Abstract: Examples described may enable consolidating pallets of items in a warehouse. An example method includes receiving real-time item information including pallet locations in a warehouse and inventory of items arranged on the pallets; based on the real-time item information, identifying a set of pallets of which at least one pallet includes less than a threshold quantity of a type of item; receiving real-time robotics information and determining, based on the real-time item and robotics information, an amount of time to condense the items on the set of pallets into a single pallet and a quantity of pallets that will become empty as a result of condensing the items; and, based on the amount of time being less than a threshold time and the quantity of pallets exceeding a threshold quantity of pallets, causing robotic devices to condense the items into the single pallet.

Abstract: An example system includes a robotic device deployed in a warehouse environment including a plurality of inventory items. The system also includes a camera coupled to the robotic device, configured to capture image data. The system also includes a computing system configured to receive the captured image data. The computing system is configured to, based on the received image data, generate a navigation instruction for navigation of the robotic device. The computing system is also configured to analyze the received image data to detect one or more on-item visual identifiers corresponding to one or more inventory items. The computing system is further configured to, for each detected visual identifier, (i) determine a warehouse location of the corresponding inventory item, (ii) compare the determined warehouse location to an expected location, and (iii) initiate an action based on the comparison.

Abstract: Examples described may enable consolidating pallets of items in a warehouse. An example method includes receiving real-time item information including pallet locations in a warehouse and inventory of items arranged on the pallets; based on the real-time item information, identifying a set of pallets of which at least one pallet includes less than a threshold quantity of a type of item; receiving real-time robotics information and determining, based on the real-time item and robotics information, an amount of time to condense the items on the set of pallets into a single pallet and a quantity of pallets that will become empty as a result of condensing the items; and, based on the amount of time being less than a threshold time and the quantity of pallets exceeding a threshold quantity of pallets, causing robotic devices to condense the items into the single pallet.

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.

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.

Abstract: Examples described may enable consolidating pallets of items in a warehouse. An example method includes receiving real-time item information including pallet locations in a warehouse and inventory of items arranged on the pallets; based on the real-time item information, identifying a set of pallets of which at least one pallet includes less than a threshold quantity of a type of item; receiving real-time robotics information and determining, based on the real-time item and robotics information, an amount of time to condense the items on the set of pallets into a single pallet and a quantity of pallets that will become empty as a result of condensing the items; and, based on the amount of time being less than a threshold time and the quantity of pallets exceeding a threshold quantity of pallets, causing robotic devices to condense the items into the single pallet.

Abstract: An example method includes receiving image data captured by a sensor on a robotic device. The robotic device is in a warehouse, including multiple inventory items stored at storage locations in the warehouse. Each inventory item has an on-item identifier that identifies it in a warehouse management system (WMS), each storage location has a storage-location identifier that identifies it in the WMS, and a first on-item identifier for a first inventory item is associated in the WMS with a first storage-location identifier for a first storage location. The method includes analyzing the received sensor data to detect an identifier captured by the sensor. The detected identifier includes one or both of the first on-item identifier and the first storage-location identifier. The method includes determining a warehouse location associated with the detected identifier and, based on the warehouse location, determining a location of the robotic device within the warehouse.

Abstract: Example methods and systems enable rearrangement of a warehouse to an optimal layout determined according to customizable goals. An example method includes receiving, at a warehouse management system (WMS), information of a warehouse and items of the warehouse, identifying an item shipment expectation including new items expected to be received at the warehouse at a future date and items present at the warehouse marked for delivery at the future date, determining an optimal layout of the items of the warehouse at a present date based on the item shipment expectation, determining an amount of time to rearrange the items, by one or more of robotic devices rearranging the items, to the optimal layout based on a time measurement for robotic devices to perform tasks, and determining to rearrange the items of the warehouse based on the amount of time to rearrange the items being less than a threshold amount of time.

Abstract: Examples described may enable consolidating pallets of items in a warehouse. An example method includes receiving real-time item information including pallet locations in a warehouse and inventory of items arranged on the pallets; based on the real-time item information, identifying a set of pallets of which at least one pallet includes less than a threshold quantity of a type of item; receiving real-time robotics information and determining, based on the real-time item and robotics information, an amount of time to condense the items on the set of pallets into a single pallet and a quantity of pallets that will become empty as a result of condensing the items; and, based on the amount of time being less than a threshold time and the quantity of pallets exceeding a threshold quantity of pallets, causing robotic devices to condense the items into the single pallet.

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.

Abstract: An example method is carried out in a warehouse environment having a plurality of inventory items located therein, each having a corresponding on-item identifier. The method involves determining a target inventory item having a target on-item identifier. The method also involves determining that a first inventory item having a first on-item identifier is loaded onto a first robotic device. The method further involves transmitting a request to verify the first on-item identifier. The method still further involves receiving data captured by a sensor of the second robotic device. The method yet further involves (i) analyzing the received data to determine the first on-item identifier, (ii) comparing the first on-item identifier and the target on-item identifier, and (iii) responsive to comparing the first on-item identifier and the target on-item identifier, performing an action.

Abstract: An example system includes a robotic device deployed in a warehouse environment including a plurality of inventory items. The system also includes a camera coupled to the robotic device, configured to capture image data. The system also includes a computing system configured to receive the captured image data. The computing system is configured to, based on the received image data, generate a navigation instruction for navigation of the robotic device. The computing system is also configured to analyze the received image data to detect one or more on-item visual identifiers corresponding to one or more inventory items. The computing system is further configured to, for each detected visual identifier, (i) determine a warehouse location of the corresponding inventory item, (ii) compare the determined warehouse location to an expected location, and (iii) initiate an action based on the comparison.

Abstract: An example method is carried out in a warehouse environment having a plurality of inventory items located therein, each having a corresponding on-item identifier. The method involves determining a target inventory item having a target on-item identifier. The method also involves determining that a first inventory item having a first on-item identifier is loaded onto a first robotic device. The method further involves transmitting a request to verify the first on-item identifier. The method still further involves receiving data captured by a sensor of the second robotic device. The method yet further involves (i) analyzing the received data to determine the first on-item identifier, (ii) comparing the first on-item identifier and the target on-item identifier, and (iii) responsive to comparing the first on-item identifier and the target on-item identifier, performing an action.