Multi-UAV Trajectory Optimization and Deep Learning-Based Imagery Analysis for a UAS-Based Inventory Tracking Solution

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Multi_UAV_Trajectory_Optimization_and_Deep_Learning_based_Imagery_Analysis_for_a_UAS_based_Inventory_Tracking_Solution.pdf (4.08 MB)
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Choi, Youngjun
Martel, Maxime
Briceno, Simon
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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http://hdl.handle.net/1853/61972
Abstract
This paper presents a multi-UAV trajectory optimization and an imagery analysis technique based on Convolutional Neural Networks (CNN) for an inventory tracking solution using a UAS platform in a large warehouse or manufacturing environment. The current inventory tracking method is a manual and time-consuming process to scan all the inventory items. Its accuracy is not consistent depending on the complexity of the scanning environment. To improve the scanning efficiency with respect to time and accuracy, this paper discusses a UAS-based inventory solution. In particular, this paper addresses two primary topics: multi-UAV trajectory optimization to scan inventory items and a multi-layer CNN architecture to identify a tag attached on the inventory item. To demonstrate the proposed multi-UAV trajectory optimization framework, numerical simulations are conducted in a representative inventory space. The proposed CNN-based imagery analysis framework is demonstrated on a flight experiment.
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2019
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