Congestion Prediction for Large Fleets of Mobile Robots
Ge Yu, Michael Wolf
Abstract
This paper introduces a deep learning (DL) approach to predicting congestion delays in large multi-robot systems. The problem is motivated by real-world problems in modern logistics automation, such as a warehouse with hundreds to thousands of coordinated mobile robots. Here, the large scale, the complexity of the control software, and the uncertainties of the robots' dynamics make direct (simulated) prediction of future robot states impractical. We propose predicting delays associated with future spatiotemporal locations, and we show this is useful for improving system performance via incorporating the predictions into path planning and travel time estimation. Our DL model uses convolutional long short-term memory (ConvLSTM) as the core structure, takes the historical congestion condition and planned paths as input, and generates the delays across all nodes in the spatial planning graph for a set of future time windows. When using predictions in a modified path planner, simulation experiments using production data show 4.4% average improvement in throughput performance versus without predictions.