The automation of factories and manufacturing processes has been accelerating over the past few years, boosted by the Industry 4.0 paradigm, including diverse scenarios with mobile, flexible agents. Efficient coordination between mobile robots requires reliable wireless transmission in highly dynamic environments, often with strict timing requirements. Goal-oriented communication is a possible solution for this problem: communication decisions should be optimized for the target control task, providing the information that is most relevant to decide which action to take. From the control perspective, networked control design takes the communication impairments into account in its optmization of physical actions. In this work, we propose a joint design that combines goal-oriented communication and networked control into a single optimization model, an extension of a multiagent POMDP which we call Cyber-Physical POMDP (CP-POMDP). The model is flexible enough to represent several swarm and cooperative scenarios, and we illustrate its potential with two simple reference scenarios with a single agent and a set of supporting sensors. Joint training of the communication and control systems can significantly improve the overall performance, particularly if communication is severely constrained, and can even lead to implicit coordination of communication actions.

翻译：近年来，在工业4.0范式推动下，工厂与制造流程自动化进程持续加速，涌现出包含移动式柔性智能体的多样化场景。移动机器人之间的高效协调需要高度动态环境中具备严格时序要求的可靠无线传输。面向目标的通信是解决该问题的可行方案：通信决策应针对目标控制任务进行优化，提供对行动决策最关键的信息。从控制视角看，网络化控制设计在其物理动作优化中纳入了通信损伤因素。本文提出一种联合设计方案，将面向目标的通信与网络化控制整合为单一优化模型——即多智能体部分可观测马尔可夫决策过程的扩展形式，我们称之为信息物理部分可观测马尔可夫决策过程（CP-POMDP）。该模型具有足够灵活性，可表征多种集群与协同场景，并通过含单智能体及一组支持传感器的两个简单参考场景展示其潜力。通信与控制系统的联合训练能显著提升整体性能，尤其在通信严重受限场景下，甚至可能实现通信动作的隐式协调。