechanism that allows an animat to remember seen objects for some time and select its behavior in a more stable way compared to a basic visual analyzer. The second algorithm describes a group recognition approach. Robots are able to exchange information about the scene they are observing presented in the form of a graph using local communication. The robot, receiving such information from its neighbors, is able to expand its picture of the world and more correctly choose its behavior based on the recognized situation than if it only had its own data. A demonstration task was set to test the efficiency, approximately simulating a colony of ants that gather food in a certain area. Colony, nest, food, and enemy individuals are modeled in the developed simulation environment. Group efficiency was defined as the amount of food collected over the allotted time period. Animates using the visual memory algorithm showed an increase in efficiency for all the studied memory parameters. Moreover, the dependence is not monotonic, and there is a certain value at which maximum efficiency is achieved. The use of the group recognition algorithm also showed an increase in efficiency compared to the basic visual analyzer. The experiments were carried out for different distances of local interaction. As with memory, there is a value of interaction distance at which the maximum efficiency is achieved. The combination of both algorithms also has interesting results. The experiments showed that the smaller the memory of the robot, the greater the increase in efficiency from using the collective recognition algorithm.

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