| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 107 === With the rapid development of technology, the concept and implementation of
smart factories have gradually emerged. Furthermore, the application of robotic vision
has become more and more extensive, and the demand is also growing. Because of
the great progress in the development of AI technology, it replaces traditional mathematical
calculations and parameter adjustments, and relies on CNN to achieve better
results. The combination of a robotic arm and a camera can help pick up objects
in the factory and replace expensive depth sensors with Mask-RCNN RBG data, but
stacked objects that are common in factories are often more complex and more difficult
to identify than daily photos. Also because of the need for more precise positioning
in the factory, general object segmentation is not enough. Another more important
reason is that the environmental shielding rate of stacked objects is often very high.
Here we have experimented with the inpainting algorithm, and also proposed a method
combining mask-RCNN and multiple GANs to solve a single object in a random bin
picking environment. The problem of positioning is to use these architectures to restore the pose of the basic object in 3D. This experiment uses the simulation environment
Assasim developed by the laboratory and itri team to produce data and verification.
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