A Deep Learning-Based Framework for Environment-Adaptive Navigation of Size-Adaptable Microswarms

Actively controllable microswarms have been a rapidly developing research field with appealing characteristics. Autonomous collision-free navigation of microswarms in confined environments is suitable for various applications, including targeted therapy and delivery. However, several challenges remain unaddressed. First, microswarms possess varying dimensions, and a path planning method suitable to swarms with different dimensions is essential to avoid obstacles. Second, studies on the environment-adaptive navigation of reconfigurable microswarms are limited. Therefore, the planning of the pattern distribution of microswarms based on the local working environment should be examined. This study proposes a deep learning (DL)-based environment-adaptive navigation scheme for swarms. The controller provides reference moving directions for swarms of different sizes in static and dynamic scenarios. Moreover, a pattern-distribution planner was designed to navigate transformable swarms in unstructured environments. To validate the proposed scheme, we applied Fe3O4 nanoparticles swarms as a case study. The proposed scheme enables motion and pattern planning for microrobots of multiple sizes and reconfigurability in various working environments, which could foster a general navigation system for reconfigurable microswarms of different sizes.