提升智能实验平台资源利用效率，实现多设备协同调度
Optimal Resource Utilization for Autonomous Laboratory Orchestrators
- 采用约束规划算法生成最优实验调度方案
- 在硬件容量限制下将总实验时间最小化
- 通过状态依赖机制保障调度方案可靠执行
在自主实验室中，AI代理会建议下一组待执行的实验。然而，如何充分利用现有资源来规划和执行这些任务则是一个全新挑战，尤其在面对真实世界硬件约束时更为复杂，特别是当存在多个容量与吞吐量各异的仪器时。本文针对金属有机框架合成的自主平台，提出一种两阶段方法以优化资源利用率：首先使用约束规划寻找最优调度方案，使总时间最小化，同时满足硬件的限制与容量；其次，通过为每个任务建立状态依赖系统，确保最优调度方案能够稳健执行。
原文摘要 · Abstract (English)
In autonomous laboratories, AI agents suggest the next batch of experiments to do. However, planning and executing those tasks taking full advantage of the available resources is a completely different question. This can be challenging when dealing with real-world hardware constraints, especially so when there are multiple instruments with different capacities and throughputs. Here we demonstrate a 2-step method to address resource utilization for our autonomous platform for metal-organic framework synthesis. First, we use constraint programming to find optimal schedules. This finds schedules that minimizes the total time while still satisfying the limitations and capacities of the hardware. Secondly, we use a system of status dependencies for each task, which allows for the robust execution of the optimal schedules.
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