Adaptive Shared Cascade Navigation Control of Magnetic Microrobots in Unstructured Dynamic Environments

Precise motion control of magnetic microrobots in complex and dynamic environments remains a critical challenge for enabling key applications such as targeted therapy and micromanipulation. Purely manual teleoperation is prone to operator fatigue and error, while fully autonomous systems often lack the robustness and adaptability to handle. Here, we propose a human–machine shared cascade control method for magnetically driven microrobots, which effectively integrates human cognitive intelligence with machine autonomy for collision-free navigation in dynamic environments. The outer-loop hybrid shared control unit smoothly modulates control authority in response to real-time collision risk, dynamically integrating the operator instructions and the autonomous navigation system output guided by the enhanced artificial potential field method to formulate the guidance law. For the inner-loop motion tracking, a data-driven adaptive orientation controller is designed, which integrates a nonlinear feedforward compensator leveraging a Gaussian process regression (GPR) model with a linear feedback controller whose parameters are optimized using the virtual reference feedback tuning (VRFT) method, ensuring fast and precise tracking of the desired motion. The effectiveness of the proposed method was validated through both simulation and physical experiments. In human-subject studies conducted on a physical magnetic actuation platform featuring both static and dynamic obstacle scenarios, quantitative results demonstrate that the shared control strategy significantly outperforms both purely manual and fully autonomous modes across all key metrics, including success rate, task completion time, stability, and safety (p < 0.001). Furthermore, successful navigation within a complex gastric model demonstrates the potential of the shared control system for practical application in unstructured environments.