Autonomous shuffling of pallets of items in a warehouse

Patent metadata

Field	Value
Publication number	US-9984339-B2
Application number	US-201615244525-A
Country	US
Kind code	B2
Filing date	Aug 23, 2016
Priority date	Aug 23, 2016
Publication date	May 29, 2018
Grant date	May 29, 2018

Abstract

Examples described may enable rearrangement of pallets of items in a warehouse to an optimal layout. An example method includes receiving real-time item information including pallet locations in a warehouse and real-time inventory of items arranged on the pallets; determining a likelihood of demand for future access to the pallets based on a pallet relocation history and item receiving/shipment expectations; based on the real-time item information and the likelihood of demand, determining an optimal controlled-access dense grid layout in which distances of the pallets from a center of the layout are related to the likelihood of demand; receiving real-time robotics information and using the real-time robotics information to determine an amount of time to rearrange the pallets to the optimal layout; and, based on the amount of time to rearrange the pallets being less than a threshold, causing the robotic devices to rearrange the pallets to the optimal layout.

First claim

What is claimed is:

1. A method comprising:
receiving, at a warehouse control system (WCS), real-time item information including real-time locations of pallets positioned in a warehouse and real-time inventory of items arranged on the pallets, wherein the real-time inventory of the items includes a content of each pallet;
determining a likelihood of demand for future access to the pallets based on a history of pallet relocation within the warehouse, an expectation of items to be received at the warehouse at a future date, and an expectation of items to be shipped out of the warehouse at a future date;
based on the real-time item information and the likelihood of demand, determining an optimal controlled-access dense grid layout to which to arrange the pallets, wherein distances of the pallets from a center of the optimal controlled-access dense grid layout are related to the likelihood of demand;
receiving, at the WCS, real-time robotics information including (i) real-time locations of a plurality of robotic devices as positioned in the warehouse, (ii) real-time task progress data for the plurality of robotic devices, (iii) a schedule for performance of the tasks, and (iv) time measurements for one or more of the plurality of robotic devices to perform the tasks;
based on the real-time robotics information, determining an amount of time to rearrange the pallets to the optimal controlled-access dense grid layout; and
causing one or more of the plurality of robotic devices to rearrange the pallets to the optimal controlled-access dense grid layout based on the amount of time to rearrange the pallets to the optimal controlled-access dense grid layout being less than a threshold amount of time.

2. The method of claim 1 , further comprising:
determining an updated likelihood of demand for future access to the pallets based on an updated history of pallet relocation within the warehouse, an updated expectation of items to be received at the warehouse at a future date, and an updated expectation of items to be shipped out of the warehouse at a future date;
based on the real-time item information and the updated likelihood of demand, determining an optimal deep lanes layout to which to arrange the pallets, wherein the optimal deep lanes layout indicates groups of pallets arranged in lanes separated by space for the plurality of robotic devices to travel;
based on the real-time robotics information, determining an amount of time to rearrange the pallets to the optimal deep lanes layout using one or more of the plurality of robotic devices; and
causing one or more of the plurality of robotic devices to rearrange the pallets to the optimal deep lanes layout based on the amount of time to rearrange the pallets to the optimal deep lanes layout being less than a threshold amount of time.

3. The method of claim 2 , wherein, in the optimal deep lanes layout, pallets having items expected to be shipped out of the warehouse within a threshold period of time from a present date are located proximal to a shipping dock in the warehouse.

4. The method of claim 2 , further comprising:
based on the real-time robotics information, estimating, for each of the pallets in the optimal deep lanes layout, (i) a quantity of robotic devices that will be needed to retrieve the pallet from a location of the pallet in the optimal deep lanes layout and (ii) an amount of time for the estimated quantity of robotic devices to retrieve the pallet from the location in the optimal deep lanes layout,
wherein causing one or more of the plurality of robotic devices to rearrange the pallets to the optimal deep lanes layout is further based on the estimated quantity of robotic devices and the estimated amount of time.

5. The method of claim 1 , further comprising:
based on the real-time robotics information, estimating, for each of the pallets in the optimal controlled-access dense grid layout, (i) a quantity of robotic devices that will be needed to retrieve the pallet from a location of the pallet in the optimal controlled-access dense grid layout and (ii) an amount of time for the estimated quantity of robotic devices to retrieve the pallet from the location in the optimal controlled-access dense grid layout,
wherein causing one or more of the plurality of robotic devices to rearrange the pallets to the optimal controlled-access dense grid layout is further based on the estimated quantity of robotic devices and the estimated amount of time.

6. The method of claim 1 , wherein determining the optimal controlled-access dense grid layout to which to arrange the pallets is further based on predetermined pallet locations in the warehouse for particular pallets, and
wherein, in the optimal controlled-access dense grid layout, the particular pallets are located at the predetermined pallet locations.

7. The method of claim 1 , wherein, in the optimal controlled-access dense grid layout, for each type of item in the warehouse, at least one pallet having the type of item is located remotely from at least one other pallet having the type of item.

8. The method of claim 1 , wherein determining the optimal controlled-access dense grid layout to which to arrange the pallets is further based on access to information identifying sets of two or more complementary items, and
wherein, in the optimal controlled-access dense grid layout, the two or more complementary items of each identified set are located proximate to one another.

9. The method of claim 1 , further comprising:
based on the real-time item information, determining that a quantity of a type of item in the warehouse exceeds a threshold surplus quantity,
wherein determining the optimal controlled-access dense grid layout to which to arrange the pallets is further based on the quantity of the type of item exceeding the threshold surplus quantity, and
wherein, in the optimal controlled-access dense grid layout, a majority of pallets having the type of item are located remotely from other pallets having the type of item and at a location proximal to the center of the optimal controlled-access dense grid layout.

10. The method of claim 1 , further comprising:
determining that an amount of space taken up in the warehouse by the pallets exceeds a threshold amount of space,
wherein at least a portion of the method is performed in response to determining that the amount of space taken up in the warehouse by the pallets exceeds the threshold amount of space.

11. The method of claim 1 , further comprising:
based on the real-time robotics information and other scheduled warehouse activities, allocating a time interval for recurring rearrangement of the pallets to a new optimal controlled-access dense grid layout,
wherein at least a portion of the method is periodically performed at the allocated time interval to optimize a layout of the pallets in the warehouse.

12. A system comprising:
a plurality of robotic devices in a warehouse;
at least one processor; and
data storage comprising instructions executable by the at least one processor to cause the system to perform operations comprising:
receiving real-time item information including real-time locations of pallets positioned in the warehouse and real-time inventory of items arranged on the pallets, wherein the real-time inventory of the items includes a content of each pallet;
determining a likelihood of demand for future access to the pallets based on a history of pallet relocation within the warehouse, an expectation of items to be received at the warehouse at a future date, and an expectation of items to be shipped out of the warehouse at a future date;
based on the real-time item information and the l

Assignees

• X Dev Llc

Inventors

• Hance Christopher
• Shaffer Daniel

Classifications

• G06Q10/06311

  Scheduling, planning or task assignment for a person or group · CPC title

• G06Q10/087

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• B65G1/0492

  with cars adapted to travel in storage aisles · CPC title

• G06Q10/06315

  Needs-based resource requirements planning or analysis · CPC title

• G06Q10/043 Primary

  Optimisation of two dimensional placement, e.g. cutting of clothes or wood · CPC title