Staples, Inc.

Packsize LLC

Rodney Gallaway

Michael Bhaskaran

Daniel Jarvis

Vikranth Gopalakrishnan

Paolo Gerli Amador

Amit Kalra

Andreas Harnesk

Jeffrey Lee Rasmussen

Eric Branch Bulloch

Kevin Bradon Maughan

Jeffrey L Rasmussen

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.

A computer system for optimizing node and edge selections within a digital graph model accesses a digital graph model of a physical warehouse location. The digital graph model comprises information indicating a location of multiple specific items of inventory among shelves in the physical warehouse location. The computer system identifies a set of orders that each comprise one or more items and an order priority. The computer system then maps each item in each order selected from the set or orders to the multiple nodes within the digital graph model. The computer system identifies a ranking node from the multiple nodes. The computer system then traverses one or more edges that extend from the ranking node to identify a shortest path within the digital graph model to fill a digital model of a picking cart above a packing threshold level.

Dynamic cart optimization system

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.

Technology for optimizing carton induction in an order fulfillment picking system is described. In an example embodiment, a method, implemented using one or more computing devices, may include receiving scan data reflecting status of cartons being conveyed by a conveying system, receiving confirmatory input reflecting pick completion for a subset of the cartons being conveyed by the conveying system, and generating an estimated time of arrival for one or more cartons not yet inducted into the conveying system based on the scan data and the confirmatory input. The method may further include generating a load forecast based on the estimated time of arrival and inducting one or more of the one or more cartons into the conveying system based on the load forecast.

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McRae, AR, United States of America

Rodney Gallaway