Collective Decision Making in Swarm Robotics with Distributed Bayesian Hypothesis Testing

Abstract

In this paper, we propose Distributed Bayesian Hypothesis Testing (DBHT) as a novel collective decision-making strategy to solve the collective perception problem. We experimented with different sampling and dissemination intervals for DBHT and concluded that the selection of both intervals presents a trade-off between speed and accuracy. After that, we compare the performance of DBHT in simulation with that of 3 other commonly used collective decision-making strategies, DVMD, DMMD and DC. We tested them on collective perception problems with different difficulties and feature patterns. We have concluded that DBHT outperforms considered existing algorithms significantly in collective perception tasks with high difficulty, namely close proportion of features and clustered feature distribution.