With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.

翻译：随着对柔性自动化的日益重视——强调系统能够适应各种任务和条件，探索未来在机器人系统中部署基于云和边缘的网络基础设施变得至关重要。本研究探讨了无线解决方案如何支持工业环境从刚性、有线配置向更具适应性、更灵活的自动化转变。我们为机器人工作负载提供了一种基于控制质量（QoC）的抽象模型，该模型以环路延迟和可靠性为参数，并联合优化系统性能。该设置涉及协作机器人执行分布式任务，突显了无线通信如何能够在柔性自动化系统中实现更动态的协调。我们利用该抽象模型来最优地最大化QoC，确保即使在变化的网络条件下也能高效运行。此外，我们的解决方案以时隙方式分配通信资源，优化了通信成本与控制成本之间的平衡。我们的仿真结果表明，最小化系统延迟并不总能保证最佳的QoC，但如果有时放宽延迟约束，允许更多数据包可靠传输，反而可能带来QoC的显著提升。