Staples, Inc.

Michael Bhaskaran

Daniel Jarvis

Vikranth Gopalakrishnan

Amit Kalra

Paolo Gerli Amador

Rodney Gallaway

Nitin Verma

Sandeep Sikka

Maimuna Rangwala

Weston Harris

Randall L Cogill

Xiaowei Lu

Christopher Yong

Vishal Musunuri

Girish Kulkarni

Paolo Geril Amador

A hybrid modular storage fetching system is described. In an example implementation, an automated guided vehicle of the hybrid modular storage fetching system includes a drive unit that provides motive force to propel the automated guided vehicle within an operating environment. The automated guided vehicle may also include a container handling mechanism including an extender and a carrying surface, the container handling mechanism having three or more degrees of freedom to move the carrying surface along three or more axes. The container handling mechanism may retrieve an item from a first target shelving unit using the carrying surface and the three or more degrees of freedom and place the item on a second target shelving unit. The automated guided vehicle may also include a power source coupled to provide power to the drive unit and the container handling mechanism.

Hybrid modular storage fetching system

A hybrid modular storage fetching system with a robot execution system (REX) is described. In an example implementation, a REX may induct, into the hybrid modular storage fetching system, an order identifying items to be fulfilled by automated guided vehicles (AGVs) at an order fulfillment facility. The REX may generate at task list including tasks for a first and second AGV, instruct the first AGV to retrieve a first item in the order from a first storage area based on the task list and deliver the first item to a pick-cell station. The REX may also instruct the second AGV to retrieve a second item of the order from a second storage area and deliver the second item to the pick-cell station. The REX may communicate with other components of the hybrid modular storage fetching system to coordinate the paths of the AGVs to fulfill the order.

The technology includes an example method for determining matching items. In some implementations, the method may determine a first set and second set of attributes and attribute types associated with a first and second item, respectively, based on description data associated with the items. The method may determine an attribute-type indicator for a pairing of a first attribute of the first set with a second attribute of the second set based on attribute types of the attributes, compute an attribute value for the pairing based on a similarity between the first and second attribute, and compute a match score for a combination of the first and second items based on the attribute value and attribute-type indicator. The method may also, in response to receiving a data query identifying the first item, provide a graphical user interface including a graphical element representing the second item based on the match score.

System and method for automatic product matching

In an example embodiment, a method may generate an item layout map for a shelving unit, where the shelving unit stores item types associated with a product category. The method may determine product attributes associated with the product category and divider configurations of the shelving unit, where a divider configuration divides the shelving unit into one or more candidate regions. The method may generate an ontology entry associated with a product attribute and the divider configuration based on the item layout map and the divider configuration of the shelving unit, the ontology entry indicating a regional entropy values of a product attribute for the candidate regions generated by the divider configuration. In some instances, the method may generate a guidance instruction related to the product attribute of the product category based on the ontology entry and provide the guidance instruction to a user.

Interactive machine learning assistant

A hybrid modular storage fetching system is described. In an example implementation, the system may include a warehouse execution system adapted to generate a picking schedule for picking pick-to-cart and high-density storage items, and an AGV dispatching system adapted to dispatch a cart automated guided vehicle and a modular storage fetching automated guided vehicle based on the picking schedule. The cart automated guided vehicle may be adapted autonomously transport a carton through a pick-to-cart area and to a pick-cell station. The modular storage fetching automated guided vehicle may be adapted to synchronously autonomously transport a modular storage unit containing items to be placed in the cartons from a high-density storage area to the pick-cell station.

A method determines a processing cluster including one or more stock keeping units (SKUs); divides the processing cluster into a first cluster and a second cluster based on SKU affinities between the one or more SKUs in the processing cluster; determines a first SKU of the first cluster to be replicated to the second cluster based on a demand correlation between the first SKU of the first cluster and a second SKU of the second cluster; replicates the first SKU of the first cluster to the second cluster; responsive to replicating the first SKU of the first cluster to the second cluster, determines whether the first cluster and the second cluster satisfy a defined constraint; and responsive to determining that the first cluster and the second cluster satisfy the defined constraint, assigns the first cluster to a first physical location and assigning the second cluster to a second physical location.

Automated guided vehicle control and organizing inventory items using stock keeping unit clusters

An automated guided vehicle (AGV) is described. In an example implementation, the AGV may include an AGV body; one or more elevator mechanisms coupled to the AGV body; a support surface coupled to the AGV body and vertically movable along the AGV body by the one or more elevator mechanisms, the support surface supporting an object from underneath the object when the object is placed on the support surface; one or more arms coupled to the AGV body and vertically movable along the AGV body by the one or more elevator mechanisms, the one or more arms articulating to move the object from a first position of the object and place the object on the support surface; and an AGV controller configured to control one or more operations of the AGV.

Automated guided vehicle for transporting objects

In an example embodiment, a method determines a container containing one or more items associated with one or more item types, the container located at a current position in a storage facility; determines one or more order likelihoods of the one or more item types contained in the container; determines a container utilization likelihood of the container based on the one or more order likelihoods of the one or more item types contained in the container; determines an optimal position for the container in the storage facility based on the container utilization likelihood of the container, the optimal position being different from the current position of the container; and instructing an automated guided vehicle (AGV) to transport the container from the current position of the container to the optimal position of the container in the storage facility.

Automated guided vehicle control and organizing inventory items using predictive models for slow item types

In an example implementation, a smart cart system may include a cart including a frame and an item holder supported by the frame and configured to transition between an open position and a storage position that is more compact than the open position. The smart cart system may include a motivator configured to provide a motive force to the cart, a power source coupled with the motivator, and a charging interface attached to the frame of the cart and that interacts with a cart charging system of a cart storage rack to charge the power source. The smart cart system may also include a user interface device, a computing device, and a smart cart controller operable on the computing device to receive information describing an attribute of an item and provide information describing the attribute of the item for presentation by the user interface device.

Autonomous shopping cart

In an embodiment, a method may determine a first candidate item type and a second candidate item type for a container. The method may determine appearance feature(s) of the first candidate item type and appearance feature(s) of the second candidate item type. The method may determine, using a trained dissimilarity model, a distinguishability score between the first candidate item type and the second candidate item type based on the appearance feature(s) of the first candidate item type and the appearance feature(s) of the second candidate item type. The method may determine to store the first candidate item type and the second candidate item type together in the container based on the distinguishability score between the first candidate item type and the second candidate item type. In some instances, the method may place item(s) of the first candidate item type and item(s) of the second candidate item type in the container.

Growing community of inventors

Sherborn, MA, United States of America

Michael Bhaskaran