Design of a large-range rotary microgripper with freeform geometries using a genetic algorithm

Area of Science:

• Microelectromechanical Systems (MEMS)
• Robotics
• Computational Design

Background:

• Microgrippers are essential for micro-manipulation tasks.
• Traditional MEMS design methods often struggle with optimizing complex geometries for performance and robustness.

Purpose of the Study:

• To develop a semiautomated design methodology for electrostatically actuated microgrippers.
• To utilize genetic algorithms for designing freeform geometries in MEMS devices.
• To enhance microgripper performance, including displacement and robustness to fabrication tolerances.

Main Methods:

• A genetic algorithm was employed to design novel freeform geometries for an electrostatically actuated microgripper.
• The design methodology focused on achieving near-optimal MEMS devices robust to fabrication tolerances.
• Experimental validation was conducted to measure microgripper performance.

Main Results:

• The designed microgripper demonstrated a large displacement of 91.5 μm at a low actuation voltage of 47.5 V.
• The microgripper successfully handled micro-objects ranging from 10 to 100 μm.
• A grasping experiment on a 77 μm human hair confirmed the microgripper's functionality and superior performance.

Conclusions:

• The genetic algorithm-driven design methodology enables the creation of high-performance MEMS microgrippers with freeform geometries.
• This approach significantly improves microgripper performance and robustness.
• The design method is adaptable for various other MEMS devices.