Design and Development of Smart Multipurpose Automated Guided Vehicle Implemented with SLAM and AMCL

The use of robotic systems in logistics has increased the importance of precise positioning, especially in warehouses. The paper presents a system that uses virtual fiducial markers to accurately predict the position of a drone in a warehouse and count items on the rack. A warehouse scenario is created in the simulation environment to determine the success rate of positioning. A total of 27 racks are lined up in the warehouse and in the center of the space, and a 6 × 6 ArUco type fiducial marker is used on each rack. The position of the vehicle is predicted by supervised learning. The inputs are the virtual fiducial marker features from the drone. The output data are the cartesian position and yaw angle. All input and output data required for supervised learning in the simulation environment were collected along different random routes. An image processing algorithm was prepared by making use of fiducial markers to perform rack counting after the positioning process. Among the regression algorithms used, the AdaBoost algorithm showed the highest performance. The R2 values obtained in the position prediction were 0.991 for the x-axis, 0.976 for the y-axis, 0.979 for the z-axis, and 0.816 for the γ-angle rotation.

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Warehouse Drone: Indoor Positioning and Product Counter with Virtual Fiducial Markers

Ekici

Seçkin

Ozek

et al. 2022

Drones

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Design and Development of Smart Multipurpose Automated Guided Vehicle Implemented with SLAM and AMCL

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References 9 publications

Warehouse Drone: Indoor Positioning and Product Counter with Virtual Fiducial Markers

Warehouse Drone: Indoor Positioning and Product Counter with Virtual Fiducial Markers

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