Spatial Constraint-Based Navigation and Emergency Replanning Adaptive Control for Magnetic Helical Microrobots in Dynamic Environments

Magnetic helical microrobots have attracted considerable attention in navigation control. However, the performance of microrobots is negatively affected by time-varying uncertain perturbations and obstacles, at the microscale. In this study, we present a navigation control scheme for accurately guiding the helical microrobot to targeted positions in dynamically changing environments. To efficiently plan smooth paths, a search-based algorithm with pruning rules is implemented to quickly find collision-free waypoints and design an optimal method with spatial and dynamic constraints for obtaining smooth paths globally. Velocity gain and potential fields are integrated to develop an emergency local motion replanning method for addressing random obstacles that suddenly appear in the preset path. In order to attain microrobot system dynamic linearization and achieve precise path following of a helical microrobot, a robust control strategy that integrates geometric and model-free controllers in a complementary manner is presented. The geometric controller as a feedforward controller, responsible for managing path information and generating guidance laws. In contrast, the model-free controller operates as a feedback controller, specifically designed to rapidly address position deviation. Meanwhile, we employ an observer to compensate for disturbances. Experimental results of precise motion control in both static and dynamic environments demonstrate the effectiveness of this navigation control scheme, which is promising for moving with high accuracy in cluttered and dynamic living enclosed environments.