ETH Zurich in Switzerland researchers have recently developed a multi-camera optical tactile sensor that collects information about the contact force distribution applied to its surface. This sensor, presented in a paper prepublished on arXiv, could be used to develop soft robotic skins based on computer vision algorithms, according to an article on Tech Xplore.
"Compared to the vision capabilities that robots can achieve using modern cameras, the sense of touch in robots is very under-developed," Camill Trueeb, Carmelo Sferrazza and Raffaello D'Andrea, the researchers who carried out the study, were quoted as saying in an email to Tech Xplore. "Vision-based tactile skins aim at bridging this gap, exploiting the capabilities of vision sensors and state-of-the-art artificial intelligence algorithms, benefiting from the accessibility of large amounts of data and computational power."
The optical tactile sensor comprises four cameras placed underneath a soft, transparent material that contains an embedded spread of spherical particles within it. The cameras track the motion of the particles, which arises from the deformation of the material when a force is applied to it.
The researchers also developed a machine learning (ML) architecture that analyzes the motion of the spherical particles in the material. By analyzing this motion, this architecture can reconstruct the forces that are causing a deformation in the material, also known as the contact force distribution.
The sensor offers feedback on the distribution of all the forces applied to its soft surface, decoupling normal and tangential components. Due to its structure and unique design, the multi-camera sensor also exhibits a larger contact surface and a thinner structure than other camera-based tactile sensors without requiring additional reflecting components (e.g., mirrors).
The researchers believe the sensor could ultimately be scaled to larger surfaces, enabling the creation of soft and sensing robotic skins. In their recent paper, the researchers discuss how their ML architecture could be adapted to facilitate these applications in the future.