US 12,229,713 B2
Methods and apparatuses for automatically palletizing and depallitizing items
David Frederick Pepley, Pittsburgh, PA (US); and Mayank Pathak, Pittsburgh, PA (US)
Assigned to INTELLIGRATED HEADQUARTERS, LLC, Mason, OH (US)
Filed by INTELLIGRATED HEADQUARTERS, LLC, Mason, OH (US)
Filed on Jul. 19, 2021, as Appl. No. 17/379,316.
Prior Publication US 2023/0020576 A1, Jan. 19, 2023
Int. Cl. G06Q 10/087 (2023.01); G06N 5/02 (2023.01)
CPC G06Q 10/087 (2013.01) [G06N 5/02 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method for determining one or more placement locations on a pallet for one or more items of a plurality of items, the computer-implemented method comprising:
generating, using one or more processors, a plurality of three-dimensional pallet cells for the pallet having a pallet volume, wherein the plurality of three-dimensional pallet cells is arranged to segment said pallet volume such that each three-dimensional cell is associated with a cell volume that is at least a portion of the pallet volume;
generating, using the one or more processors, one or more three-dimensional item cells for each item of the plurality of items, wherein each item has a corresponding item volume, and wherein the one or more three-dimensional item cells for each item contain the corresponding item volume;
training, using the one or more processors, a predictive machine learning model to select from the one or more placement locations each comprising one or more three-dimensional pallet cells for the one or more three-dimensional item cells corresponding to each item by utilizing at least one location score for at least one placement location for the one or more three-dimensional item cells, wherein the at least one location score is determined based on overall volume occupancy data of the pallet, wherein the overall volume occupancy data is determined based on a number of three-dimensional pallet cells that are associated with an occupied state, and wherein the at least one location score is increased when one of the plurality of items is placed in a placement location associated with a high occupancy of an adjacent three-dimensional item cell; and
causing, using the one or more processors, one or more prediction-based actions to be performed by a robotic arm on the one or more items based at least in part on the one or more placement locations.