As the need for efficient warehouse logistics has increased in manufacturing systems, the use of automated guided vehicles (AGVs) has also increased to reduce travel time. The AGVs are controlled by a system using laser sensors or floor-embedded wires to transport pallets and their loads. Because such control systems have only predefined palletizing strategies, AGVs may fail to engage incorrectly positioned pallets. In this study, we consider a vision sensor-based method to address this shortcoming by recognizing a pallet’s position. We propose a multi-task deep learning architecture that simultaneously predicts distances and rotation based on images obtained from a visionary sensor. These predictions complement each other in learning, allowing a multi-task model to learn and execute tasks impossible with single-task models. The proposed model can accurately predict the rotation and displacement of the pallets to derive information necessary for the control system. This information can be used to optimize a palletizing strategy. The superiority of the proposed model was verified by an experiment on images of stored pallets that were collected from a visionary sensor attached to an AGV.
Wind noise is one of the most important factor in vehicle development, and it is significantly influenced by the exterior design. The exterior design is changed many times throughout the development process, therefore it's very inefficient to make prototypes or to perform CFD simulations.
Our CFD simulation accuracy has been improved over many years through validation studies and it's now the most trustful source however it has limitation on a long simulation time. In this research, a method to efficiently create a training data set to develop a CNN Deep learning model based
on exterior images is proposed. First, CFD simulation has been performed several times with changing wind noise influence factors, and a meta-model is created based on these initial simulations. This meta-model creates various vehicle shapes, and calculates wind noise simulation results. After
that, CNN DL model is created based on the images that has been created by the meta-model which best express the wind noise influence factor. This model promptly predicts the wind noise performances and verified through CFD simulation. Through this research, we were able to predict wind noise
only with images, hence validated the possibility of general use that can be applied to various vehicles.
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