Language games are self-organised models of the evolution of language used in the context of natural language evolution. They are used to study the emergence of a shared vocabulary through a self-organisation process. These models have been traditionally tested in settings where the interaction topology among individuals is mostly static. Only recently, these models have been introduced to the context of swarm robotics, in order to study the effect of embodiment and random agent mobility on the evolution of language. These results have shown that, even in such setting, the naming game exhibits the same outcome as in simpler simulations: all agents achieve consensus on a single word. In this paper, we study the interaction effect between the naming game and one of the simplest, yet most important collective behaviour studied in swarm robotics: self-organised aggregation. This collective behaviour can be seen as the building blocks for many others, as it is required in order to gather robots, unable to sense their global position, at a single location. Achieving this collective behaviour is particularly challenging, especially in environments without landmarks. Here, we augment a classical aggregation algorithm with a naming game model. Experiments reveal that this combination extends the capabilities of the naming game as well as of aggregation: It allows the emergence of more than one word, and allows aggregation to form a controllable number of groups. These results are very promising in the context of collective exploration, as it allows robots to divide the environment in different portions and at the same time give a name to each portion, which can be used for more advanced subsequent collective behaviours.