Emergent Naming System in an Unstructured Environment: a Shortest-Path Discovery Case Study

It is often postulated that robots will eventually face conditions, whether on extraterrestrial bodies or deep underwater, that could not have been predicted by their designers. In such conditions, truly autonomous robots should be able to describe and talk about their environments in order to collectively find appropriate solutions. We designed an emergent naming systems for such purposes. This paper focuses on a shortest-path discovery scenario in an unstructured environment, where landmarks are collectively named, by a swarm of robots, as they are discovered. The robots use those landmarks as beacons for navigation and score them according to their relevance to the task at hand. Meanwhile the naming system enables the swarm to update these scores asynchronously, using very little bandwidth. We compare our naming-based navigation performances with swarms that do not communicate and swarms with prior knowledge of the environment, and find that our approach performs similarly to the latter. This has significant implications on the link between space conceptualisation and language, as this proto-language enables the robots to find a topological path without individually mapping the environment.