A Model Predictive Control Approach of Optimal Autonomous Laboratory Management

The development of autonomous laboratories has significantly advanced with the integration of computer vision, simultaneous localization and mapping, cloud computation technologies, etc. These advancements have enhanced the automation and efficiency of experimental processes. However, the optimal management of complex task scheduling within such environments remains underexplored, especially in the face of challenges such as managing numerous tasks, adhering to strict time and state constraints, and ensuring the sustainable stability and performance of the entire laboratory operation. This article introduces a novel model predictive control (MPC)-based strategy for the optimal management of task scheduling in autonomous laboratories. Our approach begins with the abstraction of the scheduling problem as a finite state machine, which lays the foundation for a systematic analysis. We then employ concepts of invariant sets and stability to ensure that the pro posed scheduling strategy is not only efficient but also resilient to operational uncertainties. The proposed approach ensures recursive feasibility, which guarantees the adaptability of the scheduling strategy over time. Through a series of simulations, we demonstrate the efficacy of our MPC-based management strategy in optimizing task scheduling, thereby significantly enhancing the laboratory's operational efficiency, stability, and sustainability. Our findings offer promising insights into the future of autonomous laboratory management, providing a robust framework for tackling the complexities of task scheduling in such environments.