CN111144802A - Intelligent warehousing and delivery method integrating AGV and mechanical arm - Google Patents

Intelligent warehousing and delivery method integrating AGV and mechanical arm Download PDF

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Publication number
CN111144802A
CN111144802A CN201911223531.6A CN201911223531A CN111144802A CN 111144802 A CN111144802 A CN 111144802A CN 201911223531 A CN201911223531 A CN 201911223531A CN 111144802 A CN111144802 A CN 111144802A
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order
agv
mechanical arm
shelf
delivery
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CN111144802B (en
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孙小轩
周庆先
王慎娜
李强
薛城
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Jiangsu Suning Logistics Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32252—Scheduling production, machining, job shop
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/60—Electric or hybrid propulsion means for production processes

Abstract

The invention discloses an intelligent warehousing and delivery method integrating an AGV and a mechanical arm, which comprises the following steps: receiving a delivery task of a warehouse management system; the warehousing control system performs task combination and shelf inventory deployment; the AGV carries the goods shelf to an appointed mechanical arm operation area; the system receives the transport completion state of the AGV and calls a mechanical arm to select the commodities; the mechanical arm is selected and finished, and the system dispatches the AGV to carry the goods shelf to leave. In the process of warehousing and delivery, the invention realizes the full flow of automatic target commodity carrying by the AGV, automatic handover between the AGV and the mechanical arm and automatic sorting by the mechanical arm without manual participation in the whole process; greatly improving the delivery efficiency, reducing the personnel cost, and improving the delivery accuracy and the real-time performance.

Description

Intelligent warehousing and delivery method integrating AGV and mechanical arm
Technical Field
The invention belongs to the technical field of logistics storage, and particularly relates to an intelligent storage and delivery method integrating an AGV and a mechanical arm.
Background
The traditional storage delivery mode mainly comprises two modes of manual delivery operation and AGV storage delivery operation, wherein the manual delivery operation is that operators take delivery tasks and then associate containers, and then sort commodities into the containers; AGV storage and delivery operation: the method comprises the steps that an operator obtains a delivery task at a designated work station, the AGV carries a target goods shelf to a manual work station, and the operator selects goods according to system prompts.
The picking efficiency of manual delivery operation is low, a large amount of human resources need to be input, and the labor cost and the management cost are brought. And manual operation greatly improves the probability of operation errors and influences the delivery accuracy and instantaneity.
AGV warehousing delivery operation is a delivery strategy which is widely applied at present, the mode does not completely solve human participation in the delivery process, and the possibility of operation errors also exists. Since the actual picking process is still done manually, there is still an efficiency bottleneck that is limited by human.
Disclosure of Invention
The invention aims to provide an intelligent warehousing and delivery method integrating an AGV and a mechanical arm.
The technical scheme for realizing the purpose of the invention is as follows: an intelligent warehousing and delivery method integrating an AGV and a mechanical arm comprises the following steps:
receiving a delivery task of a warehouse management system;
the warehousing control system performs task combination and shelf inventory deployment;
the AGV carries the goods shelf to an appointed mechanical arm operation area;
and the warehousing control system receives the AGV carrying completion state and calls the mechanical arm to select the commodities.
Further, a step-by-step method is adopted to select the goods shelves and the stations: selecting a proper station for order tasks to ensure that the relevance between the order tasks of the same station is highest; and secondly, selecting a proper shelf for the order task, and selecting the shelf with the shortest distance to the station to meet the requirement of selecting more orders to select more commodities.
Further, the method for selecting the station for the order comprises the following steps:
the method comprises the following steps: WCS executes WMS batch task
An objective function:
max∑m∑n∑jYmnj*Factormn(1)
constraint conditions are as follows:
∑iXij≤Countj(2)
∑jXij＝1 (3)
2*Ymnj≤Xmj+Xnj(4)
Ymnj≥Xmj+Xnj-1 (5)
in the formula, Xij、YmnjIs a variable, XijFor indicating whether order i is at station j, YmnjThe order form n is used for indicating whether the order form m and the order form n are both in the station j or not;
Factormn、Countjis a constant, FactormnRepresents the correlation, Count, between order m and order njRepresenting the operable capacity of the station j;
the formula (1) is an objective function and represents that the relevance among orders of all the same stations is maximum;
equation (2) is a constraint that indicates that the order that each workstation can handle does not exceed its job-ability;
equation (3) is a constraint that indicates that each order is in and only works at one workstation;
equations (4) and (5) are constraint conditions, and whether two orders arrive at the same station or not is judged.
The second method comprises the following steps: WCS executes WMS order task
Objective function
maxXij*(flagOrder+flagSKU*SKUNumi+flagSeed*seedPrij)
(6)
Constraint conditions are as follows:
∑iXij*orderVolumei≤seedVolumej(7)
∑jXij≤1 (8)
Figure BDA0002301520980000021
Xija variable represents whether the order i is placed in the seeding position j;
flagOrder、flagSKU、flagSeed、SKUNumi、seedPrij、orderVolumei、seedVolumej、seedVolumej、PRIiis a constant; wherein
Flagarder, flagSKU, and flagSeed are weighting coefficients, SKUNumiSeedPri for the quantity of goods of order ijOrder volume for the priority of the seeding bit jiSeedvolume, volume of order ijVolume of seeding site j, PRIiIs the priority of order i;
the formula (6) is an objective function and represents that the weighted sum of the order quantity, the commodity quantity and the seeding bit priority is maximum;
formula (7) is a constraint condition, and represents that the sum of the volumes of all orders selecting the same sowing position is not greater than the volume of the sowing position;
formula (8) is a constraint condition, which indicates that each order is selected to be at most one sowing position;
equation (9) is a constraint condition and indicates order preference with high priority.
Further, the method for selecting the shelf for the order comprises the following steps:
optimization model
min∑iZi*(numFlag+distFlag*Disti) (10)
Constraint conditions are as follows:
∑iSik＝skuNumk(11)
Sik≤Stockik*Zi(12)
Sik≥Zi(13)
in the above formula, Sik、ZiIs a variable, SikShow from the shelfi number of sortable items k, ZiIndicating whether shelf i can be picked to hit;
numFlag、distFlag、Disti、skuNumk、Stockikthe numFlag and the distFlag are constants, are respectively a shelf number system and a shelf distance coefficient, and represent the priority weight of the two in the model; distiIndicating the distance from shelf i to the station, skuNumkRepresenting the quantity of picking demands, Stock, for item kikIndicating the stock available quantity of the commodity k on the shelf i;
the formula (10) is an objective function, and represents that the sum of the number of the shelves and the distance between the shelves is minimum;
equation (11) is a constraint condition, which represents that the sum of the picking amounts of the goods k from all the shelves is the order quantity;
equation (12) is a constraint condition, which indicates that the item k is not more than the stock available quantity of the item on the shelf;
equations (12) and (13) are constraints indicating that the pallet is hit by an order and that the pallet transport distance is taken into consideration.
Furthermore, before a delivery task is executed, AGV and mechanical arm map design is carried out, wherein the design comprises the placing position of the mechanical arm, the area division of the AGV and an AGV running route.
Before the delivery task is executed, commodity main data initialization is carried out, namely commodity main data information is received from the WMS through an interface module, and the commodity main data information comprises the length, the width, the height, bar code information and commodity description of a commodity.
Further, receiving delivery task data of the WMS through the interface module, wherein the delivery task data comprises a task order number and delivery details.
Further, an optimal task combination form and a shelf inventory deployment result are determined, an AGV scheduling task of the shelf is generated, and corresponding inventory locking is carried out according to the deployment result.
Further, a mechanical arm picking instruction corresponding to the goods location is generated according to the AGV carrying result; and the mechanical arm control system executes a picking action according to a picking instruction of the WCS and feeds a picking result back to the WCS, and the WCS records the picking result and performs corresponding inventory deduction.
Furthermore, the mechanical arm is selected to be completed, and the system dispatches the AGV to carry the goods shelf to leave.
Compared with the prior art, the invention has the following remarkable advantages: in the process of warehousing and delivery, manual participation is not needed in the whole process, automatic target goods handling of the AGV, automatic connection of the AGV and the mechanical arm and automatic sorting of the mechanical arm are realized, the delivery efficiency is greatly improved, the personnel cost is reduced, the delivery accuracy and the real-time performance are improved, and the operation time is prolonged from 18 hours in two shifts to 24 hours of unmanned operation; the delivery efficiency can reach 600 pieces per hour at most, and is improved by 3-4 times; the delivery accuracy rate can reach 99.99%.
Drawings
FIG. 1 is a flowchart of a smart warehousing delivery method incorporating an AGV and a robotic arm according to the present invention.
FIG. 2 is a schematic block diagram of an intelligent warehousing and delivery method integrating an AGV and a robotic arm according to the present invention.
Detailed Description
As shown in fig. 1 and 2, the present invention provides an intelligent warehousing and delivery method integrating an AGV and a robot arm, including:
receiving a delivery task of a warehouse management system WMS;
the warehousing control system WCS performs task combination and shelf inventory deployment;
the AGV carries the goods shelf to an appointed mechanical arm operation area;
the system receives the transport completion state of the AGV and calls a mechanical arm to select the commodities;
the mechanical arm is selected and finished, and the system dispatches the AGV to carry the goods shelf to leave.
The delivery deployment strategy of the method is as follows: and deploying the order task to the appropriate shelf and station for the goods-to-person/goods-to-robot operation scene to process. The relation of factors such as orders, commodities, goods shelves, stations and the like is considered, and the processing mode and method thereof have direct influence on the comprehensive delivery efficiency; under the background of global optimization, the order quantity index is increased and the order line is complicated, and long-time operation does not meet the actual business requirement. Therefore, the goods shelves and the stations are selected by a step-by-step method; selecting a proper station for order tasks to ensure that the relevance between the order tasks of the same station is highest; and secondly, selecting a proper shelf for the order task, and selecting the shelf with the shortest distance to the station to meet the requirement of selecting more orders to select more commodities. The first step/the second step, the weight proportion is called according to the strategy to execute, for example, in the first step, the strategy is [ most tasks, most commodities, priority of high seeding bit is used preferentially ], the values of flagOrder, flagSKU and flagSeed are 0.6, 0.3 and 0.1 respectively; in the second step, the strategy is [ lowest cost to transport, lowest number of shelves ], distFlag and numFlag are 0.7 and 0.3, respectively.
The specific method comprises the following steps:
the first step is as follows: selecting stations for orders
Strategy one: WCS executes WMS batch task
Variables of
Xij: whether order i is on station j
Ymnj: whether order m and order n are both at station j
Constant quantity
Factormn: correlation between order m and order n
Countj: workability of station j
Optimizing the model:
max∑m∑n∑jYmnj*Factormn(1)
constraint conditions are as follows:
∑iXij≤Countj(2)
∑jXij＝1 (3)
2*Ymnj≤Xmj+Xnj(4)
Ymnj≥Xmj+Xnj-1 (5)
the formula (1) is an objective function and represents that the relevance among orders of all the same stations is maximum;
equation (2) is a constraint that indicates that the order that each workstation can handle does not exceed its job-ability;
equation (3) is a constraint that indicates that each order is in and only works at one workstation;
the formulas (4) and (5) are constraint conditions, and whether two orders arrive at the same station or not is judged;
and (2) strategy two: WCS executes WMS order task
Variables of
Xij: whether order i is put into seeding position j
Constant quantity
flagOrder, flagSKU and flagSeed are weighting coefficients;
SKUNumi: the commodity number of the order i;
seedPrij: the priority of the seeding bit j;
orderVolumei: the volume of order i;
seedVolumej: the volume of the seeding site j;
PRIi: priority of order i.
Optimizing the model:
maxXij*(flagOrder+flagSKU*SKUNumi+flagSeed*seedPrij) (6)
constraint conditions are as follows:
∑iXij*orderVolumei≤seedVolumej(7)
∑jXij≤1 (8)
Figure BDA0002301520980000061
the formula (6) is an objective function and represents that the weighted sum of the order quantity, the commodity quantity and the seeding bit priority is maximum;
formula (7) is a constraint condition, and represents that the sum of the volumes of all orders selecting the same sowing position is not greater than the volume of the sowing position;
formula (8) is a constraint condition, which indicates that each order is selected to be at most one sowing position;
equation (9) is a constraint condition and indicates order preference with high priority.
The second step is that: selecting shelves for orders
Variables of
Sik: number of items k that can be picked from shelf i
Zi: whether shelf i can be picked to hit
Constant quantity
numFlag and distFlag are a shelf number system and a shelf distance coefficient respectively, and represent the priority weights of the two in the model
Disti: distance from shelf i to station
skuNumk: number of picking demand of item k
Stockik: stock available quantity of goods k on shelf i
Optimization model
min∑iZi*(numFlag+distFlag*Disti) (10)
Constraint conditions
∑iSik＝skuNumk(11)
Sik≤Stockik*Zi(12)
Sik≥Zi(13)
The formula (10) is an objective function, and represents that the sum of the number of the shelves and the distance between the shelves is minimum;
equation (11) is a constraint condition, which represents that the sum of the picking amounts of the goods k from all the shelves is the order quantity;
equation (12) is a constraint condition, which indicates that the item k is not more than the stock available quantity of the item on the shelf;
equations (12) and (13) are constraints indicating that the pallet is hit by an order and that the pallet transport distance is taken into consideration.
In the process of warehousing and delivery, manual participation is not needed in the whole process, automatic target goods carrying by the AGV, automatic handover between the AGV and the mechanical arm and automatic sorting by the mechanical arm are realized, the delivery efficiency is greatly improved, the personnel cost is reduced, and the delivery accuracy and the delivery real-time performance are improved.
The invention integrates two kinds of automatic equipment, namely the AGV and the mechanical arm, so as to realize unmanned storage and delivery. The system adopts more accurate system level division, a large bin management mode of a black box is adopted for the AGV and the mechanical arm area WMS, and the WCS adopts inventory management from detail to each bin. Therefore, during task selection and combination, the WCS can calculate the optimized combination of tasks and shelves through an algorithm, realize that more delivery tasks are matched under the condition of the lowest carrying cost, observe the rules of priority delivery of high-priority tasks, worst effective period first-out and the like, and finish the operation before the list cutting time required by the WMS. Efficient delivery operation of the warehouse is sequentially realized, and the complexity and the overstaffed of the WMS system are avoided. The WCS is used as a control layer to uniformly schedule two kinds of automatic equipment, namely an AGV and a mechanical arm. And an AGV carrying arrival instruction is sent to the WCS, and the WCS judges the picking targets and the picking quantity and sends the picking instruction to the mechanical arm. The two devices operate independently without mutual influence and can be seamlessly butted in the service process.
The present invention will be further described with reference to the following examples.
Examples
Data preparation
(1) Map design
AGV, arm map design, including the locating place of arm, AGV's regional division, AGV operation route, wherein AGV's regional division includes transportation area and storage area.
(2) On-site master data collection and initialization
The on-site main data comprises map main data, shelf main data, mechanical arm related main data, cargo space main data and container main data.
(3) Commodity master data initialization
And receiving commodity main data information from the WMS through the interface module, wherein the commodity main data information comprises the length, width, height and bar code information of the commodity and commodity description.
(II) task execution
(1) Task reception
And receiving delivery task data of the WMS through an interface module, wherein the delivery task data comprises a task order number and delivery details.
(2) Algorithm module selection task and shelf deployment
And calculating an optimal task combination form and a shelf inventory deployment result through an algorithm to generate an AGV dispatching task of the shelf. And performing corresponding inventory locking according to the deployment result of the algorithm.
(3) AGV performs transport
And the AGV control system executes the goods shelf transportation according to the scheduling task of the WCS and feeds back the transportation result to the WCS.
(4) Mechanical arm picking
And generating a mechanical arm picking instruction corresponding to the goods location according to the AGV carrying result. And the mechanical arm control system executes the picking action according to the picking instruction of the WCS and feeds the picking result back to the WCS. The WCS records the picking results and makes corresponding inventory deductions.
(5) Feedback of sorting results
And feeding back to the WMS according to the actual sorting result.
(6) Dispatching goods shelf warehouse
And calculating the position of the goods shelf returning to the warehouse, and generating an instruction of the AGV for carrying the goods shelf returning to the warehouse.
In the invention, WCS refers to a storage control system, and WMS refers to a storage management system.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims (10)

1. An intelligent warehousing and delivery method integrating an AGV and a mechanical arm is characterized by comprising the following steps:
receiving a delivery task of a warehouse management system;
the warehousing control system performs task combination and shelf inventory deployment;
the AGV carries the goods shelf to an appointed mechanical arm operation area;
and the warehousing control system receives the AGV carrying completion state and calls the mechanical arm to select the commodities.
2. The intelligent warehousing and delivery method integrating the AGV and the mechanical arm according to claim 1, wherein the shelves and the stations are selected in a step-by-step manner: selecting a proper station for order tasks to ensure that the relevance between the order tasks of the same station is highest; and secondly, selecting a proper shelf for the order task, and selecting the shelf with the shortest distance to the station to meet the requirement of selecting more orders to select more commodities.
3. The intelligent warehousing and delivery method integrating the AGV and the mechanical arm according to claim 2, wherein the method for selecting the stations for the order comprises the following steps:
WCS executes WMS batch task
An objective function:
max ∑m∑n∑jYmnj*Factormn(1)
constraint conditions are as follows:
∑iXij≤Countj(2)
∑jXij＝1 (3)
2*Ymnj≤Xmj+Xnj(4)
Ymnj≥Xmj+Xnj-1 (5)
in the formula, Xij、YmnjIs a variable from 0 to 1, XijFor indicating whether order i is at station j, YmnjThe order form n is used for indicating whether the order form m and the order form n are both in the station j or not; xmjWhether the orders m are all at the working positions j, X or not is shownnjIndicating whether the orders n are all at the station j;
Factormn、Countjis a constant, FactormnRepresents the correlation, Count, between order m and order njRepresenting the operable capacity of the station j;
the formula (1) is an objective function and represents that the relevance among orders of all the same stations is maximum;
equation (2) is a constraint that indicates that the order that each workstation can handle does not exceed its job-ability;
equation (3) is a constraint that indicates that each order is in and only works at one workstation;
the formulas (4) and (5) are constraint conditions, and whether two orders arrive at the same station or not is judged;
WCS executes WMS order task
Objective function
max Xij*(flagOrder+flagSKU*SKUNumi+flagSeed*seedPrij)
(6)
Constraint conditions are as follows:
∑iXij*orderVolumei≤seedVolumej(7)
∑jXij≤1 (8)
Figure FDA0002301520970000021
Xija variable represents whether the order i is placed in the seeding position j;
flagOrder、flagSKU、flaagSeed、SKUNumi、seedPrij、orderVolumei、seedVolumej、seedVolumej、PRIiis a constant; wherein
Flagarder, flagSKU, and flagSeed are weighting coefficients, SKUNumiSeedPri for the quantity of goods of order ijOrder volume for the priority of the seeding bit jiSeedvolume, volume of order ijVolume of seeding site j, PRIiIs the priority of order i;
the formula (6) is an objective function and represents that the weighted sum of the order quantity, the commodity quantity and the seeding bit priority is maximum;
formula (7) is a constraint condition, and represents that the sum of the volumes of all orders selecting the same sowing position is not greater than the volume of the sowing position;
formula (8) is a constraint condition, which indicates that each order is selected to be at most one sowing position;
equation (9) is a constraint condition and indicates order preference with high priority.
4. The intelligent warehousing and delivery method integrating the AGV and the mechanical arm according to claim 2, wherein the method for selecting the shelf for the order is as follows:
optimization model
min ∑iZi*(numFlag+distFlag*Disti) (10)
Constraint conditions are as follows:
∑iSik＝skuNumk(11)
Sik≤Stockik*Zi(12)
Sik≥Zi(13)
in the above formula, Sik、ZiIs a variable, SikIndicating the number of items k, Z, that can be picked from shelf iiA variable of 0-1, indicating whether shelf i can be picked to hit;
numFlag、distFlag、Disti、skuNumk、Stockikthe numFlag and the distFlag are constants, are respectively a shelf number system and a shelf distance coefficient, and represent the priority weight of the two in the model; distiIndicating the distance from shelf i to the station, skuNumkRepresenting the quantity of picking demands, Stock, for item kikIndicating the stock available quantity of the commodity k on the shelf i;
the formula (10) is an objective function, and represents that the sum of the number of the shelves and the distance between the shelves is minimum;
equation (11) is a constraint condition, which represents that the sum of the picking amounts of the goods k from all the shelves is the order quantity;
equation (12) is a constraint condition, which indicates that the item k is not more than the stock available quantity of the item on the shelf;
equations (12) and (13) are constraints indicating that the pallet is hit by an order and that the pallet transport distance is taken into consideration.
5. The intelligent warehousing delivery method integrating the AGVs and the mechanical arms as claimed in claim 1, wherein before a delivery task is executed, the AGVs and the mechanical arm map design is carried out, wherein the design comprises the placement position of the mechanical arm, the area division of the AGVs and the running route of the AGVs.
6. The intelligent warehousing and delivery method integrating the AGV and the mechanical arm according to claim 5, wherein before a delivery task is executed, main data initialization of the commodities is performed, that is, main data information of the commodities is received from a warehousing management system through an interface module, and the main data information of the commodities comprises length, width, height, barcode information and description of the commodities.
7. The intelligent warehousing delivery method integrating the AGV and the mechanical arm according to claim 1, wherein the delivery task data of the warehousing management system, including a task order number and delivery details, are received through the interface module.
8. The intelligent warehousing and delivery method integrating the AGVs and the mechanical arms as claimed in claim 1, wherein an optimal task combination form and a shelf inventory deployment result are determined, AGV scheduling tasks of a shelf are generated, and corresponding inventory locking is performed according to the deployment result.
9. The intelligent warehousing and delivery method integrating the AGV and the mechanical arm according to claim 8, wherein a mechanical arm picking instruction corresponding to a goods location is generated according to the AGV carrying result; the mechanical arm control system executes the picking action according to the picking instruction of the storage control system and feeds the picking result back to the storage control system, and the storage control system records the picking result and performs corresponding inventory deduction.
10. The intelligent warehousing and delivery method integrating the AGV and the mechanical arm according to claim 1, wherein the mechanical arm is selected completely, and the system schedules the AGV to carry the goods shelf to leave.
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CN115009737A (en) * 2022-05-31 2022-09-06 江苏木盟智能科技有限公司 Multi-scene intelligent carrying method and system, storage medium and intelligent carrying robot
CN115187168A (en) * 2022-07-12 2022-10-14 天工爱和特钢有限公司 Logistics circulation system and method for powder metallurgy factory

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