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Blog Microlens Array Enables Development of a Thin Sensor for Computer Vision
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  • Author Author: Catwell
  • Date Created: 17 May 2022 1:16 PM Date Created
  • Views 1746 views
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Microlens Array Enables Development of a Thin Sensor for Computer Vision

Catwell
Catwell
17 May 2022

image

A new minimized MLA-based sensor (right) is seen next to a standard vision-based tactile sensor. (Credit: Chen et. al.)

Vision-based tactile sensors have been widely studied in the robotics field, as they are capable of tracking movement and changes in real-world environments with high levels of precision. However, imaging systems currently employed in the sensors are bulky, limiting the potential applications. A team at Hong Kong University of Science and Technology, led by Xia Chen, has developed a microlens array (MLA) based vision system to maintain high performance in a low thickness format—useful for embedding in a variety of devices including robotic limbs and wearables.

Designed by placing multiple microlenses in an array over the full surface of an elastic layer, the new sensor is around 5 millimeters thick, compared to thicknesses of at least 18 millimeters found in other tactile sensors. Thermal reflow and soft lithography fabrication techniques help to ensure that the surfaces of the lenses are uniformly spherical and smooth. Utilizing an MLA, images are captured in multiple image units, as many insects do. When a touching event happens, the images can be processed to provide feedback such as force distribution and displacement mapping.

The team has demonstrated the sensor’s capacity for stable imaging and tactile sensing, as well as its ability to provide precise 3D tactile information, as detailed in their research. In the future, MLA-based sensors could be integrated into a large variety of electronic and robotic systems. In the meantime, Chen and his research team hope to continue to develop the design, testing it in various real-world devices such as robotic arms. They believe their design could assist many computer vision tools, such as object recognition and tracking tools, and future research will only uncover more potential in multiple vision units.

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