Enhancing Microrobot Swarm Stability and Adaptation by Autonomous Field‐of‐View Planning

Automatic navigation of microrobot swarms driven by magnetic fields has attracted considerable attention due to potential applications in biomedical fields. However, achieving minimal loss and maintaining swarm cohesion while traversing heterogeneous landscapes over long distances remains a challenge. This article introduces a control strategy based on autonomous field‐of‐view (FOV) planning for navigating microrobot swarms across large workspaces that span multiple FOVs. High‐resolution global images of the workspace are obtained using an image stitching method that combines phase correlation and template matching. Global path planning is accomplished with the A* algorithm, followed by local path planning utilizes the optimized informed rapidly‐exploring random tree star (OI‐RRT*) algorithm in each FOV to ensure swarm adaptation. The strategy also incorporates an FOV planning algorithm to optimize FOV positioning, along with a displacement platform to ensure smooth transitions between FOVs. A real‐time visual feedback control system monitors both channel width and swarm position. This strategy improves swarm navigation efficiency and stability, as demonstrated through experimental validation, and holds significant potential for targeted drug delivery and other biomedical applications.