A group of researchers at MIT have tackled the issue of ensuring smooth traffic in a warehouse full of robots by building a deep-learning model.
The model “encodes important information about the warehouse, including the robots, planned paths and obstacles, and uses it to predict the best areas of the warehouse to decongest to improve overall efficiency,” according to an article in MIT News.
The plan divides the warehouse robots into smaller group, with the the reduced group size resulting in less congestion that with traditional algorithms. The authors say that this method decongests the robots almost four times faster than a strong random search.
“We devised a new neural network architecture that is actually suitable for real-time operations at the scale and complexity of these warehouses. It can encode hundreds of robots in terms of their trajectories, origins, destinations, and relationships with other robots, and it can do this in an efficient manner that reuses computation across groups of robots,” says Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering, and author of a paper on this technique.
