Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments

Solving complex problems collectively with simple entities is a challenging task for swarm robotics. For the task of collective decision-making, robots decide based on local observations on the microscopic level to achieve consensus on the macroscopic level. We study this problem for a common benchmark of classifying distributed features in a binary dynamic environment. Our special focus is on environmental features that are dynamic as they change during the experiment. We present a control algorithm that uses sophisticated statistical change detection in combination with Bayesian robots to classify dynamic environments. The main profit is to reduce false positives allowing for improved speed and accuracy in decision-making. Supported by results from various simulated experiments, we introduce three feedback loops to balance speed and accuracy. In our benchmarks, we show the superiority of our new approach over previous works on Bayesian robots. Our approach of using change detection shows a more reliable detection of environmental changes. This enables the swarm to successfully classify even difficult environments (i.e., hard to detect differences between the binary features), while achieving faster and more accurate results in simpler environments.