Deep Learning-Based Process Control of Microrobot Swarms Guided by Phase Diagrams

Microrobot swarms with locomotion dexterity and shape reconfigurability show immense potential in biomedical applications. Automatic control strategies are critical for the navigation of swarms in unstructured environments. Existing control methods mainly focus on initial and final states of swarms, and swarm process control designed to avoid undesired states throughout the control process is yet investigated. In this work, we develop a deep learning-based process control strategy for swarms guided by phase diagrams. Two deep neural networks are respectively built to model the swarm shape and kinematics. Control approaches based on precise swarm models are designed to automatically tune multiple swarm parameters. A phase diagram-based controller is proposed to guide the swarm reconfiguration while eliminating the coupling effects between swarm parameters. The swarm is enabled to accurately track predefined trajectories while performing continuous reconfiguration with desired states during the entire process. By integrating the process control of swarm pattern and locomotion, the swarm can dynamically adapt to constrained unstructured spaces and achieve robust collision avoidance.