Motion-based communication for robotic swarms in exploration missions

Abstract

Many people are fascinated by biological swarms, but understanding the behavior and inherent task objectives of a bird flock or ant colony requires training. Whereas several swarm intelligence works focus on mimicking natural swarm behaviors, we argue that this may not be the most intuitive approach to facilitate communication with the operators. Instead, we focus on the legibility of swarm expressive motions to communicate mission-specific messages to the operator. To do so, we leverage swarm intelligence algorithms on chain formation for resilient exploration and mapping combined with acyclic graph formation (AGF) into a novel swarm-oriented programming strategy. We then explore how expressive motions of robot swarms could be designed and test the legibility of nine different expressive motions in an online user study with 98 participants. We found several differences between the motions in communicating messages to the users. These findings represent a promising starting point for the design of legible expressive motions for implementation in decentralized robot swarms.