There is more to see Robots can now Detect Previously "Invisible" Objects

Source: Fraunhofer-IOF | Translated by AI 2 min Reading Time

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Robots have found it difficult to detect transparent, reflective, or deep black objects as they are so-called uncooperative surfaces. Researchers have now improved the vision of robots.

For the optics of robotic systems, the surfaces of some objects are hardly detectable, and therefore, the objects themselves are not either. However, with AI and special projections, researchers have managed to make the "ghost objects" identifiable...(Image: Fraunhofer-IOF)
For the optics of robotic systems, the surfaces of some objects are hardly detectable, and therefore, the objects themselves are not either. However, with AI and special projections, researchers have managed to make the "ghost objects" identifiable...
(Image: Fraunhofer-IOF)

So-called uncooperative surfaces are difficult for conventional sensor systems to capture. However, researchers at the Fraunhofer Institute for Optics and Precision Engineering (IOF) have overcome this challenge with the "goROBOT3D" system, which recognizes such surfaces through "intelligent" thermal imaging, as it is now reported. They have further developed existing systems for this purpose. The measurement and evaluation time for transparent or deep black objects with the new system could be reduced from the previous 15 to just under 1.5 seconds. For this purpose, IOF also developed a new projection method for thermal 3D sensing.

Diffractive Optical Elements Create Anchor Points

The developed procedure transfers a "Single Shot" technology to thermal 3D measurement technology. In the process, the surface of the measurement scenery is structurally heated. A thermally statistical dot pattern arising on the surface of the objects is emitted and recognized using two thermal imaging cameras. Through spatial cross-correlation, a 3D result can be obtained from the captured image pair, as the researchers from Jena explain. Because instead of previously using stripe projections to generate the pattern, two diffractive optical elements (DOE) now create an irregular dot pattern. Such DOEs use the principle of light diffraction to multiply the incoming laser beam and divide it into a pattern. Through the clever combination of the DOEs, the required dot pattern could be projected onto the transparent object efficiently and for the first time in the shortest possible time.

Dot Pattern Allows Robots to Grip in Milliseconds

The captured 3D data is then analyzed using artificial intelligence (AI). Suitable gripping points and directions are derived and transmitted to a robotic arm with a suction gripper. For this purpose, the researchers employ a so-called "bin-picking" procedure. It can be said that it is essentially a grab into the box, i.e., the targeted picking of chaotically arranged objects. And the drastically shortened recording and evaluation time creates new possibilities for automated industrial processes, for example, in manufacturing plants or product design. Robots can not only safely identify and pick transparent or dark objects but also continue working almost without interruption. And while one object is being handled, the next measurement can already take place.

For those interested: The IOF will showcase this new possibility at the Automatica in Munich from June 24th to 27th in Hall 4.

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