Researchers develop fastest pick-and-place robot in the world


Monday, 12 September, 2022

Researchers develop fastest pick-and-place robot in the world

A research team at the FEMTO-ST Institute in France has developed a miniature robot capable of manipulating micrometric objects at unprecedented speeds. The work has been published in journal Science Robotics.

Speed and precision are two major issues in robotics and in Industry of the Future (also known as Industry 4.0). Within this framework, the RoMoCo research team of the AS2M department at the FEMTO-ST Institute has developed a unique robot. MiGriBot is a miniature robot able to perform pick-and-place operations of sub-millimetre objects at unpresented speeds.

The performance is made possible by its unique architecture that allows it to grip and manipulate micro-objects barely visible to the naked eye (from 40 µm to several hundred µm). Where other microrobots have a rigid end-effector, MiGriBot is based on a new principle with an articulated end. This articulated end makes it possible to drive the microgripper without any wire or embedded actuator.

The second advantage of this robot is that all its degrees of mobility, including the ones from the microgripper on the articulated end, are operated from the base of the robot, making its mobile parts very lightweight.

Its robotic structure occupies a surface of only 20 x 20 mm2. This level of compactness is achieved by using silicon for the rigid elements, a polymer (polydimethylsiloxane) as flexible joints and piezoelectric actuators equipped with position sensors. MiGriBot is therefore lighter, more compact and faster than existing robotic micro-manipulators.

While the fastest industrial pick-and-place robots do not exceed 250 cycles per minute, the combination of all the features of this robot: soft joints, small footprint, integrated gripping, lightweight structure and closed-loop control of the fast actuators allows MiGriBot to reach 720 pick-and-place operations per minute with about one micrometre accuracy.

Top image: The research team: Maxence Leveziel, Wissem Haouas, Michaël Gauthier, Guillaume Laurent and Redwan Dahmouche.

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