Optoelectronic Manipulation Platform for Parallel and Sequential Droplet Processing

Optoelectronic manipulation platforms have emerged as a transformative technology for droplet microfluidics, offering the unique capability to actuate droplets on unstructured surfaces via dynamic light patterns. However, realizing its full potential for high-throughput applications has been hindered by the challenges of orchestrating collision-free motion for massive droplet swarms and the lack of intelligent, real-time feedback. In this work, we report an integrated platform, OptoBot${ }^{@} 500$, that overcomes these limitations by synergizing optoelectrowetting (OEW) with advanced computer vision and path planning. The system utilizes a deep learning-based YOLO model for high-precision single-cell identification and the Window Hierarchical Cooperative Algorithm for robust multi-agent navigation. This paradigm establishes a scalable solution for next-generation single-cell analysis, combinatorial chemistry, and synthetic biology.