4 Ways Deep Learning Optimizes Factory Automation

Robotics Business Review

Factory robots are about to get even smarter. Japanese robot makers, which account for 52% of global supply, are pushing further into artificial intelligence and deep learning so that smart machines can work faster and with greater flexibility amid a shrinking workforce.

Deep learning systems can recognize objects in messy, real-world situations and they’re expected to enhance the perception abilities of industrial robots. At iREX 2017 in Tokyo, FANUC, Yaskawa Electric and other manufacturers put on demos showcasing how deep learning can optimize factory automation.

Deep Learning for Random bin picking

FANUC’s booth at the biennial show had its usual offerings of industrial robot arms, collaborative robots, a titanic car-hoisting robot, and its Field System, a networking platform for the industrial internet of things (IIoT) that can provide breakdown predictions. But staff were keen to point out demos developed in collaboration with Preferred Networks that exploited deep learning.

In one demo, a bin-picking LR Mate 200iD robot used a deep learning algorithm and a 3D sensor to analyze a bin with a random assortment of cylindrical work pieces. FANUC’s system ranked the cylinders according to which ones were unobstructed and easiest to grasp. A screen overlaid grasping probability scores on an image of the cylinders, with the unobstructed ones scoring near 100.

The system has a higher ratio of successful grasps compared to robots without deep learning functions, according to FANUC. It was trained for about eight hours to learn which pieces were within easy reach, but that can be reduced to two hours when using four networked robots. The system took about two years to develop, and FANUC plans to release the picking application in March 2018.

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