MIT Develops AI Traffic Solution to Streamline Warehouse Robots
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MIT researchers apply ideas from AI traffic congestion mitigation to optimize warehouse robot coordination. Their deep learning technique breaks the problem into smaller groups for faster replanning.
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The neural network architecture encodes information about hundreds of robots and relationships between them to predict the best areas to decongest.
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Iteratively, the algorithm identifies the most promising robot group to decongest using the neural network, coordinates them with a search-based solver, and repeats.
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By capturing complex robot interactions and streamlining computation, this technique decongests warehouses 3.5-4x faster than non-learning methods.
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The model generalizes well to unseen warehouse environments. Future work involves deriving simple rules from the neural network decisions.