Social Exploration in Robot Swarms

Robot swarms have shown great potential for exploration of unknown environments, utilizing simple robots with local interaction and limited sensing. Despite this, complex indoor environments can create issues for reactive swarm behaviours where specific paths need to be travelled and bottlenecks are present. In this paper we present our social exploration algorithm which allows the swarm to decide between different options of swarm behaviours to search randomly generated environments. Using a "happiness" measure, agents can reason over the performance of different swarm behaviours, aiming to promote free movement. Agents collaborate to share opinions of different behaviours, forming teams which are capable of adapting their exploration to any given environment. We demonstrate the ability of the swarm to explore complex environments with minimal information and highlight increased performance in relation to other swarm behaviours over 250 randomly generated environments.