Collective Decision-Making with Bayesian Robots in Dynamic Environments

Collective decision-making enables self-organizing robot swarms to act autonomously on a swarm level and is essential to coordinate their actions as a whole. When robots only share and communicate information locally a distributed and decentralized approach is required. In a previous paper [4], an efficient method based on a distributed Bayesian algorithm was created to distinguish a binary environment. We extended it to have the capability of dealing with dynamic environments. Therefore, it must avoid global lock-in states. In many realistic applications the robot swarm needs to adapt to (collectively) measurable changes at runtime by revising previous collective decisions. The trade-off between decision-making speed and readiness to revise previous decisions is a seemingly unavoidable challenge. We present our extension of the former approach and study how this trade-off can efficiently be balanced.