Researchers applied deep-learning approaches from vehicle routing to streamline planning trajectories for robots in an e-commerce warehouse. Their method breaks the problem down into smaller chunks and then predicts the best chunks to solve with traditional algorithms.
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.
They built a deep-learning model that 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 to improve overall efficiency. Wu, senior author of a paper on this technique, is joined by lead author Zhongxia Yan, a graduate student in electrical engineering and computer science. The work will be presented at the International Conference on Learning Representations.From a bird's eye view, the floor of a robotic e-commerce warehouse looks a bit like a fast-paced game of"Tetris."
Because time is so critical during replanning, the MIT researchers use machine learning to focus the replanning on the most actionable areas of congestion -- where there exists the most potential to reduce the total travel time of robots. The technique also streamlines computation by encoding constraints only once, rather than repeating the process for each subproblem. For instance, in a warehouse with 800 robots, decongesting a group of 40 robots requires holding the other 760 robots as constraints. Other approaches require reasoning about all 800 robots once per group in each iteration.
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