Computational techniques for locating industrial products in warehouses
Journal
Contemporary Engineering Sciences
Date Issued
2023
DOI
10.12988/ces.2023.93121
Abstract
The computational estimation of an indoor or open-space warehouse
inventory is based on the prior knowledge of the pallet dimensions. The
input data consist of a three-dimensional point-cloud created by three-
dimensional (3D) scanners (LiDAR technology) adapted to aerial ve-
hicles (Drones). For research purposes, a storage simulator has been
implemented in the Python language (version 3.9). In the rst phase,
this research focuses on the ideal case of point-dispersion, with integer
values for coordinates, in which a unit length corresponds to the distance
between two neighboring pixels in the horizontal or vertical direction.
In a subsequent stage, the generator of three-dimensional points will be
modi ed to produce more realistic warehouse models. Improved ver-
sions of existing algorithms will be proposed, taking into consideration
the height variations.
inventory is based on the prior knowledge of the pallet dimensions. The
input data consist of a three-dimensional point-cloud created by three-
dimensional (3D) scanners (LiDAR technology) adapted to aerial ve-
hicles (Drones). For research purposes, a storage simulator has been
implemented in the Python language (version 3.9). In the rst phase,
this research focuses on the ideal case of point-dispersion, with integer
values for coordinates, in which a unit length corresponds to the distance
between two neighboring pixels in the horizontal or vertical direction.
In a subsequent stage, the generator of three-dimensional points will be
modi ed to produce more realistic warehouse models. Improved ver-
sions of existing algorithms will be proposed, taking into consideration
the height variations.
File(s)![Thumbnail Image]()
Name
varsamisCES1-2023.pdf
Size
178.99 KB
Format
Adobe PDF
Checksum (MD5)
7362f8c3d1147daad910118fdf30ee42