LSTM
Assembly Line
Predicting congestion in fleets of robots
Many Amazon fulfillment centers use mobile robots to move shelves, retrieve products, and deliver them to workers for sorting, reducing the need for employees to walk long distances. For simplicity and scalability, the path-planning algorithm those robots currently use focuses on individual agents and ignores interactions between multiple agents.
In a paper we presented at this year’s International Conference on Robotics and Automation (ICRA), we propose a deep-learning model that can predict congestion on the floor in real time. We tested the model’s predictions in simulations of two downstream applications: dynamic path planning in sortation centers, where our model improved throughout by 4.4%, and travel time estimation, where it improved the mean absolute percentage error by 30% to 40% relative to the current production methods.
Cooperation between Control Technology and AI Technology to Improve Plant Operation
As the manufacturing industry is shifting its production model from mass production to the production of multiple products in small or variable quantities, more sophisticated operation of production equipment is required. Yokogawa has a unique approach to this problem, which was adopted by the New Energy and Industrial Technology Development Organization (NEDO). This paper describes details of this NEDO project and its achievements, as well as a study on the effective use of AI technology, which is another theme of this project.
In the NEDO project, to create this time-series model, we used effective nonlinear methods: multilayer perceptron (MLP), BiLSTM, and QRNN. As a result, we obtained correlation coefficients greater than 0.7 in the model. To verify whether this time-series model can reproduce the behavior of the target process, we evaluated its accuracy index. In addition, we used the model to solve the optimization problem and automatically calculate the optimal control parameters (PID values).