Localized communication in swarms has been shown to increase swarm effectiveness in some situations by allowing for additional opportunities for cooperation. However, communication and utilization of potentially outdated information is also a concern. We present an explicit non-directional goal-based communication model and message accept reject scheme, and test our model in a set of object gathering experiments with a swarm of robots. The results of the experiments indicate that even low levels of communication regarding the swarm's goal outperform high levels of random information communication.
Regardless of the medium, the purpose is clear: to recruit other members of the swarm for cooperative task completion. There have been many variants in the implementations, all to increase the swarm effectiveness given their specific situation @cite_18 . However, it is clear that communication is useful to increase the swarm effectiveness in accomplishing the task.
@cite_18
2053796391 1533885008 1500120067 2002188687
We study the efficiency of cooperative behavior in a society of interacting agents. After reviewing the problem and defining the concept of swarm intelligence, we examine the collective behavior of many-body active clusters through the task of gathering pucks in a field. We used a simple robot with a drive system and the simplest means of interaction; a light and some sensors. The effectiveness of group behavior was studied for various (homogeneous, localized) puck distributions in a real experiment, simulations, and analysis. To evaluate the efficiency of group behavior, we examined the scaling relation between the task completion time and the number of robots, and the relation between the interaction duration and the efficiency of the group. We also found that a critical density for efficiency when the interaction distance is finite. These results show that cooperation between elements using a simple interaction strongly enhances the performance of the group compared to independent individuals. This paper reviews research in three important areas concerning robot swarms: communication, diversity, and learning. Communication (or the lack of it) is a key design consideration for robot teams. Communication can enable certain types of coordination that would be impossible otherwise. However communication can also add unnecessary cost and complexity. Important research issues regarding communication concern what should be communicated, over what range, and when the communication should occur. We also consider how diverse behaviors might help or hinder a team, and how to measure diversity in the first place. Finally, we show how learning can provide a powerful means for enabling a team to master a task or adapt to changing conditions. We hypothesize that these three topics are critically interrelated in the context of learning swarms, and we suggest research directions to explore them. Communication is often required for coordination of collective behaviours. Social insects like ants, termites or bees make use of different forms of communication, which can be roughly classified in three classes: indirect (stigmergic) communication, direct interaction and direct communication. The use of stigmergic communication is predominant in social insects (e.g., the pheromone trails in ants), but also direct interactions (e.g., antennation in ants) and direct communication can be observed (e.g., the waggle dance of honey bee workers). Direct communication may be beneficial when a fast reaction is expected, as for instance, when a danger is detected and countermeasures must be taken. This is the case of hole avoidance, the task studied in this paper: a group of self-assembled robots – called swarm-bot – coordinately explores an arena containing holes, avoiding to fall into them. In particular, we study the use of direct communication in order to achieve a reaction to the detection of a hole faster than with the sole use of direct interactions through physical links. We rely on artificial evolution for the synthesis of neural network controllers, showing that evolving behaviours that make use of direct communication is more effective than exploiting direct interactions only. Swarm formation and swarm flocking may conflict each other. Without explicit communication, such conflicts may lead to undesired topological changes since there is no global signal that facilitates coordinated and safe switching from one behavior to the other. Moreover, without coordination signals multiple swarm members might simultaneously assume leadership, and their conflicting leading directions are likely to prevent successful flocking. To the best of our knowledge, we present the first set of swarm flocking algorithms that maintain connectivity while electing direction for flocking, under conditions of no communication. The algorithms allow spontaneous direction requests and support direction changes.