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US-12626223-B2 - Method and system for allocating an AGV vehicle in a plant location

US12626223B2US 12626223 B2US12626223 B2US 12626223B2US-12626223-B2

Abstract

Systems and a method control and allocate an automated guided vehicle (AGV) at a plant location of a plant. An order generator is enabled to be configured with an anticipating logical rule whose conditional statement includes a set of anticipating plant status conditions whose fulfillment causes the order generator to send to a fleet manager a corresponding AGV dummy transport order. The AGV dummy transport order requests an allocation of an AGV vehicle, in a busy state, to a plant location until a set of corresponding anticipated conditions are satisfied or until a predefined timer expires.

Inventors

  • Moshe Hazan

Assignees

  • SIEMENS INDUSTRY SOFTWARE LTD.

Dates

Publication Date
20260512
Application Date
20210429

Claims (20)

  1. 1 . A method for allocating, by a data processing system, an automated guided vehicle (AGV) disposed to a location of a plant, wherein a fleet of AGVs is managed by a fleet manager (FM), wherein the fleet manager is configured to receive a transport order from an order generator (OG) and is configured to send a transport request to a selected AGV, wherein the order generator is configured to send the transport order to the fleet manager upon satisfaction of conditional statements of logical rules, wherein the conditional statements of logical rules include sets of plant status conditions, the method comprises the steps of: enabling the order generator to be configured with an anticipating logical rule whose conditional statement includes a set of anticipating plant status conditions whose fulfillment causes the order generator to send to the fleet manager a corresponding AGV dummy transport order, wherein the corresponding AGV dummy transport order requests an allocation of the AGV vehicle, in a busy state, to a plant location until a set of corresponding anticipated conditions are satisfied or until a predefined timer expires; sending to the fleet manager the corresponding AGV dummy transport order via the order generator, upon fulfillment of a specific set of the anticipating plant status conditions; requesting, via the fleet manager, an allocation of the selected AGV vehicle to a specific plant location; sending, by the fleet manager, a transport task request to the selected AGV vehicle to send the selected AGV vehicle to the specific plant location; and keeping the selected AGV vehicle in a waiting mode at the specific plant location and marking the selected AGV vehicle as being in the busy state until the set of corresponding anticipated conditions are satisfied or until the predefined specific timer expires; wherein in the waiting mode, the selected AGV vehicle does not perform a transport task.
  2. 2 . The method according to claim 1 , wherein the set of anticipating plant status conditions are determined by recognizing a pattern of plant status conditions anticipating an occurrence of a pattern of corresponding anticipated conditions on a data set obtained as outcome of an execution of a virtual simulation of plant operations.
  3. 3 . The method according to claim 1 , wherein an anticipated condition is an AGV transport order requesting the AGV vehicle in a specific working area which can be covered by the AGV vehicle allocated in a dummy task at the specific plant location.
  4. 4 . The method according to claim 3 , wherein the dummy task in a given location may be used to cover multiple anticipated conditions linked to arrivals of a set of AGV orders in a set of multiple locations coverable from the given location.
  5. 5 . The method according to claim 2 , wherein the data set of the virtual simulation is selected from the group consisting of: transport orders sent by the OG to each of the FMs to each of the AGVs; signals and sensor states before the transport orders are sent; robotic tasks preceding the transport orders and their corresponding duration times; a number of parts moved on each conveyor and their types; a number of robotic operations, their types and corresponding robots; employed times of each said AGV vehicle for reaching its target position; status situations in precedent and subsequent workstations; human tasks; and other relevant plant statuses.
  6. 6 . The method according to claim 1 , wherein the set of anticipating plant status conditions are selected from the group consisting of: a set of conditions on plant signals; a set of conditions on plant sensors; a set of conditions on plant statuses; a set of conditions on statuses of any plant equipment or of any plant object or of any human; and a set of conditions combining any of the above.
  7. 7 . The method according to claim 1 , wherein, during plant operations, the order generator receives a new anticipating logical rule.
  8. 8 . A data processing system, comprising: a processor; a memory connected to said processor; the data processing system configured to: enable an order generator to be configured with an anticipating logical rule whose conditional statement includes a set of anticipating plant status conditions whose fulfillment causes the order generator to send to a fleet manager a corresponding automated guided vehicle (AGV) dummy transport order, wherein the corresponding AGV dummy transport order requests an allocation of an AGV, in a busy state, to a plant location until a set of corresponding anticipated conditions are satisfied or until a predefined timer expires; enable the order generator to send, upon fulfillment of a specific set of anticipating plant status conditions, to the fleet manager a corresponding specific AGV dummy transport order; enable the fleet manager to request an allocation of a selected AGV vehicle to a specific plant location; enable the fleet manager to send a transport task request to the selected AGV vehicle to send the selected AGV vehicle to the specific plant location; and enable the selected AGV vehicle to remain in a waiting mode at the specific plant location and mark the selected AGV vehicle as being in the busy state until the set of corresponding anticipated conditions are satisfied or until the predefined specific timer expires; wherein in the waiting mode, the selected AGV vehicle does not perform a transport task.
  9. 9 . The data processing system according to claim 8 , wherein the specific set of anticipating plant status conditions are determined by recognizing a pattern of plant status conditions anticipating an occurrence of a pattern of corresponding anticipated conditions on a data set obtained as outcome of an execution of a virtual simulation of plant operations.
  10. 10 . The data processing system according to claim 8 , wherein an anticipated condition is an AGV transport order requesting the AGV vehicle in a specific working area which can be covered by the AGV vehicle allocated in a dummy task at the specific plant location.
  11. 11 . The data processing system according to claim 10 , wherein the dummy task in a given location may be used to cover multiple anticipated conditions linked to arrivals of a set of AGV orders in a set of multiple locations coverable from the given location.
  12. 12 . The data processing system according to claim 9 , wherein the data set of the virtual simulation is selected from the group consisting of: transport orders sent by the OG to fleet manages for each of the AGVs; signals and sensor states before the transport orders are sent; robotic tasks preceding the transport orders and their corresponding duration times; number of parts moved on each conveyor and their types; a number of robotic operations, their types and corresponding robots; employed times of each of the AGV for reaching its target position; status situations in precedent and subsequent workstations; human tasks; and other relevant plant statuses.
  13. 13 . The data processing system according to claim 8 , wherein the specific set of anticipating plant status conditions are selected from the group consisting of: a set of conditions on plant signals; a set of conditions on plant sensors; a set of conditions on plant statuses; a set of conditions on statuses of any plant equipment or of any plant object or of any human; and a set of conditions combining any of the above.
  14. 14 . A non-transitory computer-readable medium encoded with executable instructions that, when executed, cause at least one data processing system to: enable an order generator (OG) to be configured with an anticipating logical rule whose conditional statement contains a set of anticipating plant status conditions and whose fulfillment causes the order generator to send to a fleet manager (FM) a corresponding automated guided vehicle (AGV) dummy transport order, wherein the AGV dummy transport order requests an allocation of an AGV vehicle, in a busy state, at a plant location until a set of corresponding anticipated conditions are satisfied or until a predefined timer expires; enable the order generator to send, upon fulfillment of a specific set of anticipating plant status conditions, to the fleet manager a corresponding specific AGV dummy transport order; enable the fleet manager to request an allocation of a selected AGV vehicle to a specific plant location; enable the fleet manager to send a transport task request to the selected AGV vehicle to send the selected AGV vehicle to the specific plant location; and enable the selected AGV vehicle to remain in a waiting mode at the specific plant location and mark the selected AGV vehicle as being in the busy state until the set of corresponding anticipated conditions are satisfied or until the predefined specific timer expires; wherein in the waiting mode, the selected AGV vehicle does not perform a transport task.
  15. 15 . The non-transitory computer-readable medium according to claim 14 , wherein the specific set of anticipating plant status conditions are determined by recognizing a pattern of plant status conditions anticipating an occurrence of a pattern of corresponding anticipated conditions on a data set obtained as outcome of an execution of a virtual simulation of plant operations.
  16. 16 . The non-transitory computer-readable medium according to claim 14 , wherein an anticipated condition is an AGV transport order requesting the AGV vehicle to a specific working area which can be covered by the AGV vehicle allocated in a dummy task at the specific plant location.
  17. 17 . The non-transitory computer-readable medium according to claim 16 , wherein the dummy task in a given location may be used to cover multiple anticipated conditions linked to arrivals of a set of AGV transport orders in a set of multiple locations coverable from the given location.
  18. 18 . The non-transitory computer-readable medium according to claim 15 , wherein the data set of the virtual simulation is selected from the group consisting of: the transport orders sent by the order generator to each of the fleet managers to each of the AGV; signals and sensor states before the transport orders are sent; robotic tasks preceding the transport orders and their corresponding duration times; number of parts moved on each conveyor and their types; number of robotic operations, their types and corresponding robots; employed times of each of the AGVs for reaching its target position; status situations in precedent and subsequent workstations; human tasks; and other relevant plant statuses.
  19. 19 . The non-transitory computer-readable medium according to claim 14 , wherein the specific set of anticipating plant status conditions are selected from the group consisting of: a set of conditions on plant signals; a set of conditions on plant sensors; a set of conditions on plant statuses; a set of conditions on statuses of any plant equipment or of any plant object or of any human; and a set of conditions combining any of the above.
  20. 20 . The non-transitory computer-readable medium according to claim 14 , wherein, during plant operations, the order generator is enabled to receive a new anticipating logical rule.

Description

FIELD AND BACKGROUND OF THE INVENTION The present disclosure is directed, in general, to computer-aided design, visualization, and manufacturing (“CAD”) systems, product lifecycle management (“PLM”) systems, product data management (“PDM”) systems, production environment simulation, and similar systems, that manage data for products and other items (collectively, “Product Data Management” systems or PDM systems). More specifically, the disclosure is directed to systems related to automated guided vehicles (“AGV”) systems in the field of industrial automation. In field of industrial automation, AGV systems take an increasingly important role in the logistics of manufacturing plants. In a typical AGV system, the AGV fleet manager (“FM”) is responsible for ensuring a smooth routing of the AGV vehicle fleet on the plant carpet by determining which selected AGV vehicle is to be dispatched in a desired location of a plant working area in order to execute specific transport requests. FIG. 2 schematically illustrates, for explanatory purposes, a simplified block diagram of modules for managing the movements of AGV vehicles and the movements of industrial robots in a real plant floor. The fleet manager 201 receives AGV transport orders by the order generator (“OG”) 202, such transport orders are typically based on the fulfillments of logical rules 212 on plant status conditions received via plant signals 204; whereby the plant signals include also signals from sensors. Such logical rules 212 typically determine when/which transport orders are to be sent to the FM module 202. Based on the received transport orders, the fleet manager 201 typically sends corresponding transport task requests to the selected AGV vehicles 221 by managing the dispatching of its AGV fleet via routing algorithms by taking into account signals and statuses received from the AGV vehicles 221 and by taking into account signals (such connection is in FIG. 2 not shown) received from other plant entities including the plants signals 204. Those skilled in the art will recognize that the block diagram of FIG. 2 is a very simplified explanatory schema of the functioning of industrial AGV systems and it does not include the full schema structure of possible modules, devices and/or of possible respective interactions. For example, a connection between the plant signals 204 and the fleet manager 202 is in FIG. 2 not shown. For example, device servers are in FIG. 2 not shown, examples of device servers include, but are not limited by, a Warehouse Control System (“WCS”) or other systems such as Supervisory Control and Data Acquisition (“SCADA”), Distributed Control System (“DCS”) for controlling various plant mechanisms. Examples of OG modules 202 include, but are not limited by, a Warehouse Management System, a Manufacturing Execution System or any other module capable of generating AGV transportation orders for the fleet manager 201. Thus, as schematically illustrated in FIG. 2, the movements of the AGV vehicles 221 on the plant floor are managed by the fleet manager 201 which is dispatching transport tasks upon reception of OG transport orders. The movements of the robots 225 are managed by the Programmable Logic Controller (“PLC”) module 205 which decides when/which robotic programs are to be executed for each robot. For example, once a given robot 225 gets from the PLC controller 205 a green light to perform a predefined program, the given robot deterministically performs its predefined program while exchanging signals with the PLC controller 205, e.g. in order to get permissions before entering into a risky zone during the program execution. During plant operations, the AGV vehicles interact during their transport tasks with a large variety of factory entities located on the plant shop floor. Examples of factory entities include, but are not limited by, mechanisms positioned on the AGV vehicle motion path (e.g. automatic doors, elevators, traffic lights, etc) and mechanisms related to the AGV vehicle tasks (e.g. conveyors, robots, fixtures, etc). Examples of static factory entities interacting with the AGV vehicle include, but are not limited by walls, fences, crates, etc. Examples of dynamic factory entities interacting with the AGV system include, but are not limited by, human workers, man-controlled forklifts and other predictable and unpredictable moving objects etc. In manufacturing facilities, the AGV deployment engineers are assigned the challenging engineering task of deploying AGV systems by adjusting their interactions with the factory entities in order to optimize the routing algorithm of the AGV fleet manager at the plant floor. Nowadays, a Virtual commissioning (“VC”) software application may be employed to achieve a realistic AGV simulation for assisting the AGV deployment engineers in verifying and validating the interactions between the AGV system and the factory entities. PCT/IB2020/057254 teaches an advantageous technique to achie