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About This Product
■Intelligence to industrial robots.
The Mech-Mind robot vision system is a next-generation robot vision system created by Mech-Mind Robotics, a company based in Beijing and Shanghai, China, founded by engineers who graduated from the Technical University of Munich, Germany. In addition to object recognition using 3D image recognition and deep learning technology, it is the ultimate robot system that enables robot movement without teaching by forming a unique robot action plan.
In addition, Deep Learning image recognition uses a recognition technology that utilizes a super model (a recognition algorithm formed through learning) that is formed through large-scale learning using a huge amount of image data, which is impossible in Japan. This is a next-generation robot picking system that is capable of image recognition.
Example using Mech-Mind robot vision
■Support for piece picking of various types using super model and high quality 3D data
The Mech-Mind robot vision system is capable of picking pieces of various types by utilizing a 3D vision camera and Deep Learning. Not only is the point cloud generated from a 3D camera highly accurate, but it also utilizes a supermodel that incorporates a recognition algorithm formed by large-scale learning of a huge amount of image data, making it possible to handle a wide variety of workpieces in one go. A major feature is that it can be recognized with one super model.
The biggest advantage of introducing Mech-Eye is the ability to utilize supermodels based on overwhelmingly large-scale learning like no other, and it allows us to propose solutions that utilize 3D vision even in situations that were previously difficult. is now possible.
■Picking example using Mech-Mind robot vision system
The Mech-Mind robot vision system, which can handle a variety of workpieces, uses large-scale image data learning and 3D camera recognition accuracy to handle black or glossy workpieces, which was difficult with conventional methods, and can handle black or glossy workpieces without prior learning. can now be applied to picking.
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