Mission planning system

Abstract

A mission planning method is directed to orchestrating robots, such as moving automated pallets within an environment. The method includes fulfilling orders based on incoming orders, assembling mixed items on automated pallets, reorganizing storage of automated pallets, consolidating stacks of items on automated pallets to create more empty automated pallets, providing inbound and outbound handling of automated pallets, directing automated robots for interaction activities, and dynamic scheduling due to unexpected delays or events.

Description

[0001] MISSION PLANNING SYSTEM

[0002] CROSS-REFERENCE TO RELATED APPLICATION

[0003] This application claims the benefit under 35 U.S.C. § 119(e) of co-pending U.S. Provisional Patent Application No. 63 / 668,990 titled MISSION PLANNING SYSTEM filed on July 9, 2024, which is herein incorporated by reference in its entirety for all purposes.

[0004] BACKGROUND OF THE DISCLOSURE

[0005] Traditional warehouse management systems (WMSs) and transportation management systems (TMSs) have evolved from manual, spreadsheet-based tracking methods to sophisticated, software- driven platforms. These systems are designed to improve the organization, efficiency, and flow of goods within and between facilities. Core WMS functions include tracking the arrival and departure of goods, maintaining precise inventory levels and locations, optimizing spatial utilization, and coordinating material handling tasks within a facility, such as a warehouse. TMS platforms complement these capabilities by managing the routing, scheduling, and execution of goods movement between facilities across land, sea, and air transportation modes. In many cases, WMS and TMS platforms operate as intermediaries, processing directives from higher-level enterprise resource planning (ERP) systems and other business platforms that govern supply chain priorities, orders, and inventory policies.

[0006] SUMMARY OF THE DISCLOSURE

[0007] Aspects of the present disclosure are directed to integrating WMS and TMS platforms, enabling coordinated control over end-to-end logistics operations, and directing robotic platforms to receive task directives from WMS / TMS via human-machine interfaces. Aspects of the present disclosure further are directed to providing a unified framework capable of natively orchestrating physical automation across multiple facilities, vehicle types, and robotic platforms in real time.

[0008] One aspect of the present disclosure is directed to a computer-implemented mission planning system comprising a storage device configured to store a plurality of basic unit instructions, one or more of the basic unit instructions having at least one procedure identifier, at least one processor coupled to the storage device and the communication network, and one or more components executable by the at least one processor and collectively configured to fulfill orders based on incoming orders, assemble mixed automated pallets, reorganize storage, consolidate stacks of automated pallets to create more empty automated pallets, provide inbound and outbound handling of automated pallets, schedule personnel for interaction activities, and dynamic schedule due to unexpected delays or events.

[0009] Embodiments of the computer-implemented mission planning system further may include efficiently processing incoming orders and generating the necessary actions to fulfill them accurately and timely. The mission planning system may optimize the assembly of mixed items on automated pallets, considering factors, such as item compatibility, weight distribution, and stacking constraints. The mission planning system may analyze the facility layout and may perform actions to optimize storage organization, ensuring efficient item retrieval and storage operations. The mission planning system may intelligently consolidate stacks of items to maximize available empty automated pallets, improving overall storage capacity and utilization. The mission planning system may manage the movement of goods during inbound and outbound processes, coordinating actions for efficient loading and unloading operations. The mission planning system may assign personnel to specific activities that require human intervention or interaction, ensuring efficient utilization of human resources. The mission planning system dynamically may adjust the schedule in response to unexpected delays or events, optimizing resource allocation and minimizing disruptions.

[0010] Another aspect of the present disclosure is directed to a mission planning system comprising fulfillment based on incoming orders, building mixed item automated pallets, reorganizing storage, consolidating stacks of items on automated pallets to create more empty automated pallets, providing inbound and outbound handling of automated pallets, scheduling personnel for interaction activities, and dynamic scheduling due to unexpected delays or events.

[0011] Embodiments of the mission planning system further may include efficiently processing incoming orders and generating the necessary actions to fulfill them accurately and timely. The mission planning system may optimize the assembly of mixed item automated pallets, considering factors such as item compatibility, weight distribution, and stacking constraints. The mission planning system may analyze the facility layout and may perform actions to optimize storage organization, ensuring efficient item retrieval and storage operations. The mission planning system may intelligently consolidate stacks to maximize available empty automated pallets, improving overall storage capacity and utilization. The mission planning system may manage the movement of goods during inbound and outbound processes, coordinating actions for efficient loading and unloading operations. The mission planning system may assign personnel to specific activities that require human intervention or interaction, ensuring efficient utilization of human resources. The mission planning system may dynamically adjust the schedule in response to unexpected delays or events, optimizing resource allocation and minimizing disruptions.

[0012] One aspect of the present disclosure is directed to a computer-implemented mission planning system for moving automated pallets within an environment. In one embodiment, the computer- implemented mission planning system comprises a storage device configured to store a plurality of basic unit instructions, one or more of the basic unit instructions having at least one procedure identifier, at least one processor coupled to the storage device and the communication network, and one or more components executable by the at least one processor and collectively configured to provide instructions to fulfill orders based on incoming orders, assemble mixed automated pallets, reorganize storage of automated pallets, consolidate stacks of automated pallets to create more empty automated pallets, provide inbound and outbound handling of automated pallets, direct automated robots for interaction activities, and dynamic schedule due to unexpected delays or events.

[0013] Embodiments of the mission planning system further may include configuring the at least one processor to process incoming orders and generate actions to fulfill the incoming orders. The at least one processor further may be configured to optimize the assembly of mixed automated pallets and consider factors, such as item compatibility, weight distribution, and stacking constraints. The at least one processor further may be configured to analyze the facility layout and perform actions to optimize storage organization, ensuring efficient item retrieval and storage operations. The at least one processor further may be configured to consolidate stacks to maximize available empty automated pallets, improving overall storage capacity and utilization. The at least one processor further may be configured to manage the movement of goods during inbound and outbound processes, coordinating actions for efficient loading and unloading operations. The at least one processor further may be configured to assign personnel to specific activities that require human intervention or interaction, ensuring efficient utilization of human resources. The at least one processor further may be configured to dynamically adjust the schedule in response to unexpected delays or events, optimizing resource allocation and minimizing disruptions.

[0014] Another aspect of the present disclosure is directed to a mission planning method for moving automated pallets within an environment. In one embodiment, the method comprises: fulfilling orders based on incoming orders; assembling mixed automated pallets; reorganizing storage of automated pallets; consolidating stacks of automated pallets to create more empty automated pallets; providing inbound and outbound handling of automated pallets; directing automated robots for interaction activities; and dynamic scheduling due to unexpected delays or events. Embodiments of the method further may include processing incoming orders and generating actions to fulfill the incoming orders. The method further may include optimizing the assembly of mixed automated pallets and considering factors, such as item compatibility, weight distribution, and stacking constraints. The method further may include analyzing the facility layout and performing actions to optimize storage organization, ensuring efficient item retrieval and storage operations. The method further may include consolidating stacks to maximize available empty automated pallets, improving overall storage capacity and utilization. The method further may include managing the movement of goods during inbound and outbound processes, coordinating actions for efficient loading and unloading operations. The method further may include assigning personnel to specific activities that require human intervention or interaction, ensuring efficient utilization of human resources. The method further may include dynamically adjusting the schedule in response to unexpected delays or events, optimizing resource allocation and minimizing disruptions.

[0015] Yet another aspect of the present disclosure is directed to a mission planning method configured to reduce a number of actions. In one embodiment, the method comprises: collecting specified data; locating automated pallets that have items; selecting one or more automated pallets to achieve a mission; implementing a stacking algorithm to determine whether items that are part of a same order are stacked together and how many empty automated pallets are needed to carry stacks; creating one or more stacks on an empty automated pallet; generating a ledger of unassigned actions; iterating over the ledger unassigned actions and assigning tasks based on priorities set by an enterprise resource planning system, number of personnel, and / or vehicle availability; and implementing a path planning system to determine exact moves required the one or more automated pallets.

[0016] Embodiments of the method further may include repeating the method steps described above. The method further may include maintaining track of a sequence of commands and personnel instructions. Collecting specified data includes orders of what items need to be taken in and / or released from the warehouse, where they are going to and from and when they will arrive or need to depart from the ERP, the time in which the vehicle carrying the arriving or departing items is scheduled to arrive or depart, respectively, as well as real-time updates on those projects as the vehicle’s situation changes, and the metadata on the items relevant to how they will be stacked on an automated pallet with other items going to the same destination, such as the case dimensions, mass, and box crush rating from the item master database. The method further may include, if an assigned task requires the movement of the one or more automated pallets, invoking a goalframe generator configured to process a list of assigned tasks and create structured data describing a facility environment and proposed moves of the one or more automated pallets.

[0017] BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

[0019] FIG. 1 is a view an area of an exemplary facility divided into a grid of cells;

[0020] FIG. 2 is a view of the area showing zones and uses within the grid of cells;

[0021] FIG. 3 is a view of the area showing cells being occupied by automated robots or having automated robots passing therethrough;

[0022] FIGS. 4A and 4B are views of the area showing a current state and a desired state, respectively;

[0023] FIGS. 5 A and 5B are views of the area showing movement of automated robots from origin cells to destination cells, respectively;

[0024] FIGS. 6 A and 6B are views of the area showing several path options for the movement of two automated robots from their origin cells to their destination cells, respectively;

[0025] FIGS. 7 A and 7B are views of the area showing a selected path for the movement of the two automated robots from their origin cells to their destination cells, respectively;

[0026] FIGS 8A, 8B and 8C are views of the area showing a current state, an interim state and a desired state, respectively;

[0027] FIGS. 9 A and 9B are views of the area showing the selected path for the movement of two automated pallets in the interim state, respectively, with a projected time being illustrated in FIG. 9B;

[0028] FIGS. 10A and 10B are views of the area showing another embodiment of the current state and the desired state, respectively;

[0029] FIGS 11 A and 1 IB are views of two automated robots showing a current state and a desired state, respectively;

[0030] FIG. 12 is a view of two single item pallets, two additional single item pallets, and a mixed item pallet;

[0031] FIG. 13 is a diagram of data flow within a path planning system;

[0032] FIG. 14 is a diagram of data flow within a mission planning system; FIG. 15 is a block diagram of one example of a computer system that may be used to perform processes and functions disclosed herein;

[0033] FIG. 16 is a flow diagram of a path planning method of an embodiment of the present disclosure; and

[0034] FIG. 17 is a flow diagram of a path planning method of another embodiment of the present disclosure.

[0035] DETAILED DESCRIPTION

[0036] To facilitate the seamless operation of large fleets of mobile robots across warehouse and logistics environments, a system and method of mission planning is disclosed herein. The mission planning system and method address the challenge of converting abstract, high-level tasks into precise robot control commands, personnel instructions, and comprehensive warehouse schedules. By effectively translating these abstract tasks into actionable commands and schedules, the mission planning system and method ensure the achievement of activities within the warehouse.

[0037] The mission planning system and method can be integrated with enterprise resource planning (ERP) systems, which generate tasks and operations that require labor-intensive execution. The integration of the systems results in the generation of comprehensive to-do lists, enabling streamlined and efficient task management.

[0038] The ability to convert abstract, high-level tasks into specific robot control commands is central to optimizing operations in a warehouse environment. This conversion allows for the automation of complex tasks, reducing human intervention and enhancing overall efficiency. The mission planning system and method disclosed herein improve the way warehouses and logistics environments operate, streamlining processes and maximizing productivity.

[0039] In summary, the disclosed mission planning system and method address the challenges associated with optimizing warehouse operations by converting abstract, high-level tasks into precise robot control commands, personnel instructions, and comprehensive schedules. The mission planning system and method enable large fleets of mobile robots to perform valuable activities within warehouse and logistics environments. By integrating these systems with ERP systems, the overall efficiency and organization of goods management arc improved.

[0040] In one embodiment, the mission planner embodies software system designed to operate on a computer located at the facility, generally referred to as an on-site node. The mission planner facilitates seamless communication with an interface network and automated robots within the facility. The mission planner consists of three main components: an action reducer, a goalframe generator, and a horizon planner.

[0041] The action reducer component continuously retrieves relevant data from cloud software and databases associated with the interface network, including information about robots, items, activities, transportation vehicles, and more. The action reducer converts this data into a detailed ledger of required actions necessary to accomplish activities or missions within the facility. The action reducer also considers optimization of the facility and robots, adding actions to enhance organization. Additionally, the action reducer can invoke a stacking algorithm to determine the number and exact configurations of stacks required to fulfill specific orders. As the ledger of actions is constructed, the action reducer assigns the appropriate robots and personnel necessary to perform these actions. This information is then passed to the goalframe generator.

[0042] The goalframe generator is invoked by the mission planner when actions generated by the action reducer require repositioning of entities within the facility. The goalframe generator takes the assigned robots and personnel for each action and composes this information into a sequential list of goalframes, which are structured data required by a path planning system. By creating this list of goalframes, the goalframe generator enables the invocation of the path planning system, which generates individual robot motion commands based on the goalframes.

[0043] The action reducer and goalframe generator components form a continuous loop, where the outputs of each component feed into the other. This loop ensures a seamless flow of information and coordination between the actions, assigned robots, and personnel. The loop can be run indefinitely, allowing for continuous operation of the facility enabled by an interface network system.

[0044] The horizon planner employs a parallel computing strategy to expedite the processes of the action reducer and goalframe generator. The horizontal planner is trained on previously collected data for the given facility and customer workflows, and leverages parallel computing techniques. An algorithm estimates a full schedule for the facility up to a parameterized “horizon” period, such as two days or three days by performing accelerated simulation. This estimation provides an overview of the planned activities and resources within the facility, allowing for proactive decision-making and optimization.

[0045] The mission planning system, enabled by the mission planner, can execute a variety of mission types, including but not limited to: • Fulfilling Orders for the Interface Network: The mission planning system efficiently processes incoming orders and generates the necessary actions to fulfill them accurately by a deadline set by the interface network.

[0046] • Building Mixed Pallets: The interface network is configured to optimize the assembly of mixed pallets by personnel or pick and place robots, defined herein as the process of stacking multiple boxes containing distinct item types, each potentially varying in shape, size, and mass, onto a standard or automated shipping pallet. The system considers parameters, such as item compatibility, weight distribution, load capacity of cartons, and spatial constraints to generate optimized “stacks” or vertical columns of boxes arranged adjacent to one another. These stacks arc placed on a shipping pallet, which is a flat transport structure designed to support one or more items in a stable manner for conveyance within and between warehouse facilities. The optimized stacking logic ensures that the resulting load maintains structural integrity, balance, and protection of items during transportation, thereby reducing the risk of product damage in transit.

[0047] • Reorganizing Storage: The mission planning system analyzes the facility layout and performs actions to optimize storage organization, ensuring efficient item retrieval and storage operations.

[0048] • Consolidating Stacks to Create More Empty Automated Pallets: The mission planning system intelligently consolidates stacks to maximize available empty automated pallets, improving overall storage capacity and utilization.

[0049] • Inbound & Outbound Handling: The mission planning system manages the movement of goods during inbound and outbound processes, coordinating actions for efficient loading and unloading operations.

[0050] • Scheduling Personnel for Interaction Activities: The mission planning system assigns personnel to specific activities that require human intervention or interaction, ensuring efficient utilization of human resources.

[0051] • Dynamic Scheduling due to Unexpected Delay s / Events: The mission planning system dynamically adjusts the schedule in response to unexpected delays or events, optimizing resource allocation and minimizing disruptions.

[0052] • Accommodates plans for maintenance activities of any robotic entity operating within the facility.

[0053] In one embodiment, a mission planning sequence includes the following steps.

[0054] 1. The cloud collects the following data: a. Orders of what items need to be taken in and / or released from the warehouse, where they are going to and from and when they will arrive or need to depart from the ERP. b. The time in which the vehicle carrying the arriving or departing items is scheduled to arrive or depart, respectively, as well as real-time updates on those projects as the vehicle’s situation changes. c. The metadata on the items relevant to how they will be stacked on an automated pallet with other items going to the same destination, such as the case dimensions, mass, and box crush rating from the item master database.

[0055] The mission planner receives this information from the cloud software.

[0056] 2. The action reducer finds all automated pallets that have the required items, and optimally chooses which ones to use to accomplish each mission. This decision is based on accessibility of the automated pallet within the storage area and the availability of the required quantity of items on the automated pallet.

[0057] 3. The action reducer invokes the stacking algorithm to figure out how items that are part of the same order are optimally stacked together and how many empty (outbound) automated pallets will be needed to carry those stacks and then creates the exact stacks required to be built and pairs the stacks with empty automated pallets.

[0058] 4. The action reducer generates a ledger of unassigned actions by decomposing the overall mission. An example of an action in this ledger is: move Pallet 1 to picking cell position [CIO] on the grid. The action reducer is configured with a set of parameters that change how actions are created. For clarity, everything in the warehouse and the back of the truck when parked at the loading dock of a warehouse are divided into a grid of cells the size of an automated pallet that an automated pallet can occupy in certain ways based on the cell’s designated function. Storage cells allow automated pallets to park in the facility or inside a truck, circulation cells allow automated pallets to pass through but not stop, and interaction cells allow automated pallets to park while a human or other robot manipulates the items it is carrying.

[0059] 5. The action reducer begins to iterate over the ledger of unassigned actions and assigns tasks based on priorities set by the ERP, number of personnel, truck availability, and many other dynamic events. In addition, the action reducer is configured with a set of parameters that change how actions are assigned.

[0060] 6. If an assigned task requires the movement of automated pallets (or other robots that are controlled by the mission planner in the future), then the goalframe generator is invoked. 7. The goalframe generator takes a list of assigned tasks that require robot motion and creates structured data (called a goalframe) describing the zone’s state prior to any robot moves, and desired state post robot moves.

[0061] 8. The goalframe generator then invokes the path planning system to work out the exact moves required to satisfy the desired state of the goalframe. This is essentially how many grid spaces in which direction and which order are required to get from the automated pallet origin cell to the desired destination cell.

[0062] 9. Steps 5 - 8 are looped until the mission is complete.

[0063] 10. The horizon planner keeps track of the sequence of robot commands as well as personnel instructions generated within the mission planner. The mission planner can spawn another thread of computation to project schedules for days in advance or as far out as the data collected in step on is available.

[0064] 11. The sequences of commands (otherwise known as plans) created by the mission planner are sent to the robot control system at the appropriate time and ordered to begin true movement and activity within the facility.

[0065] In summary, the mission planner, sometimes referred to a mission planning system, incorporates the action reducer, goalframe generator, and horizon planner components to enable efficient planning, coordination, and scheduling of activities within the facility. The mission planner supports a variety of mission types, allowing for seamless fulfillment of orders, pallet building, storage reorganization, and more. The ability of the mission planning system to dynamically adapt to unexpected events ensures optimal resource utilization and operational efficiency.

[0066] The following provides technical information regarding the process of resolving a mission using the interface network system. The example illustrates the data flow and steps involved in fulfilling a specific order, but it is important to note that the disclosed approach can be applied to various types of missions.

[0067] Retrieval of order information and item metadata:

[0068] The mission planning system receives the order information from the cloud software. Additionally, the mission planning system retrieves the required metadata about the items from the item master database of the interface network, which is a data architecture that maintains the collective item data of all enabled commercial facilities including weight, dimensions, carton type and universal codes.

[0069] Robotic entity selection: The action reducer component searches for robotic entities, including automated pallets and automated picking modules, within the facility that contain the required items. Using optimization algorithms, the action reducer determines the optimal selection of robotic entities to fulfill the mission efficiently.

[0070] Stacking algorithm:

[0071] The action reducer invokes the stacking algorithm to determine the number of outbound automated pallets required and the specific stacks that need to be built to accommodate the order.

[0072] Generation of unassigned actions:

[0073] The action reducer decomposes the overall mission and generates a ledger of unassigned actions. Each action represents a specific task that needs to be performed. For example, an action could be moving Automated Pallet 1 to picking cell ID CIO on the grid so that the picking module can move items from Automated Pallet 1 to Automated Pallet 2 currently in picking cell ID C 11 as part of building a stack of mixed pallets. The creation of actions is configurable using parameters that allow for customization.

[0074] Assignment of tasks:

[0075] The action reducer iterates over the ledger of unassigned actions and assigns tasks based on various factors, such as priorities, the availability of robots, personnel, transportation vehicles, and dynamic events. Task assignment is configurable using parameters that allow for flexibility and customization.

[0076] Invocation of the goalframe generator:

[0077] If an assigned task requires the movement of robotic entities integrated into the interface network system, the goalframe generator is invoked. This component takes the list of assigned tasks that require robot motion and generates structured data known as a goalframes. A goalframe describes a current state of the facility and a desired state after all selected robotic entities are moved.

[0078] Creation of goalframes:

[0079] The goalframe generator creates goalframes by representing the current state of the zone and the desired state in a structured format. The goalframe provides a visual representation of the zone, including automated pallets, picking modules, picking personnel, and the areas to be altered for the mission. FIGS. 1A and IB illustrate examples of a goalframe showing a current state of a zone and a desired state of the zone. FIG. 1A shows a storage area and FIG. IB shows an interaction area. Specifically, squares, each indicated at 2, represent picking personnel or automated pallets located in an interaction area. Squares, each indicated at 4, represent automated pallets. Squares, each indicated at 6, represent automated pallets that are required to move for the purpose of the stated mission. Squares, each indicated at 8, represent automated pallets that are moved by the picking personnel.

[0080] Path planning:

[0081] The mission planner invokes the path planning system, which utilizes the goalframes to determine the exact robot moves required to achieve the desired state. The path planning system generates detailed robot motion commands based on the information provided in the goalframes.

[0082] Looping of steps:

[0083] As previously described, steps 5 to 8 are looped until the mission is completed. This iterative process ensures continuous coordination, task assignment, and path planning based on the evolving state of the mission and facility.

[0084] Horizon planner:

[0085] The horizon planner tracks the sequence of robot commands and personnel instructions generated within the mission planning system. The horizon planner has the ability to project schedules for future days by spawning another thread of computation. This allows for advanced planning and scheduling of activities within the facility.

[0086] Execution of plans:

[0087] The sequences of commands and plans generated by the mission planning system are sent to a robot control system at the appropriate time to initiate real movement and activity within the facility. This enables the execution of planned tasks and ensures coordinated operations.

[0088] The described example demonstrates a specific mission type, but it serves as a representation of the general data How for all missions within the interface network system. By following this approach, the system can efficiently process orders, coordinate tasks, and optimize operations within a facility.

[0089] The following detailed description provides technical information regarding the method used to optimize mission planning in warehousing and logistics. This method addresses the challenge of creating a mission planning algorithm that is optimized for specific scenarios based on the industry and the facility that the interface network system is operating. The optimization process consists of two phases: achieving a generalizable base and fine-tuning the algorithm for production-ready deployment.

[0090] Determination of standardized simulation scenarios and metrics:

[0091] To evaluate the performance of the mission planning algorithms, standardized simulation scenarios are established. These scenarios represent various warehouse logistics situations, such as order fulfillment, item storage, and personnel allocation. Additionally, appropriate metrics are formulated into an objective function that is configured to measure an effectiveness of the algorithm. This objective function considers metrics including time, energy, consumption, resource allocation and customer satisfaction.

[0092] Attainment of a generalizable base version of mission planning algorithms: la. Initial Implementation: The optimization process starts with a simple implementation of the mission planning algorithms that may not perform optimally. lb. Algorithm Competition: Multiple versions of the mission planning algorithms are developed and pitted against each other in simulation scenarios. The results of these simulations are analyzed to identify strengths and weaknesses of each version. lc. Iterative Enhancement: Based on the analysis from step lb, the algorithm versions arc iteratively enhanced to handle standard scenarios effectively. This involves modifying algorithms, introducing new strategies, and refining decision-making processes. This enhancement process includes both manual refinement by humans and automated refinement by a machine learning process, e.g., an Al system. ld. Large-Scale Scenario Testing: The successful algorithm candidates resulting from the iterative enhancements are tested against a large set of programmatically generated scenarios. This extensive testing helps identify any remaining issues or limitations. le. Success Threshold: The algorithm candidates undergo further iterations until achieving a success rate of at least 80% across the large set of scenarios. This success rate serves as the benchmark for a generalizable base version of the mission planning algorithms.

[0093] Incorporation of real customer scenarios:

[0094] 2a. Context-Based Rule Integration: Before deploying the interface network system to specific customers, the base mission planner algorithms are modified by adding additional rules that are context-based and specific to real customer scenarios. These rules are designed to address unique requirements or constraints that arise in practical warehouse logistics operations.

[0095] 2b. Performance Validation: The modified algorithms are rigorously tested against standard scenarios, non-standard scenarios, and customer- specific scenarios to ensure then- performance meets the desired optimization criteria. This validation process includes assessing efficiency, resource allocation, customer satisfaction, and other relevant metrics.

[0096] Through this method of iterative enhancements, the mission planning algorithms are systematically optimized for specific warehouse scenarios.

[0097] Referring to the drawings, and more particularly to FIG. 1, an area of an exemplary facility is generally indicated at 10. As shown, the area 10 is divided into a grid of cells, with each cell being indicated at 12. Although the area 10 is shown to include 110 cells 12 (a 10 by 11 grid), any number of cells 12 can be used to determine movement of automated pallets within the area 10.

[0098] Referring to FIG. 2, the cells 12 within the area 10 can be configured to include zones and uses within the area 10. In one example, the zones may include a zone of obstructed cells indicated at 14, a zone of interaction cells indicated at 16, a zone of circulation cells indicated at 18, and a zone of storage cells indicated at 20. The zones 14, 16, 18, 20 may be varied depending on the shape and size of the facility.

[0099] Referring to FIG. 3, the cells are shown with a representation of automated pallets indicated by square 22, the automated pallets sometimes being referred to as mobile robots. The automated pallets 22 are shown as either occupying the cells 12 or moving through adjacent cells 12. Specifically, the automated pallet 22 can be shown entirely within the cell 12 or the automated pallet 22 can be shown to be positioned between two adjacent cells 12.

[0100] Referring to FIGS. 4A and 4B, FIG. 4A shows a current state of the cells 12 of the area 10 of the facility and FIG. 4B shows a desired state of the cells 12 of the area 10 of the facility. In one embodiment, the goalframe generator is configured to create goalframes by representing the current state of the area 10 and the desired state of the area 10 in a structured format. The goalframe provides a visual representation of the area 10, including automated pallets 22, picking modules (not designated), picking personnel 24, and cells 12 of the area to be altered to complete the mission. As shown, automated pallets 22 identified at A, B, C, D are shown to move from cells 12 shown in FIG. 4A to different cells 12 shown in FIG. 4B.

[0101] Referring to FIGS. 5A and 5B, as noted, automated pallets 22 are identified at A, B, C, D. In FIG. 5A, the automated pallets 22 identified at A, B, C, D are positioned within their respective origin cells 22. In FIG. 5B, the automated pallets 22 identified at A, B, C, D are shown in their desired destination cells 12. In one embodiment, a human or a robotic picker, e.g., picking personnel 24, can be provided to move items from one automated pallet 22 to another automated pallet 22, e.g., to and from automated pallet 22 identified at B.

[0102] Referring to FIGS. 6 A and 6B, the path planning system can be configured to illustrate several path options for the movement of two automated pallets 22 from their origin cells 12 to their destination cells 12, respectively. The path planning system can be configured to move automated pallets, e.g., automated pallets 22 identified at C, D, from their original cells 12 shown in the current state (FIG. 6A) to the destination cells 12 shown in the desired state (FIG. 6B). The planning path system further can be configured to provide several path options, which are indicated at 26. The path planning system rewards a path that does not require automated pallets 22 to stop within cells 12 of the circulation zone 18, thereby minimizing the total move count, and rewards a path that does not move other automated pallets 22.

[0103] Referring to FIGS. 7 A and 7B, a selected path 28 indicates the movement of the two automated pallets 22 identified at C, D from their origin cells 12 to their destination cells 12, respectively. Specifically, the selected path 28 defines the movement of the automated pallet C to the cells 12 of the circulation zone 18 first to not disturb other activities in the interaction zone. Recognizing that the desired state of automated pallet 22 identified at C is the cell 12 that automated pallet 22 identified at D occupies in the current state, the mission planner may be configured to request a path plan for the automated pallet identified at D and execute the movement of automated pallet 22 identified at D before automated pallet 22 identified at C arrives so that automated pallet 22 identified at C is not waiting in a cell 12 in the circulation zone 18 for the desired cell 12 in the storage zone 20 to clear.

[0104] Referring to FIGS 8A, 8B and 8C, the area 10 is shown in a current state, an interim state and a desired state, respectively. In one embodiment, the action reducer of the mission planning system is employed. For example, the action reducer may recognize that automated pallet 22 identified at B can reach a desired state by moving automated pallet 22 identified at E along path 30 or by moving automated pallet 22 identified at F along path 32, but also recognizing automated pallet 22 identified at E must also move to create a path for automated pallet 22 identified at A, thereby reducing an action by having both automated pallets 22 identified at A and B take the path 30 through current positions of automated pallet 22 identified at E.

[0105] Referring to FIGS. 9 A and 9B, selected paths 34, 36 for the movement of two automated pallets 22 identified at A, C in the interim state, respectively, is shown. For example, the path planning system is configured to determine the paths 34, 36 of both automated pallet 22 identified at A and automated pallet 22 identified at C must cross the same cell 12. The path planning system ensures that the shared cell 12 is not occupied at the same time with a buffer. FIG. 9B further illustrates time stamps on approximate times in which the automated pallet 22 identified at A and the automated pallet 22 identified at C enter certain cells 12. In one example, the automated pallet 22 identified at A enters a sequence of cells 22 at times 0:00 to 0:13 and the automated pallet 22 identified at C enters a sequence of cells 22 at times 0:00 to 0:05. At the cell 22 identified by “A 0:08” and “C 0:04” the automated pallets 22 identified by A and B pass through the cell 22 at different times to ensure that the shared cell 22 is not occupied at the same time, with a buffer being provided. Referring to FIGS. 10A and 10B, a function of the horizon planner is storage reorganization. In one example, the current state in FIG. 10A shows the end of a shift. The horizon planner sets the priority of each automated pallet 22 for the upcoming work period. For example, automated pallets 22 assigned priority 1 are needed first in the next shift, automated pallets 22 assigned priority 2 are required in a subsequent shift, and automated pallets 22 assigned priority 3 are not needed within the current planning horizon. There can be an infinite number of priority levels. To minimize retrieval time and effort, the horizon planner simulates multiple scenarios and evaluates which sequence of goalframes strategically place automated pallets 22 based on accessibility. The sequence of goalframes is stored for the mission planner to consider during execution. For example, priority 1 automated pallets are stored closest to cells 12 in the circulation zone 18 or empty cells 12 in the storage zone 20 with direct access to the cells 12 in the circulation zone 18, priority 2 automated pallets 22 occupy intermediate positions, and priority 3 automated pallets 22 are placed in the least accessible locations, e.g., far from cells 12 of the circulation zone 18 or behind multiple blocking moves.

[0106] Referring to FIGS 11 A and 1 IB, FIG. 11 A shows two automated pallets 22a, 22b in a current state and FIG. 1 IB shows the same two automated pallets 22a, 22b in a desired state. Specifically, the current state of FIG. 11 A shows two automated pallets 22a, 22b carrying an item, each indicated at 40, which is identified by A. In many warehouses, it is common to store on the automated pallet 22 more items 40 than can the automated pallet can handle and to pick from multiple automate pallets simultaneously to support concurrent operations. With the shown embodiment, as inventory on the two automated pallets 22a, 22b decreases, and their combined quantity falls below the maximum capacity of a single automated pallet 22a or 22b, the system consolidates the remaining items onto one automated pallet 22b. This frees up the second automated pallet 22a, allowing it to be repurposed for another task.

[0107] Referring to FIG. 12, two single item pallets indicated at 22c, 22d, two additional single item pallets indicated at 22e, 22f, and a mixed item pallet indicated at 22g are shown. Specifically, in one example, automated pallet 22c supports items 40 indicated at B, automated pallet 22d supports items 40 indicated at D, automated pallet 22e supports items 40 indicated at A, automated pallet 22f supports items indicated at C, and automated pallet 22g supports mixed items indicated at A, B, C, D. The stacking algorithm is configured to analyze box size, box strength, box weight and likeness of items (such as toothpaste and toothbrushes that will go next to each other in a retail store) and builds the optimal stack on the automated pallet 22. The mission planner of the mission planning system requests paths from the path planner of the path planning system to bring the single item automated pallets 22 from the cells 12 in the storage zone to the cells 12 in the interaction zone 16 where the empty mixed automated pallet 22 is located to be picked. The mission planner moves the single item automated pallet 22, e.g., automated pallet 22g, to the cells 12 in the interaction zone 16 in the order in which their items will be stacked on the mixed automated pallet.

[0108] Referring to FIG. 13, data is shown flowing within the path planning system. As shown, data flows from a goalframe module 50 to a multiagent interpolation algorithm module 52 to a multistep move computation module 54 to a mission planner module 56. As data flows from the goalframe module 50 to the multiagent interpolation algorithm module 52, information for the current and desired automated pallet position and the grid layout, indicated at 58, is transferred to the multiagent interpolation algorithm module 52. As data flows from the multiagcnt interpolation algorithm module 52 to the multistep move computation module 54, a sequence of moves including one or more automated robot commands, indicated at 60, are transferred to the multistep move computation module 54. As data flows from the multistep move computation module 54 to the mission planner module 56, a sequence of moves, indicated at 62, allows for more than single grid space movements per automated pallet 22. In one embodiment, the data may include a JavaScript Object Notation (JSON object), which is a data structure used for storing and transmitting data in a human-readable format.

[0109] Referring to FIG. 14, information on orders indicated at 70, vehicle data indicated at 72 and item metadata indicated at 74 is transmitted to a cloud network 76 and fed to an action reducer 78, a goalframe generator 80, a goalframe 82 and an automated pallet or robot control system 84. The information may be transmitted to the cloud network 76 through the internet, utilizing suitable networking technologies and protocols. The action reducer 78 is configured to share information and the reduce the number of actions with a stacking algorithm 86 and a ledger 88. The goalframe 82 is configured to share information with a horizon planner 90 and a path planner 92. The inputs from the cloud network 76 may be sent to both the horizon planner 90 and to a mission planner. Information is fed back to the action reducer 78 from the goalframe 82 to optimize the function of the action reducer 78. Once a suitable path is determined, the goalframe 82 provides commands to the automated pallet control system 84 to control movement of automated pallets 22 within the cells 12 of the area 10. The horizon planner 90 planner may be configured to perform accelerated simulations based on the current state of the facility, along with inputs from the cloud network 76, and generate branches of scenarios. The mission planner may be configured to evaluate outputs from the horizon planner 90 and determine whether to either continue just-in-time planning or follow a sequence from horizon planner 90 instead. The horizon planner 90, based on information from the mission planner, may be configured to determine if a sequence of movements of the automated pallets has already been computed. If the sequence has already been determined, then there is no need to recompute or provide a new sequence (just simply continue computing or optimizing the sequence later down the line). If the sequence has not already been determined, the horizon planner starts from current state and begins evaluating new sequences.

[0110] Various controllers may execute various operations discussed above. For example, the controller may be configured to perform the methods of the mission planning system. Using data stored in associated memory and / or storage, the controller may execute one or more instructions stored on one or more non-transitory computer-readable media, which the controller may include and / or be coupled to, that may result in manipulated data. In some examples, the controller may include one or more processors or other types of controllers. In one example, the controller is or includes at least one processor. In another example, the controller performs at least a portion of the operations discussed above using an application- specific integrated circuit tailored to perform particular operations in addition to, or in lieu of, a general-purpose processor. As illustrated by these examples, examples in accordance with the present disclosure may perform the operations described herein using many specific combinations of hardware and software and the disclosure is not limited to any particular combination of hardware and software components. Examples of the disclosure may include a computer-program product configured to execute methods, processes, and / or operations discussed above. The computer-program product may be, or include, one or more controllers and / or processors configured to execute instructions to perform methods, processes, and / or operations discussed above.

[0111] In one example, referring to FIG. 15, an exemplary computer system is generally indicated at 100. As shown, the computer system 100 includes one or more computers that exchange information. More specifically, the computer system 100 includes computers and / or devices 102, 104, 106. As shown, the computers and / or devices 102, 104, 106 are interconnected by, and may exchange data through, a communication network 108. The network 108 may include any communication network through which computer systems may exchange data. To exchange data using the network 108, the computers and / or devices 102, 104, 106 and the network 108 may use various methods, protocols and standards. To ensure data transfer is secure, the computers and / or device 102, 104, 106 may transmit data via the network 108 using a variety of security measures including, for example, TLS, SSL or VPN. While the computer system 100 illustrates three networked computers and / or device 102, 104, 106, the computer system 100 is not so limited and may include any number of computers and / or device, networked using any medium and communication protocol. As shown, the computer system 100 includes a processor 110, a memory 112, an interconnection element 114, an interface 116, such as a user interface or screen, and a data storage element 118. To implement at least some of the aspects, functions and processes disclosed herein, the processor 110 performs a series of instructions that result in manipulated data. The processor 110 may be any type of processor, multiprocessor or controller. The processor 110 may be connected to other system components, including one or more memory devices 112, by the interconnection element 114. The memory 112 stores programs and data during operation of the computer system 100. The interconnection element 114 may include one or more physical busses, for example, busses between components that arc integrated within a same machine, but may include any communication coupling between system elements including specialized or standard computing bus technologies. The interconnection element 114 enables communications, such as data and instructions, to be exchanged between system components of the computer system 250. The interface device 116 may receive input or provide output. More particularly, output devices may render information for external presentation. Input devices may accept information from external sources. Examples of the interface device 116 include keyboards, mouse devices, trackballs, microphones, touch screens, printing devices, display screens, speakers, network interface cards, etc. The interface device 116 allows the computer system 100 to exchange information and to communicate with external entities, such as users and other systems. The data storage element 118 includes a computer-readable and writeable nonvolatile, or non- transitory, data storage medium in which instructions are stored that define a program or other object that is executed by the processor 110. The data storage element 118 also may include information that is recorded, on or in, the medium, and that is processed by the processor 110 during execution of the program. More specifically, the information may be stored in one or more data structures specifically configured to conserve storage space or increase data exchange performance. The instructions may be persistently stored as encoded signals, and the instructions may cause the processor 110 to perform any of the functions described herein.

[0112] In operation, the processor 110 or some other controller causes data to be read from the nonvolatile recording medium into another memory, such as the memory 112, that allows for faster access to the information by the processor 110 than does the storage medium included in the data storage element 118. The memory 112 may be located in the data storage element 118 or in the memory; however, the processor 110 manipulates the data within the memory 112, and then copies the data to the storage medium associated with the data storage element 118 after processing is completed. A variety of components may manage data movement between the storage medium and other memory elements and examples are not limited to particular data management components. Further, examples are not limited to a particular memory system or data storage system.

[0113] Referring to FIG. 16, in one embodiment, a mission planning method for moving automated pallets within an environment is generally indicated at 200. As shown, the method includes at step 202 fulfilling orders based on incoming orders, at step 204 assembling mixed automated pallets, at step 206 reorganizing storage of automated pallets, at step 208 consolidating stacks of automated pallets to create more empty automated pallets, at step 210 providing inbound and outbound handling of automated pallets, at step 212 directing automated robots for interaction activities, and at step 214 dynamic scheduling due to unexpected delays or events. In some embodiments, the method 200 further may include processing incoming orders and generating actions to fulfill the incoming orders, optimizing the assembly of mixed automated pallets and considering factors, such as item compatibility, weight distribution, and stacking constraints, analyzing the facility layout and performing actions to optimize storage organization, ensuring efficient item retrieval and storage operations, consolidating stacks to maximize available empty automated pallets, improving overall storage capacity and utilization, managing the movement of goods during inbound and outbound processes, coordinating actions for efficient loading and unloading operations, assigning personnel to specific activities that require human intervention or interaction, ensuring efficient utilization of human resources, and / or dynamically adjusting the schedule in response to unexpected delays or events, optimizing resource allocation and minimizing disruptions.

[0114] Referring to FIG. 17, in another embodiment, a mission planning method configured to reduce a number of actions is generally indicated at 300. As shown, the method includes at step 302 collecting specified data, at step 304 locating automated pallets that have items, at step 306 selecting one or more automated pallets to achieve a mission, at step 308 implementing a stacking algorithm to determine whether items that are part of a same order are stacked together and how many empty automated pallets are needed to carry stacks, at step 310 creating one or more stacks on an empty automated pallet, at step 312 generating a ledger of unassigned actions, at step 316 iterating over the ledger unassigned actions and assigning tasks based on priorities set by an enterprise resource planning system, number of personnel, and / or vehicle availability, and at step 316 implementing a path planning system to determine exact moves required the one or more automated pallets. In some embodiments, the method 300 further may include repeating steps 302-316. The method 300 further may include maintaining track of a sequence of commands and personnel instructions. Collecting specified data may include orders of what items need to be taken in and / or released from the warehouse, where they are going to and from and when they will arrive or need to depart from the ERP, the time in which the vehicle carrying the arriving or departing items is scheduled to arrive or depart, respectively, as well as realtime updates on those projects as the vehicle’s situation changes, and the metadata on the items relevant to how they will be stacked on an automated pallet with other items going to the same destination, such as the case dimensions, mass, and box crush rating from the item master database. The method 300 further may include, if an assigned task requires the movement of the one or more automated pallets, invoking a goalframe generator configured to process a list of assigned tasks and create structured data describing a facility environment and proposed moves of the one or more automated pallets.

[0115] Having thus described several aspects of at least one embodiment, it is to be appreciated various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure and are intended to be within the scope of the disclosure. Accordingly, the foregoing description and drawings are by way of example only.

[0116] What is claimed is:

Claims

CLAIMS1. A computer- implemented mission planning system for moving automated pallets within an environment, comprising: a storage device configured to store a plurality of basic unit instructions, one or more of the basic unit instructions having at least one procedure identifier; at least one processor coupled to the storage device and the communication network; and one or more components executable by the at least one processor and collectively configured to provide instructions to: fulfill orders based on incoming orders; assemble mixed automated pallets; reorganize storage of automated pallets; consolidate stacks of automated pallets to create more empty automated pallets; provide inbound and outbound handling of automated pallets; direct automated robots for interaction activities; and dynamic schedule due to unexpected delays or events.

2. The computer-implemented mission planning system of claim 1, wherein the at least one processor further is configured to process incoming orders and generate actions to fulfill the incoming orders.

3. The computer-implemented mission planning system of claim 1, wherein the at least one processor further is configured to optimize the assembly of mixed automated pallets and consider factors, such as item compatibility, weight distribution, and stacking constraints.

4. The computer-implemented mission planning system of claim 1, wherein the at least one processor further is configured to analyze the facility layout and perform actions to optimize storage organization, ensuring efficient item retrieval and storage operations.

5. The computer-implemented mission planning system of claim 1, wherein the at least one processor further is configured to consolidate stacks to maximize available empty automated pallets, improving overall storage capacity and utilization.

6. The computer-implemented mission planning system of claim 1, wherein the at least one processor further is configured to manage the movement of goods during inbound and outbound processes, coordinating actions for efficient loading and unloading operations.

7. The computer-implemented mission planning system of claim 1, wherein the at least one processor further is configured to assign personnel to specific activities that require human intervention or interaction, ensuring efficient utilization of human resources.

8. The computer-implemented mission planning system of claim 1, wherein the at least one processor further is configured to dynamically adjust the schedule in response to unexpected delays or events, optimizing resource allocation and minimizing disruptions.

9. A mission planning method for moving automated pallets within an environment, the method comprising: fulfilling orders based on incoming orders; assembling mixed automated pallets; reorganizing storage of automated pallets; consolidating stacks of automated pallets to create more empty automated pallets; providing inbound and outbound handling of automated pallets; directing automated robots for interaction activities; and dynamic scheduling due to unexpected delays or events.

10. The method of claim 9, further comprising processing incoming orders and generating actions to fulfill the incoming orders.

11. The method of claim 9, further comprising optimizing the assembly of mixed automated pallets and considering factors, such as item compatibility, weight distribution, and stacking constraints.

12. The method of claim 9, further comprising analyzing the facility layout and performing actions to optimize storage organization, ensuring efficient item retrieval and storage operations.

13. The method of claim 9, further comprising consolidating stacks to maximize available empty automated pallets, improving overall storage capacity and utilization.

14. The method of claim 9, further comprising managing the movement of goods during inbound and outbound processes, coordinating actions for efficient loading and unloading operations.

15. The method of claim 9, further comprising assigning personnel to specific activities that require human intervention or interaction, ensuring efficient utilization of human resources.

16. The method of claim 9, further comprising dynamically adjusting the schedule in response to unexpected delays or events, optimizing resource allocation and minimizing disruptions.

17. A mission planning method configured to reduce a number of actions, the method comprising: a) collecting specified data; b) locating automated pallets that have items; c) selecting one or more automated pallets to achieve a mission; d) implementing a stacking algorithm to determine whether items that are part of a same order are stacked together and how many empty automated pallets are needed to carry stacks; e) creating one or more stacks on an empty automated pallet; f) generating a ledger of unassigned actions; g) iterating over the ledger unassigned actions and assigning tasks based on priorities set by an enterprise resource planning system, number of personnel, and / or vehicle availability; and h) implementing a path planning system to determine exact moves required the one or more automated pallets.

18. The method of claim 17, further comprising repeating steps g - h.

19. The method of claim 17, further comprising maintaining track of a sequence of commands and personnel instructions.

20. The method of claim 17, wherein collecting specified data includes orders of what items need to be taken in and / or released from the warehouse, where they are going to and from and when they will arrive or need to depart from the ERP, the time in which the vehicle carrying the arriving or departing items is scheduled to arrive or depart, respectively, real-time updates on those projects as the vehicle’s situation changes, and the metadata on the items relevant to how they will be stacked on an automated pallet with other items going to the same destination.

21. The method of claim 17, further comprising, if an assigned task requires the movement of the one or more automated pallets, invoking a goalframc generator configured to process a list of assigned tasks and create structured data describing a facility environment and proposed moves of the one or more automated pallets.

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