AI deep-learning model developed to streamline operations in a robotic warehouse

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Researchers from Massachusetts Institute of Technology (MIT) have developed a deep-learning technique that can identify the optimal areas for reducing robotic traffic in a warehouse production system.

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According to the research team, large robotic warehouses are increasingly becoming part of the supply chain in many industries, from e-commerce to automotive production, but getting hundreds of robots to and from their destinations efficiently is ‘no easy task.’

As navigating the robots is such a complex problem, the MIT researchers said that even the best path-finding algorithms struggle to keep up with the pace of e-commerce or manufacturing.

To tackle this, the researchers, who use AI to mitigate traffic congestion, applied similar ideas from that domain to the new warehouse model.

The deep-learning model encodes important information about the warehouse, including the robots, planned paths, tasks, and obstacles, and uses it to predict the best areas of the warehouse to decongest in order to improve overall efficiency.

Their technique divides the warehouse robots into groups, so smaller groups of robots can be decongested faster with traditional algorithms used to coordinate the robots.

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robotic warehouses
deep-learning model