Dynamic Obstacle Avoidance of Metamorphic Microrobots Using Concentric Sector Navigator

Autonomous navigation remains a critical challenge for microrobots operating in dynamic and unstructured biological environments, particularly when their motion modality changes during medical procedures. To address this problem, we develop a reliable autonomous navigation strategy that accounts for the metamorphic transition of a microrobot from an individual microroller state to a swarm state. The proposed approach combines a concentric sector navigator (CSN) for local path planning and dynamic obstacle avoidance with a state‐recognition model that identifies the microroller and swarm states for state‐dependent navigation control. To validate its effectiveness, experiments were conducted in obstacle‐rich environments and micromaze settings, demonstrating that the CSN method enables robust collision‐free navigation in both the microroller and swarm states under dynamic obstacles. The results verify that the proposed strategy provides an effective solution for autonomous navigation of metamorphic microrobots in complex environments.