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

Area of Science:

• Robotics
• Control Systems
• Biomedical Engineering

Background:

• Precise control of magnetic microrobots is crucial for applications like targeted therapy but challenging in dynamic environments.
• Manual teleoperation and fully autonomous systems have limitations in robustness and adaptability.

Purpose of the Study:

• To propose a human-machine shared cascade control method for magnetically driven microrobots.
• To achieve collision-free navigation in dynamic environments by integrating human intelligence and machine autonomy.

Main Methods:

• An outer-loop hybrid shared control unit modulates control authority based on collision risk.
• An inner-loop adaptive orientation controller uses Gaussian Process Regression (GPR) and Virtual Reference Feedback Tuning (VRFT).

Main Results:

• Human-subject studies showed the shared control strategy significantly outperformed manual and autonomous modes in success rate, completion time, stability, and safety (p < 0.001).
• Successful navigation in a complex gastric model demonstrated practical applicability in unstructured environments.

Conclusions:

• The proposed human-machine shared control method offers a robust and adaptive solution for magnetic microrobot navigation.
• This approach enhances microrobot performance and safety, paving the way for advanced therapeutic and micromanipulation applications.