CN108398924B - Scheduling method and scheduling device for robot transport vehicle - Google Patents
Scheduling method and scheduling device for robot transport vehicle Download PDFInfo
- Publication number
- CN108398924B CN108398924B CN201710068223.5A CN201710068223A CN108398924B CN 108398924 B CN108398924 B CN 108398924B CN 201710068223 A CN201710068223 A CN 201710068223A CN 108398924 B CN108398924 B CN 108398924B
- Authority
- CN
- China
- Prior art keywords
- area
- robotic
- idle
- robot
- scheduling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 64
- 239000000969 carrier Substances 0.000 claims description 73
- 238000009826 distribution Methods 0.000 claims description 19
- 235000020004 porter Nutrition 0.000 claims description 19
- 102000012498 secondary active transmembrane transporter activity proteins Human genes 0.000 claims description 18
- 108040003878 secondary active transmembrane transporter activity proteins Proteins 0.000 claims description 18
- 238000003860 storage Methods 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 abstract description 10
- 230000002776 aggregation Effects 0.000 description 14
- 238000004220 aggregation Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 14
- 238000004590 computer program Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000000638 solvent extraction Methods 0.000 description 5
- 230000003068 static effect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000002609 medium Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000006163 transport media Substances 0.000 description 1
Images
Classifications
-
- 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] or computer integrated manufacturing [CIM]
- G05B19/4189—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] or computer integrated manufacturing [CIM] characterised by the transport system
- G05B19/41895—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] or computer integrated manufacturing [CIM] characterised by the transport system using automatic guided vehicles [AGV]
-
- 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
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
Abstract
The invention discloses a scheduling method and a scheduling device of a robot transport vehicle, and relates to the technical field of automatic warehousing. The scheduling method comprises the following steps: dividing the warehouse picking area into a plurality of areas; determining an area where the number of the current robotic handling vehicles is less than the minimum configuration number of the robotic handling vehicles, and determining a target area of an idle robotic handling vehicle according to the determined area; an idle robotic vehicle is scheduled to a target area. Through the scheme, the idle robot carrying vehicles can be preset to the areas which are possibly needed, for example, the areas with the current number of the robot carrying vehicles lower than the minimum configuration number, the areas with dense goods carrying tools to be delivered from a warehouse, the areas with dense historical goods carrying tools to be delivered from the warehouse and the like, so that the response time of the idle robot carrying vehicles to the goods picking task is shortened, and the working efficiency of the robot carrying vehicles is improved.
Description
Technical Field
The invention relates to the technical field of automatic warehousing, in particular to a scheduling method and a scheduling device of a robot transport vehicle.
Background
With the development of the automatic warehouse technology, the robot carrying vehicle is widely applied to the warehouse goods carrying work, and the human resources are greatly saved.
The robotic cart may be idle when there are no picking tasks or when the workstations are all occupied. Due to the uncertainty of the next picking task in time and space, how to schedule the robot carrier after being idle can make the robot carrier respond to the next picking task more quickly, thereby improving the working efficiency of the robot carrier.
Disclosure of Invention
The invention needs to solve a technical problem that: the scheduling problem of idle robot carrier to improve robot carrier's work efficiency.
In a first aspect of the present invention, a method for scheduling a robotic cart includes: dividing the warehouse picking area into a plurality of areas; determining an area where the number of the current robotic handling vehicles is less than the minimum configuration number of the robotic handling vehicles, and determining a target area of an idle robotic handling vehicle according to the determined area; an idle robotic vehicle is scheduled to a target area.
The minimum arrangement quantity of the robot carriers in each area is determined according to the distribution proportion of the robot carriers in the area and the total investment of the robot carriers.
And determining the area with the largest allocation proportion of the robot carriers in the area where the number of the current robot carriers is less than the minimum allocation number of the robot carriers as the target area of the idle robot carriers.
The allocation proportion of the robot transport vehicles in each area is determined according to the ratio of the number of the goods transport tools to be delivered in the area to the total number of the goods transport tools to be delivered in all the areas; alternatively, the allocation ratio for each of the zones of robotic vehicles may be determined based on a ratio of the historical frequency of the shipment in that zone to the historical total frequency of the shipment in all of the zones.
Preferably, dividing the warehouse pick zone into zones comprises: clustering the goods handling tools to be delivered according to the position distribution information of the goods handling tools to be delivered, and taking a goods handling tool gathering area to be delivered corresponding to each cluster as an area; or clustering the historical ex-warehouse goods carrying tools according to the position distribution information and the ex-warehouse frequency information of the historical ex-warehouse goods carrying tools, and taking a historical ex-warehouse goods carrying tool gathering area corresponding to each cluster as an area.
Preferably, the scheduling method of the robotic cart further includes: judging whether the number of the current robot carriers in the area where the idle robot carriers are located is larger than the minimum configuration number of the robot carriers in the area; if the judgment result is yes, determining a target area of the idle robot carrier according to the determined area; and if the judgment result is negative, determining the area where the idle robot transport vehicle is located as the target area of the idle robot transport vehicle.
Preferably, the scheduling method of the robotic cart further includes: monitoring whether the electric quantity of the idle robot carrying vehicle reaches a charging threshold value, if the electric quantity of the idle robot carrying vehicle is lower than or equal to the charging threshold value, scheduling the idle robot carrying vehicle to charge the electric pile, and if the electric quantity of the idle robot carrying vehicle is higher than the charging threshold value, executing a target area determining step and a step of scheduling the idle robot carrying vehicle to the target area.
Preferably, the scheduling method of the robotic cart further includes: selecting a position of the carrier near the center position in the target area as a target position; and scheduling the idle robotic vehicle to a target location of the target area.
Preferably, the scheduling method of the robotic cart further includes: when a new picking task exists, the idle robot carriers which reach the area where the picking task is located are dispatched to execute the picking task, or the idle robot carriers closest to the picking task are dispatched from the idle robot carriers going to the target area to execute the picking task.
In a second aspect of the present invention, there is provided a scheduling apparatus for a robotic handling vehicle, comprising: the area dividing module is used for dividing the warehouse picking area into a plurality of areas; the system comprises a target area determining module, a judging module and a control module, wherein the target area determining module is used for determining an area where the number of the current robotic handling vehicles is less than the minimum configuration number of the robotic handling vehicles and determining a target area of an idle robotic handling vehicle according to the determined area; and the scheduling module is used for scheduling the idle robot truck to the target area.
Preferably, the scheduling apparatus of the robotic cart further includes: and the allocation quantity determining module is used for determining the minimum allocation quantity of the robotic porters in each area according to the allocation proportion of the robotic porters in each area and the investment total amount of the robotic porters.
The target area determining module is used for determining an area with the largest distribution proportion of the robot carriers in the area where the number of the current robot carriers is less than the minimum configuration number of the robot carriers as a target area of the idle robot carriers.
Preferably, the scheduling apparatus of the robotic cart further includes: the distribution proportion determining module is used for determining the distribution proportion of the robot transport vehicles in each area according to the ratio of the number of the goods transport tools to be delivered out of the area to the total number of the goods transport tools to be delivered out of the area; or, for determining the allocation ratio of robotic vehicles for each zone based on the ratio of the historical frequency of shipment of the goods handling tool in that zone to the historical total frequency of shipment of the goods handling tool in all zones.
Preferably, the region dividing module is used for clustering the goods handling tools to be delivered according to the position distribution information of the goods handling tools to be delivered, and taking the goods handling tool gathering region to be delivered corresponding to each cluster as a region; or clustering the historical ex-warehouse goods carrying tools according to the position distribution information and the ex-warehouse frequency information of the historical ex-warehouse goods carrying tools, and taking a historical ex-warehouse goods carrying tool gathering area corresponding to each cluster as an area.
Preferably, the scheduling apparatus of the robotic cart further includes: at least one module of the judging module and the electric quantity detecting module;
the judging module is used for judging whether the number of the current robot carriers in the area where the idle robot carriers are located is larger than the minimum configuration number of the robot carriers in the area; and a target area determination module for performing a step of determining a target area of the idle robot truck according to the determined area, if the determination result is yes; determining the area where the idle robot carrying vehicle is located as a target area of the idle robot carrying vehicle under the condition that the judgment result is negative;
the electric quantity detection module is used for monitoring whether the electric quantity of the idle robot transport vehicle reaches a charging threshold value; and the scheduling module is used for scheduling the idle robot carrier to charge the electric pile under the condition that the electric quantity of the idle robot carrier is lower than or equal to the charging threshold value, and scheduling the idle robot carrier to the target area under the condition that the electric quantity of the idle robot carrier is higher than the charging threshold value.
Preferably, the scheduling module is further configured to: selecting a carrier position near the center position in the target area as a target position, and scheduling the idle robotic carrier to the target position of the target area; or when a new picking task exists, scheduling the idle robot carrier reaching the area where the picking task is located to execute the picking task, or scheduling the idle robot carrier closest to the picking task from the idle robot carriers going to the target area to execute the picking task.
In a third aspect of the present invention, there is provided a scheduling apparatus for a robotic handling vehicle, comprising: a memory; and a processor coupled to the memory, the processor configured to execute the aforementioned method of scheduling a robotic cart based on instructions stored in the memory.
In a fourth aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing the computer to execute the aforementioned scheduling method of a robotic truck.
According to the scheme, the idle robot carrying vehicles can be adjusted to the areas which are possibly needed in advance, for example, the areas with the current number of the robot carrying vehicles lower than the minimum configuration number, the areas with intensive goods carrying tools to be delivered from a warehouse, the areas with intensive historical goods carrying tools to be delivered from the warehouse and the like, so that the response time of the idle robot carrying vehicles to the goods picking task is shortened, and the working efficiency of the robot carrying vehicles is improved.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of a method of scheduling robotic vehicles of the present invention.
Figure 2 is a flow diagram of another embodiment of a method of scheduling robotic vehicles of the present invention.
Fig. 3A is a flowchart illustrating a method for scheduling a robotic van based on information of goods to be delivered.
FIG. 3B is a diagram illustrating the result of zone division according to the degree of aggregation of the cargo conveyers to be delivered
FIG. 4 is a flowchart illustrating a method for implementing scheduling of robotic vehicles based on historical shipment information.
Fig. 5 is a schematic configuration diagram of an embodiment of a scheduling apparatus of a robotic van according to the present invention.
Fig. 6 is a schematic structural view of a scheduling apparatus of a robotic cart according to an embodiment of the present invention.
Fig. 7 is a structural view of a scheduling apparatus of a robotic truck according to still another embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a scheduling method for idle robot carriers.
In the present invention, a robotic handling vehicle is an intelligent device capable of automatically handling goods. The robotic transport vehicle may transport goods with the aid of a transport tool. For example, a robotic cart may use pallets to transport medium items and pallets to transport small items. The idle robot carrier is a robot carrier that is currently in an idle state and does not perform a carrying task.
FIG. 1 is a flow chart of one embodiment of a method of scheduling robotic vehicles of the present invention. By the scheduling method of the embodiment, the idle robot carriers can be scheduled to the area where the number of the current robot carriers is lower than the minimum configuration number, so that the response time of the idle robot carriers to the picking task is shortened, and the working efficiency of the robot carriers is improved.
As shown in fig. 1, the scheduling method 100 of this embodiment includes:
s102, the warehouse picking area is divided into a plurality of areas.
For example, a static partitioning method or a dynamic partitioning method may be employed to partition the warehouse pick zone into several areas.
The static division method is also called a fixed area division method, and is used for fixedly dividing the warehouse sorting area into a plurality of areas.
The dynamic division method can dynamically divide the warehouse picking area into a plurality of areas according to the aggregation degree of the goods handling tools to be delivered or the aggregation degree of the historical goods handling tools to be delivered. The following embodiments will describe specific implementation procedures of the dynamic partitioning method.
Compared with a static partitioning method, the dynamic partitioning method can reflect the aggregation degree of the goods to be transported more accurately. Compared with the dynamic division method realized according to the aggregation degree of the goods handling tools for ex-warehouse, the dynamic division method realized according to the aggregation degree of the goods handling tools for historical ex-warehouse fully considers the fluctuation of the goods to be handled in time, and can more accurately reflect the current aggregation degree of the goods to be handled.
And S104, determining the area where the number of the current robotic porters is less than the minimum configuration number of the robotic porters, and determining the target area of the idle robotic porters according to the determined area.
In one embodiment, a region having the greatest allocation ratio of robotic vehicles among regions where the number of current robotic vehicles is less than the minimum number of robotic vehicles may be determined as the target region of the idle robotic vehicle.
For example, if the allocation ratio of the robotic vehicles of the area 1 is the largest and the area 1 satisfies the condition that the number of the current robotic vehicles in the area 1 is smaller than the minimum number of the robot vehicles arranged in the area 1, the area 1 is determined as the target area of the idle robotic vehicle. The allocation ratio of the robotic vehicles of zone1 is the largest, and at the allocation ratio of zone2, if zone1 does not satisfy the aforementioned condition, and zone2 satisfies the condition that the number of the robotic vehicles currently in zone2 is smaller than the minimum number of the robotic vehicles allocated in zone2, zone2 is determined as the target zone of the idle robotic vehicle.
In specific implementation, the target region may be determined by the following two methods, for example. The first method is to determine all areas where the number of current robotic vehicles is smaller than the minimum number of robotic vehicles to be deployed, and then determine the area where the allocation ratio of robotic vehicles is the largest among all the areas as the target area of the idle robotic vehicle. The second method is that the areas with the current number of the robot transport vehicles smaller than the minimum configuration number of the robot transport vehicles are searched according to the distribution proportion of the robot transport vehicles from large to small, and the areas which are searched firstly and meet the conditions are determined as the target areas of the idle robot transport vehicles. Compared with the first method, the second method can terminate the searching process for the idle robot transport vehicle once the target area is searched, and processing resources can be saved without traversing all areas.
The minimum number of the robotic trucks arranged in each area may be a fixed value, or may be determined according to the allocation ratio of the robotic trucks in the area and the total investment of the robotic trucks. For example, the product of the allocation ratio of the robotic truck in the area and the total amount of the robotic truck input is rounded down, and the obtained integer value is used as the minimum allocation number of robotic trucks in the area. The formula is expressed as follows:
Mi＝rounddown(Ri×N,0)
where Mi represents the minimum number of robot carriers arranged in the area i, RiThe allocation ratio of the robotic vehicles in the area i is shown, N is the total amount of the robotic vehicle input, and rounddown is a function rounded down.
Wherein the allocation ratio for the robotic vehicles of each zone is determined based on the ratio of the number of the goods-handling tools to be delivered in that zone to the total number of the goods-handling tools to be delivered in all zones. Alternatively, the allocation ratio for each of the zones of robotic vehicles may be determined based on a ratio of the historical frequency of the shipment in that zone to the historical total frequency of the shipment in all of the zones. The formula is expressed as follows:
wherein R isiAssignment ratio Q of robot carriers in area iiRepresenting the number of cargo handlers to be shipped out of the area i or representing the historical frequency of shipment of the cargo handlers in the area i, and n represents the total number of areas.
And S106, scheduling the idle robot transport vehicle to a target area.
The dispatch station may issue a dispatch instruction to the idle robotic vehicle instructing the idle robotic vehicle to travel to a target area to wait for a next pick task. When a new picking task exists, the idle robot carriers which reach the area where the picking task is located are dispatched to execute the picking task, or the idle robot carriers closest to the picking task are dispatched from the idle robot carriers going to the target area to execute the picking task.
Through the scheme, the idle robot carriers can be pre-adjusted to the area where the number of the current robot carriers is lower than the minimum configuration number, so that the response time of the idle robot carriers to the picking task is shortened, and the working efficiency of the robot carriers is improved.
In addition, if the warehouse picking area is dynamically divided into a plurality of areas according to the aggregation degree of the goods carriers to be delivered in the dispatching process, and/or if the allocation proportion of the robot carriers in each area is determined according to the ratio of the number of the goods carriers to be delivered in the area to the total number of the goods carriers to be delivered in all the areas in the dispatching process, and then the minimum allocation number of the robot carriers in the area is determined, the idle robot carriers can be further preset to the areas with the intensive goods carriers to be delivered, so that the response time of the idle robot carriers to picking tasks is shortened, and the working efficiency of the robot carriers is improved.
Similarly, if the warehouse picking area is dynamically divided into a plurality of areas according to the aggregation degree of the historical delivery goods handling tools in the scheduling, and/or if the allocation proportion of the robot handling vehicles in each area is determined according to the ratio of the historical delivery frequency of the goods handling tools in the area to the historical delivery total frequency of the goods handling tools in all the areas in the scheduling, and then the minimum allocation number of the robot handling vehicles in the area is determined, the idle robot handling vehicles can be further adjusted to the intensive area of the historical delivery goods handling tools in advance, so that the response time of the idle robot handling vehicles to the picking tasks is shortened, and the working efficiency of the robot handling vehicles is improved.
The above scheduling method may be further optimized, and the optimized scheduling method for robotic vehicles is described below in conjunction with fig. 2.
Figure 2 is a flow diagram of another embodiment of a method of scheduling robotic vehicles of the present invention.
As shown in fig. 2, the scheduling method 200 of this embodiment includes:
s202, the warehouse picking area is divided into a plurality of areas.
S204, monitoring whether the electric quantity of the idle robot carrying vehicle reaches a charging threshold value, if the electric quantity of the idle robot carrying vehicle is lower than or equal to the charging threshold value, scheduling the idle robot carrying vehicle to charge the pile, and if the electric quantity of the idle robot carrying vehicle is higher than the charging threshold value, continuing to execute the subsequent steps.
And S206, judging whether the number of the current robot carriers in the area where the idle robot carriers are located is larger than the minimum configuration number of the robot carriers in the area.
If the determination result is no, the area where the idle robot cart is located is determined as the target area of the idle robot cart (S207). Thereby, scheduling idle robotic vehicles too frequently is avoided.
If the determination result is yes, the area where the allocation ratio of the robotic vehicles is the largest among the areas where the number of the current robotic vehicles is smaller than the minimum number of the robotic vehicles is determined as the target area of the idle robotic vehicle (S208).
S210, a position of the conveyance tool near the center position in the target area is selected as a target position.
S212, the idle robotic truck is scheduled to a target location in the target area.
When a new picking task exists, the idle robot carriers which reach the area where the picking task is located are dispatched to execute the picking task, or the idle robot carriers closest to the picking task are dispatched from the idle robot carriers going to the target area to execute the picking task.
By means of the scheme, the response time of the idle robot carrier to the picking task can be shortened, and occupation of warehouse channel resources can be reduced.
The scheduling method for the robotic truck in the foregoing embodiment may implement a scheduling process for a free robotic truck based on information of goods to be delivered from warehouse or based on information of goods delivered from warehouse in history, for example, and the two scheduling methods are specifically described below.
Fig. 3A is a flowchart illustrating a method for scheduling a robotic van based on information of goods to be delivered.
As shown in fig. 3A, the scheduling method 300 of this embodiment includes:
s302, the warehouse picking area is divided into a plurality of areas according to the aggregation degree of the goods carrying tools to be delivered.
Specifically, the goods handling tools to be delivered are clustered according to the position distribution information of the goods handling tools to be delivered, and a goods handling tool gathering area to be delivered corresponding to each cluster is used as an area. The clustering method can be realized by adopting a k-means clustering algorithm, for example. Fig. 3B is a diagram illustrating the result of Zone division according to the degree of aggregation of the to-be-out cargo handlers, and the warehouse pick Zone is divided into 6 zones, which are respectively represented as Zone1, Zone2, Zone3, Zone4, Zone5, and Zone 6.
Then, optionally, steps S304 and S306 are performed, or step S308 is directly performed.
S304, monitoring whether the electric quantity of the idle robot carrying vehicle reaches a charging threshold value, if the electric quantity of the idle robot carrying vehicle is lower than or equal to the charging threshold value, scheduling the idle robot carrying vehicle to charge the pile, and if the electric quantity of the idle robot carrying vehicle is higher than the charging threshold value, continuing to execute the subsequent steps.
S306, it is determined whether the current number of robotic vehicles in the area where the idle robotic vehicle is located is greater than the minimum number of robotic vehicles in the area (the first number of robotic vehicles).
Wherein the minimum allocation quantity (first allocation quantity) of the robotic vehicles is determined according to the allocation ratio of the robotic vehicles in the area and the total input quantity of the robotic vehicles, and the allocation ratio of the robotic vehicles in the area is determined according to the ratio of the number of the goods-handling tools to be delivered in the area to the total number of the goods-handling tools to be delivered in all the areas.
If the determination result is no, the area where the idle robot cart is located is determined as the target area of the idle robot cart (S307). Thereby, scheduling idle robotic vehicles too frequently is avoided.
If the determination result is yes, an area where the allocation ratio of the robot carriers is the largest among areas where the number of the current robot carriers is smaller than the minimum number of the robot carriers (first number of the robot carriers) is determined as a target area of the idle robot carrier (S308).
Then, optionally, step S310 is performed, or step S312 is directly performed.
S310, a position of the conveyance tool near the center position in the target area is selected as a target position.
S312, the idle robotic truck is scheduled to a target area or a target location.
By means of the scheme, the response time of the idle robot carrier to the picking task can be shortened, and occupation of warehouse channel resources can be reduced. In addition, based on the scheduling scheme realized by the information of the goods to be delivered, the region division can more accurately reflect the aggregation degree of the goods to be transported, and can more accurately determine the allocation proportion of the robot carrying vehicles in each region, so that the idle robot carrying vehicles are pre-adjusted to the regions with dense goods carrying tools to be delivered, and the scheduling efficiency of the idle robot carrying vehicles is further improved.
FIG. 4 is a flowchart illustrating a method for implementing scheduling of robotic vehicles based on historical shipment information.
As shown in fig. 4, the scheduling method 400 of this embodiment includes:
s402, the warehouse sorting area is dynamically divided into a plurality of areas according to the aggregation degree of the historical goods carrying tools which are out of the warehouse.
Specifically, the historical ex-warehouse cargo handling tools are clustered according to the position distribution information and ex-warehouse frequency information of the historical ex-warehouse cargo handling tools, and a historical ex-warehouse cargo handling tool gathering area corresponding to each cluster is used as one area.
Then, optionally, steps S404 and S406 are performed, or step S408 is directly performed.
S404, monitoring whether the electric quantity of the idle robot carrying vehicle reaches a charging threshold value, if the electric quantity of the idle robot carrying vehicle is lower than or equal to the charging threshold value, scheduling the idle robot carrying vehicle to charge the pile, and if the electric quantity of the idle robot carrying vehicle is higher than the charging threshold value, continuing to execute the subsequent steps.
S406, it is determined whether the current number of the robotic vehicles in the area where the idle robotic vehicle is located is greater than the minimum number of the robotic vehicles arranged in the area (the second number of the robotic vehicles arranged).
Wherein the minimum allocation quantity (second allocation quantity) of the robotic vehicles is determined according to the allocation ratio of the robotic vehicles in the area and the total investment of the robotic vehicles, and the allocation ratio of the robotic vehicles in the area is determined according to the ratio of the historical frequency of delivery of the goods handling tools in the area to the historical frequency of delivery of the goods handling tools in all areas.
If the determination result is no, the area where the idle robot cart is located is determined as the target area of the idle robot cart (S407). Thereby, scheduling idle robotic vehicles too frequently is avoided.
If the determination result is yes, an area where the allocation ratio of the robot carriers is the largest among areas where the number of the current robot carriers is smaller than the minimum number of the robot carriers (second number of the robot carriers) is determined as the target area of the idle robot carrier (S408).
Then, optionally, step S410 is performed, or step S412 is directly performed.
And S410, selecting the position of the conveying tool close to the central position in the target area as a target position.
And S412, scheduling the idle robot truck to the target area or the target position.
By means of the scheme, the response time of the idle robot carrier to the picking task can be shortened, and occupation of warehouse channel resources can be reduced. In addition, based on the scheduling scheme realized by the historical warehouse-out goods information, the region division can more accurately reflect the aggregation degree of the historical warehouse-out goods, and can more accurately determine the allocation proportion of the robot carrying vehicles in each region, so that the idle robot carrying vehicles are scheduled to the regions with dense historical warehouse-out goods carrying tools in advance, and the scheduling efficiency of the idle robot carrying vehicles is further improved.
Assuming that the reduction amount of the response time of each robot carrier to a new task after being idle at every time is T, the total number of the robot carriers is N, the idle frequency of the robot carriers is F, and the total saved time is as follows:
fig. 5 is a schematic configuration diagram of an embodiment of a scheduling apparatus of a robotic van according to the present invention.
As shown in fig. 5, the scheduling apparatus 500 of this embodiment includes:
an area dividing module 502 for dividing the warehouse pick zone into a number of areas;
a target area determination module 504 for determining an area where the number of current robotic vehicles is less than the minimum configured number of robotic vehicles and determining a target area of an idle robotic vehicle based on the determined area;
a scheduling module 506 for scheduling the idle robotic vehicle to the target area.
The region dividing module 502 is configured to cluster the goods handling tools to be delivered according to the position distribution information of the goods handling tools to be delivered, and use a region where the goods handling tools to be delivered are clustered as a region; or clustering the historical ex-warehouse goods carrying tools according to the position distribution information and the ex-warehouse frequency information of the historical ex-warehouse goods carrying tools, and taking a historical ex-warehouse goods carrying tool gathering area corresponding to each cluster as an area.
The target area determining module 504 is configured to determine, as the target area of the idle robotic truck, an area where the allocation ratio of robotic trucks is the greatest among areas where the number of current robotic trucks is less than the minimum number of robotic trucks.
Wherein, the scheduling module 506 is further configured to: selecting a carrier position near the center position in the target area as a target position, and scheduling the idle robotic carrier to the target position of the target area; or when a new picking task exists, scheduling the idle robot carrier reaching the area where the picking task is located to execute the picking task, or scheduling the idle robot carrier closest to the picking task from the idle robot carriers going to the target area to execute the picking task.
As shown in fig. 5, the scheduling apparatus 500 further includes: the allocation quantity determining module 5031 is used for determining the minimum allocation quantity of the robotic porters in each area according to the allocation proportion of the robotic porters in each area and the investment total of the robotic porters.
As shown in fig. 5, the scheduling apparatus 500 further includes: an allocation proportion determining module 5032, configured to determine an allocation proportion of the robotic truck in each region according to a ratio of the number of the goods handling tools to be delivered to the region to the total number of the goods handling tools to be delivered to the region; or, for determining the allocation ratio of robotic vehicles for each zone based on the ratio of the historical frequency of shipment of the goods handling tool in that zone to the historical total frequency of shipment of the goods handling tool in all zones.
As shown in fig. 5, the scheduling apparatus 500 further includes: at least one of the determining module 5033 and the electric quantity detecting module 5034;
a determining module 5033, configured to determine whether the number of current robotic trucks in the area where the idle robotic truck is located is greater than the minimum configuration number of robotic trucks in the area; a target area determination module 504, configured to, if the determination result is yes, perform a step of determining a target area of the idle robot cart based on the determined area; determining the area where the idle robot carrying vehicle is located as a target area of the idle robot carrying vehicle under the condition that the judgment result is negative;
an electric quantity detection module 5034 for monitoring whether the electric quantity of the idle robot carrier reaches a charging threshold; and the scheduling module 506 is configured to schedule the idle robot truck to charge the pile when the electric quantity of the idle robot truck is less than or equal to the charging threshold, and schedule the idle robot truck to the target area when the electric quantity of the idle robot truck is greater than the charging threshold.
Fig. 6 is a schematic structural view of a scheduling apparatus of a robotic cart according to an embodiment of the present invention. As shown in fig. 6, the apparatus 600 of this embodiment includes: a memory 610 and a processor 620 coupled to the memory 610, the processor 620 configured to perform a method of scheduling a robotic vehicle in any of the foregoing embodiments based on instructions stored in the memory 610.
Fig. 7 is a structural view of a scheduling apparatus of a robotic truck according to still another embodiment of the present invention. As shown in fig. 7, the apparatus 700 of this embodiment includes: the memory 610 and the processor 620 may further include an input/output interface 730, a network interface 740, a storage interface 750, and the like. These interfaces 730, 740, 750, as well as the memory 610 and the processor 620, may be connected, for example, by a bus 760. The input/output interface 730 provides a connection interface for input/output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 740 provides a connection interface for various networking devices. The storage interface 750 provides a connection interface for external storage devices such as an SD card and a usb disk.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (14)
1. A method for scheduling a robotic cart, comprising:
dividing the warehouse picking area into a plurality of areas;
determining the areas where the number of the current robotic porters is smaller than the minimum configuration number of the robotic porters, and determining a target area of an idle robotic porter according to the determined areas, wherein the minimum configuration number of the robotic porters in each area is determined according to the allocation proportion of the robotic porters in the area and the total input amount of the robotic porters, and the allocation proportion of the robotic porters in each area is determined according to the ratio of the number of goods handling tools to be delivered from the warehouse in the area to the total amount of the goods handling tools to be delivered from the warehouse in all the areas, or determined according to the ratio of the historical delivery frequency of the goods handling tools in the area to the historical delivery frequency of the goods handling tools in all the areas;
scheduling the idle robotic vehicle to the target area.
2. The method of claim 1, wherein the determining a target area of an idle robotic cart from the determined areas comprises:
and determining an area with the largest allocation proportion of the robot carriers in the area where the number of the current robot carriers is less than the minimum allocation number of the robot carriers as a target area of the idle robot carriers.
3. The method of claim 1, wherein the dividing the warehouse pick zone into zones comprises:
clustering the goods handling tools to be delivered according to the position distribution information of the goods handling tools to be delivered, and taking a goods handling tool gathering area to be delivered corresponding to each cluster as an area; or the like, or, alternatively,
and clustering the historical ex-warehouse goods carrying tools according to the position distribution information and ex-warehouse frequency information of the historical ex-warehouse goods carrying tools, and taking a historical ex-warehouse goods carrying tool gathering area corresponding to each cluster as an area.
4. The method of claim 1, further comprising:
judging whether the number of the current robot carriers in the area where the idle robot carriers are located is larger than the minimum configuration number of the robot carriers in the area;
if the judgment result is yes, determining a target area of the idle robot carrier according to the determined area;
and if the judgment result is negative, determining the area where the idle robot transport vehicle is located as the target area of the idle robot transport vehicle.
5. The method of claim 1, further comprising:
monitoring whether the electric quantity of an idle robot carrier reaches a charging threshold value, if the electric quantity of the idle robot carrier is lower than or equal to the charging threshold value, scheduling the idle robot carrier to charge a pile, and if the electric quantity of the idle robot carrier is higher than the charging threshold value, executing a target area determining step and scheduling the idle robot carrier to the target area.
6. The method of claim 1, further comprising:
selecting a position of the carrier near the center position in the target area as a target position;
and scheduling the idle robotic truck to the target location of the target area.
7. The method of claim 1, further comprising:
when a new picking task exists, scheduling the idle robot carrier reaching the area where the picking task is located to execute the picking task, or scheduling the idle robot carrier closest to the picking task from the idle robot carriers going to the target area to execute the picking task.
8. A scheduling device for a robotic cart, comprising:
the area dividing module is used for dividing the warehouse picking area into a plurality of areas;
the distribution proportion determining module is used for determining the distribution proportion of the robot transport vehicles in each area according to the ratio of the number of the goods transport tools to be delivered out of the area to the total number of the goods transport tools to be delivered out of the area; or, the allocation proportion of the robotic truck in each region is determined according to the ratio of the historical frequency of the goods handling tool to the total historical frequency of the goods handling tool in all regions;
the allocation quantity determining module is used for determining the minimum allocation quantity of the robotic porters in each area according to the allocation proportion of the robotic porters in each area and the investment total amount of the robotic porters;
the system comprises a target area determining module, a judging module and a control module, wherein the target area determining module is used for determining an area where the number of the current robotic handling vehicles is less than the minimum configuration number of the robotic handling vehicles and determining a target area of an idle robotic handling vehicle according to the determined area;
a scheduling module to schedule the idle robotic vehicle to the target area.
9. The apparatus of claim 8, wherein the target area determination module is to determine an area of greatest assigned proportion of robotic vehicles in an area where a number of current robotic vehicles is less than a minimum configured number of robotic vehicles as the target area for an idle robotic vehicle.
10. The apparatus of claim 8, wherein the region dividing module is configured to cluster the to-be-discharged goods handling tools according to the position distribution information of the to-be-discharged goods handling tools, and take a region of the to-be-discharged goods handling tools cluster corresponding to each cluster as a region; or the like, or, alternatively,
and clustering the historical ex-warehouse goods carrying tools according to the position distribution information and ex-warehouse frequency information of the historical ex-warehouse goods carrying tools, and taking a historical ex-warehouse goods carrying tool gathering area corresponding to each cluster as an area.
11. The apparatus of claim 8, further comprising: at least one module of the judging module and the electric quantity detecting module;
the judging module is used for judging whether the number of the current robot carriers in the area where the idle robot carriers are located is larger than the minimum configuration number of the robot carriers in the area; and the target area determining module is used for determining the target area of the idle robot transport vehicle according to the determined area when the judgment result is yes; determining the area where the idle robot carrying vehicle is located as a target area of the idle robot carrying vehicle under the condition that the judgment result is negative;
the electric quantity detection module is used for monitoring whether the electric quantity of the idle robot transport vehicle reaches a charging threshold value; and the scheduling module is used for scheduling the idle robot transport vehicle to charge the electric pile under the condition that the electric quantity of the idle robot transport vehicle is lower than or equal to the charging threshold value, and scheduling the idle robot transport vehicle to the target area under the condition that the electric quantity of the idle robot transport vehicle is higher than the charging threshold value.
12. The apparatus of claim 8, wherein the scheduling module is further configured to:
selecting a carrier position in a target area near a center position as a target position and scheduling the idle robotic carrier to the target position of the target area; or the like, or, alternatively,
when a new picking task exists, scheduling the idle robot carrier reaching the area where the picking task is located to execute the picking task, or scheduling the idle robot carrier closest to the picking task from the idle robot carriers going to the target area to execute the picking task.
13. A scheduling device for a robotic cart, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of any of claims 1-7 based on instructions stored in the memory.
14. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710068223.5A CN108398924B (en) | 2017-02-08 | 2017-02-08 | Scheduling method and scheduling device for robot transport vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710068223.5A CN108398924B (en) | 2017-02-08 | 2017-02-08 | Scheduling method and scheduling device for robot transport vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108398924A CN108398924A (en) | 2018-08-14 |
| CN108398924B true CN108398924B (en) | 2020-09-01 |
Family
ID=63093800
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710068223.5A Active CN108398924B (en) | 2017-02-08 | 2017-02-08 | Scheduling method and scheduling device for robot transport vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108398924B (en) |
Families Citing this family (41)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109409603B (en) * | 2018-10-26 | 2022-04-19 | 广东科达洁能股份有限公司 | Scheduling algorithm applied to multiple traveling vehicles |
| CN109613896A (en) * | 2018-11-20 | 2019-04-12 | 上海物景智能科技有限公司 | A scheduling method and device for multiple automatic guided vehicles |
| CN111308995B (en) * | 2018-11-27 | 2024-01-16 | 北京京东乾石科技有限公司 | Scheduling method and device of transfer robot, medium and electronic equipment |
| CN111275370A (en) * | 2018-12-04 | 2020-06-12 | 北京京东尚科信息技术有限公司 | AGV dynamic scheduling method, system, equipment and storage medium |
| CN109670708A (en) * | 2018-12-19 | 2019-04-23 | 中山乐心电子有限公司 | The distribution system and method for picking business of storing in a warehouse |
| CN109636295A (en) * | 2018-12-26 | 2019-04-16 | 深圳蓝胖子机器人有限公司 | Dispatching method, the device, storage medium that sorting system, mobile sorting are stood |
| CN109848050A (en) * | 2018-12-26 | 2019-06-07 | 深圳蓝胖子机器人有限公司 | Mobile sorting station and its dynamic adjusting method, device, storage medium |
| CN111724015B (en) * | 2019-03-21 | 2023-08-08 | 北京京东乾石科技有限公司 | Method, device, equipment and storage medium for switching transport tasks |
| CN110021189A (en) * | 2019-04-26 | 2019-07-16 | 成都智狐科技有限公司 | Intelligent parking method based on parallel access |
| CN111950818A (en) * | 2019-05-14 | 2020-11-17 | 北京京东尚科信息技术有限公司 | Unmanned vehicle dispatching method and device |
| CN111950943A (en) * | 2019-05-14 | 2020-11-17 | 北京京东尚科信息技术有限公司 | Dispatching method, device and equipment for distribution vehicle and storage medium |
| CN110533358A (en) * | 2019-07-17 | 2019-12-03 | 天津五八到家科技有限公司 | Transport task distribution method, device and electronic equipment |
| CN112474368B (en) * | 2019-09-12 | 2023-12-05 | 北京京东乾石科技有限公司 | Goods picking method, device, equipment and computer readable medium |
| CN110766228B (en) * | 2019-10-25 | 2022-07-05 | 北京京东乾石科技有限公司 | Method, device, picking system, electronic device and medium for picking |
| CN111768062B (en) * | 2019-10-31 | 2024-06-21 | 北京京东乾石科技有限公司 | Method and device for determining scheduling mode of sorting equipment and electronic equipment |
| CN112785212B (en) * | 2019-11-06 | 2024-09-24 | 北京京东乾石科技有限公司 | Transportation equipment management method and device |
| CN112966992B (en) * | 2019-12-13 | 2024-06-18 | 北京京东乾石科技有限公司 | Order production method and device |
| CN113050549B (en) | 2019-12-26 | 2022-06-10 | 炬星科技（深圳）有限公司 | Method, device and storage medium for freely networkable robot to perform tasks |
| CN113044454B (en) * | 2019-12-27 | 2022-12-20 | 北京极智嘉科技股份有限公司 | Method for controlling goods taking of carrying equipment and related device |
| CN113222311A (en) * | 2020-02-06 | 2021-08-06 | 北京京东乾石科技有限公司 | Robot parking method and system |
| CN113534783B (en) * | 2020-04-15 | 2024-06-14 | 北京旷视机器人技术有限公司 | Distribution method and device of transport vehicle in warehouse and electronic equipment |
| CN111762519B (en) * | 2020-04-22 | 2022-06-07 | 北京京东乾石科技有限公司 | Method and system for guiding picking robot operation and scheduling device |
| CN113554250B (en) * | 2020-04-23 | 2024-11-26 | 北京京东乾石科技有限公司 | Information processing method and device for transport vehicle |
| CN113673918B (en) * | 2020-05-15 | 2024-09-24 | 北京京东乾石科技有限公司 | Method and device for controlling transportation device and storage medium |
| CN113799638A (en) * | 2020-06-15 | 2021-12-17 | 锥能机器人嘉兴有限公司 | Method, apparatus and storage medium for controlling the charging of an automated transport unit |
| CN111563493B (en) * | 2020-07-15 | 2021-07-20 | 炬星科技（深圳）有限公司 | Work information acquisition method and equipment based on image recognition and storage medium |
| CN111824662B (en) * | 2020-07-24 | 2022-03-08 | 北京极智嘉科技股份有限公司 | Stereoscopic warehouse for picking goods, control method and control center |
| CN120106460A (en) * | 2020-08-31 | 2025-06-06 | 北京极智嘉科技股份有限公司 | Robot task allocation method, device, electronic device and storage medium |
| CN112077861A (en) * | 2020-09-23 | 2020-12-15 | 江西太空机器人科技有限公司 | Robot scheduling method, device, equipment and storage medium |
| WO2022095316A1 (en) * | 2020-11-06 | 2022-05-12 | 上海快仓智能科技有限公司 | Warehousing system control method and apparatus, and device and computer-readable storage medium |
| CN112591663A (en) * | 2020-11-13 | 2021-04-02 | 机械工业第九设计研究院有限公司 | Automatic handling system based on AGV and multi-AGV cooperation method |
| JP7066029B1 (en) * | 2020-12-04 | 2022-05-12 | シャンハイ クイックトロン インテリジェント テクノロジー カンパニー リミテッド | Warehouse system control methods, appliances, equipment and computer readable storage media |
| CN112633756A (en) * | 2020-12-30 | 2021-04-09 | 深圳市人工智能与机器人研究院 | Warehouse logistics scheduling method and related equipment |
| CN112966977A (en) * | 2021-03-31 | 2021-06-15 | 深圳市库宝软件有限公司 | Task allocation method and device, control terminal and warehousing system |
| CN115345397A (en) * | 2021-05-13 | 2022-11-15 | 深南电路股份有限公司 | A material scheduling method, device and computer-readable storage medium |
| CN113753462B (en) * | 2021-09-14 | 2023-04-11 | 深圳市库宝软件有限公司 | Task allocation method, device, equipment, warehousing system and storage medium |
| CN114873122B (en) * | 2022-04-14 | 2023-09-05 | 深圳市海柔创新科技有限公司 | Method, device, equipment and storage medium for storage of material boxes |
| CN115303691A (en) * | 2022-08-04 | 2022-11-08 | 深圳市库宝软件有限公司 | Container handling task assignment method, device, equipment, system and storage medium |
| WO2024078453A1 (en) * | 2022-10-12 | 2024-04-18 | 北京极智嘉科技股份有限公司 | Robot scheduling method and apparatus |
| CN115649722B (en) * | 2022-12-26 | 2023-04-14 | 浙江凯乐士科技集团股份有限公司 | Dispatching method and device for storage vehicles |
| CN117273606B (en) * | 2023-09-19 | 2024-04-12 | 中油管道物资装备有限公司 | Unmanned carrier scheduling method and system based on intelligent warehouse |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105490345A (en) * | 2016-01-04 | 2016-04-13 | 杭州亚美利嘉科技有限公司 | A charging control method and device for a robot |
| CN105892321A (en) * | 2016-04-28 | 2016-08-24 | 京东方科技集团股份有限公司 | Dispatching method and device for cleaning robot |
| CN205766102U (en) * | 2016-05-30 | 2016-12-07 | 上海发那科机器人有限公司 | There is the cooperation robot of region locomotivity |
| CN106228302A (en) * | 2016-07-21 | 2016-12-14 | 上海仙知机器人科技有限公司 | A kind of method and apparatus for carrying out task scheduling in target area |
| CN106354142A (en) * | 2016-10-27 | 2017-01-25 | 金建荣 | Intelligent transportation system based on Internet of Things and application |
| CN106355345A (en) * | 2016-09-08 | 2017-01-25 | 京东方科技集团股份有限公司 | Intelligent dispatching system and method of automatic vending robots |
-
2017
- 2017-02-08 CN CN201710068223.5A patent/CN108398924B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105490345A (en) * | 2016-01-04 | 2016-04-13 | 杭州亚美利嘉科技有限公司 | A charging control method and device for a robot |
| CN105892321A (en) * | 2016-04-28 | 2016-08-24 | 京东方科技集团股份有限公司 | Dispatching method and device for cleaning robot |
| CN205766102U (en) * | 2016-05-30 | 2016-12-07 | 上海发那科机器人有限公司 | There is the cooperation robot of region locomotivity |
| CN106228302A (en) * | 2016-07-21 | 2016-12-14 | 上海仙知机器人科技有限公司 | A kind of method and apparatus for carrying out task scheduling in target area |
| CN106355345A (en) * | 2016-09-08 | 2017-01-25 | 京东方科技集团股份有限公司 | Intelligent dispatching system and method of automatic vending robots |
| CN106354142A (en) * | 2016-10-27 | 2017-01-25 | 金建荣 | Intelligent transportation system based on Internet of Things and application |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108398924A (en) | 2018-08-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108398924B (en) | Scheduling method and scheduling device for robot transport vehicle | |
| CN109978423B (en) | Inventory scheduling method, inventory scheduling device and computer-readable storage medium | |
| US11597600B2 (en) | Article picking method, server and system | |
| CN110059992B (en) | Goods warehouse-in method, device and computer readable storage medium | |
| CN112278674B (en) | Scheduling method, device, equipment and storage medium | |
| EP3816919A1 (en) | Order processing method and device, server, and storage medium | |
| CN110390499B (en) | Order distribution method and device | |
| US11155412B2 (en) | Warehouse management system and warehouse management method | |
| CN112633756A (en) | Warehouse logistics scheduling method and related equipment | |
| CN107194646A (en) | Stock's dispatching method and device | |
| KR20230067645A (en) | Order processing and shipping methods, devices, systems and storage media | |
| CN113213039B (en) | Material box robot control method and device, electronic equipment and storage medium | |
| CN110633926A (en) | Method, apparatus and system for handling work-bin and storage medium | |
| WO2022135088A1 (en) | Control method and apparatus for warehouse robot, and device and storage medium | |
| CN109552795B (en) | Goods delivery method and device and computer readable storage medium | |
| CN114415610B (en) | Scheduling method and device for robot, electronic equipment and storage medium | |
| CN109961244A (en) | Item sorting method and related apparatus | |
| CN114580996A (en) | Method for discharging bin | |
| CN112193953B (en) | Elevator resource scheduling method and device | |
| CN115293654A (en) | Shuttle vehicle task scheduling method and device and computer equipment | |
| CN113128924B (en) | Cargo scheduling method, apparatus and computer readable storage medium | |
| CN107168260A (en) | A kind of store management scheduling system of electric-power metering production | |
| CN116451957A (en) | Method, device, system and storage medium for configuring multicast wall | |
| CN114833071B (en) | Logistics sorting station and cargo sorting method and system thereof | |
| CN111123870A (en) | System control method and device and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20190801 Address after: 300 457 days First Floor, Phase 2, No. 10, Fourth Avenue, Binhai New Area Economic and Technological Development Zone, Tianjin Applicant after: Tianjin Jingdong Shentuo Robot Technology Co., Ltd. Address before: East Building 11, 100195 Beijing city Haidian District xingshikou Road No. 65 west Shan creative garden district 1-4 four layer of 1-4 layer Applicant before: Beijing Jingdong Shangke Information Technology Co., Ltd. Applicant before: Beijing Jingdong Century Commerce Co., Ltd. |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200521 Address after: Room A1905, 19th floor, No. 2 Building, 18 Kechuang 11th Street, Beijing Daxing District, Beijing Applicant after: Beijing Jingdong Qianshi Technology Co.,Ltd. Address before: 300 457 days First Floor, Phase 2, No. 10, Fourth Avenue, Binhai New Area Economic and Technological Development Zone, Tianjin Applicant before: Tianjin Jingdong Shentuo Robot Technology Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |