Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing

Abstract

Multi-option collective decision-making is a challenging task in the context of swarm intelligence. In this paper, we extend the problem of collective perception from simple binary decision-making of choosing the color in majority to estimating the most likely fill ratio from a series of discrete fill ratio hypotheses. We have applied direct comparison (DC) and direct modulation of voter-based decisions (DMVD) to this scenario to observe their performances in a discrete collective estimation problem. We have also compared their performances against an Individual Exploration baseline. Additionally, we propose a novel collective decision-making strategy called distributed Bayesian belief sharing (DBBS) and apply it to the above discrete collective estimation problem. In the experiments, we explore the performances of considered collective decision-making algorithms in various parameter settings to determine the trade-off among accuracy, speed, message transfer and reliability in the decision-making process. Our results show that both DC and DMVD outperform the Individual Exploration baseline, but both algorithms exhibit different trade-offs with respect to accuracy and decision speed. On the other hand, DBBS exceeds the performances of all other considered algorithms in all four metrics, at the cost of higher communication complexity.