Optimal Resource Utilization for Autonomous Laboratory Orchestrators
This work provides a practical solution for optimizing resource use in autonomous experimental platforms, which is relevant for labs with multiple instruments but represents an incremental improvement over existing scheduling approaches.
The paper addresses resource utilization in autonomous laboratories by developing a two-step method combining constraint programming for optimal scheduling and a status dependency system for robust execution, achieving minimized total time under hardware constraints.
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.