Autonomous Collision-Free Navigation of Microvehicles in Complex and Dynamically Changing Environments.

Area of Science:

• Robotics
• Artificial Intelligence
• Nanotechnology

Background:

• Micro- and nanoscale robots are emerging but lack autonomous control in complex environments.
• Designing adaptive systems for micro/nanorobotics in dynamic settings remains a significant challenge.

Purpose of the Study:

• To develop a smart microvehicle with a fully autonomous navigation system.
• To enable precise and adaptive control for micro/nanorobots in complicated and changing environments.

Main Methods:

• A system combining a microscope-coupled CCD camera, an AI planner, and a magnetic field generator was developed.
• Real-time localization, environmental detection, and adaptive path planning were utilized.
• External electromagnetic torque controlled the microrobot's direction.

Main Results:

• The autonomous navigation system successfully guided the microvehicle in complex patterns.
• The system demonstrated effective navigation amidst dynamically changing obstacles and in biological environments.
• Vision-based, closed-loop control and path planning were key to successful navigation.

Conclusions:

• The developed smart microvehicle offers precise autonomous navigation capabilities.
• This vision-based control system is highly promising for micro/nanorobots in realistic, unpredictable scenarios.
• The system addresses the unmet challenge of adaptive control for micro/nanorobotics.