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. We utilize a model predictive control algorithm that optimizes the rate of material flow using the global state estimate in real-time. We show that our proposed framework outperforms distributed control methods by 40-100% in simulation and physical experiments.

翻译：在多智能体系统中，当多个智能体共享共同任务（例如在回收设施中分拣不同材料）时，集中式控制相比分布式控制具有显著优势。传统上，分拣设施中的每个智能体需单独调参，若某个智能体效率低于其他设备，则会导致整体性能次优。集中式控制通过利用全局系统状态信息可突破这一瓶颈，但可能面临计算成本过高的问题。本文提出一种名为"纵向控制体"（LCV）的新型框架，用于建模回收设施中的物料流动。我们采用卡尔曼滤波器，将局部物料测量数据融入系统物料流动的全局估计。通过模型预测控制算法，基于全局状态估计实时优化物料流动速率。仿真与物理实验表明，所提框架相较于分布式控制方法性能提升40%-100%。