Longitudinal Control Volumes: A Novel Centralized Estimation and Control Framework for Distributed Multi-Agent Sorting Systems

Abstract-- Centralized control of a multi-agent system improves upon distributed control especially when multiple agents share a common task e.g., sorting different materials in a recycling facility. Traditionally, each agent in a sorting facility is tuned individually which leads to suboptimal performance if one agent is less efficient than the others. Centralized control overcomes this bottleneck by leveraging global system state information, but it can be computationally expensive. In this work, we propose a novel framework called Longitudinal Control Volumes (LCV) to model the flow of material in a recycling facility. We then employ a Kalman Filter that incorporates local measurements of materials into a global estimation of the material flow in the system. In this work, we present a novel modeling framework that divides the entire conveyor into smaller Longitudinal Centralized control approaches improve task performance Control Volumes (LCV) which enables a low-dimensional over distributed approaches by leveraging information about representation and prediction of material flows throughout the system state to coordinate agent actions.