CN116605574B - Parameter configuration and collaborative scheduling platform for large-scale robot picking system - Google Patents
Parameter configuration and collaborative scheduling platform for large-scale robot picking system Download PDFInfo
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- CN116605574B CN116605574B CN202310890261.4A CN202310890261A CN116605574B CN 116605574 B CN116605574 B CN 116605574B CN 202310890261 A CN202310890261 A CN 202310890261A CN 116605574 B CN116605574 B CN 116605574B
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- 239000013598 vector Substances 0.000 claims description 23
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- 238000000034 method Methods 0.000 claims description 10
- 238000012913 prioritisation Methods 0.000 claims description 4
- 238000012876 topography Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 description 13
- 230000006872 improvement Effects 0.000 description 8
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
- B65G1/137—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed
- B65G1/1373—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed for fulfilling orders in warehouses
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- 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]
Abstract
The application relates to the technical field of logistics scheduling, in particular to a parameter configuration and collaborative scheduling platform of a large-scale robot picking system. The system comprises a task allocation unit, a warehouse data identification unit, a global path coordination unit and a motion control unit. According to the application, the task allocation unit is used for allocating robots which work efficiently, the warehouse data identification unit is used for identifying warehouse data placed by the current logistics, the global path coordination unit is used for analyzing global influence factors based on the warehouse data and the data for identifying picked goods, and the global path coordination unit is used for cooperatively optimizing a plurality of robot routes which run simultaneously, so that the robots do not influence each other when the optimal operation is met, the robots are prevented from colliding when picking or carrying goods, the safety is improved while the efficient operation is met, and the channel size can be analyzed when the robots pass through the channel, so that the robots can conveniently pass through, the warehouse congestion caused by all waiting is avoided, and the picking efficiency is improved.
Description
Technical Field
The application relates to the technical field of logistics scheduling, in particular to a parameter configuration and collaborative scheduling platform of a large-scale robot picking system.
Background
With the rapid development of industries such as logistics, electronic commerce and the like, the application of a large-scale robot picking system is also more and more widespread, however, the current system has the following problems: manual intervention is high in cost, allocation tasks are not accurate enough, picking efficiency is low, and the like, so that an intelligent robot picking system needs to be provided, so that the efficiency and stability of the intelligent robot picking system are improved, and the operation cost is reduced;
the following problems exist in the existing robot picking system, such as high manual intervention cost, inaccurate task allocation, low picking efficiency and the like, so that the stability and efficiency of the robot picking system are improved, the operation cost is reduced, the robot picking system is an innovation and commercial value in the field of the robot picking system, and in the prior art, the robot picking is scheduled in many ways, for example: according to the Chinese patent application number: CN202111669707.8 provides a multi-machine collaborative robot system and a control method thereof. The method comprises the following steps: receiving logistics bill information, and converting the logistics bill information into text information, wherein the text information comprises names of materials to be transported, preset quantity and destination points; acquiring a material storage place list storing materials to be transported, sequencing the material storage place list from the near to the far according to the distance from a destination point, and acquiring a first material storage place sequenced first in the list; acquiring a first idle logistics robot list around a first material storage place and the positions of the first idle logistics robot list, and sequencing the first idle logistics robot list from the near to the far according to the distance between the first idle logistics robot list and the first material storage place; and informing the first number of idle logistics robots to go to the first material storage place, and after loading the preset number of materials to be transported, transporting the preset number of materials to be transported to the destination point. According to the method, intelligent analysis and task allocation can be performed according to the logistics freight bill, so that freight bill materials can be transported to a destination point from a material storage area in an efficient and time-saving manner;
however, at present, when the robots are scheduled, the paths with relatively short distances are selected more preferentially, however, when a plurality of robots are scheduled simultaneously, the distances between a plurality of end points of material picking are very short, so that the paths picked by the robots are concentrated in the narrow paths to cause blockage, and particularly when the robots carry a large amount of logistics goods, the optimal paths are not convenient and adaptive to be planned according to the global planning of the storage, so that collision is caused between the logistics goods and the aisle.
Disclosure of Invention
The application aims to provide a parameter configuration and cooperative scheduling platform of a large-scale robot picking system so as to solve the problems in the background technology.
In order to achieve the above purpose, the application provides a parameter configuration and cooperative scheduling platform of a large-scale robot picking system, which comprises a task allocation unit, a warehouse data identification unit, a global path cooperative unit and a motion control unit;
the task allocation unit is used for establishing a task allocation model, identifying a state input model of the robot and outputting the robot to be operated; the warehouse data identification unit is used for identifying warehouse data of logistics placement; the global path coordination unit is used for coordinating the data of the picked goods and the warehouse data based on the warehouse data identified by the warehouse data identification unit and the data of the picked goods, and distributing the size and the path of the articles carried by the robot; the motion control unit is used for configuring motion parameters according to the robot route operated by the global path coordination unit.
As a further improvement of the technical scheme, the task allocation unit comprises a priority allocation module, a robot state identification module and a cooperative scheduling module;
the priority distribution module is used for typing in priority data of the logistics cargoes to be picked; the robot state recognition module is used for recognizing robot state data; the collaborative scheduling module is used for scheduling the subsequent logistics picking work of the robot based on the robot state identification module to obtain an optimal path plan and determine the robot to be worked.
As a further improvement of the technical scheme, specifically, the state data of the robot state identification module comprises electric quantity data, working states, working progress, position data and working time.
As a further improvement of the technical scheme, in the priority allocation module, the following formula is adopted to perform priority ordering:
P = W * D
wherein P represents the priority of logistics goods, W represents the importance degree of the goods, and D represents the distance between the goods and the robot;
the cooperative scheduling module adopts a path planning algorithm, and the expression is as follows:
f(n) = g(n) + h(n)
where f (n) represents the valuation function value of the current node, g (n) represents the cost from the start point to the current node, and h (n) represents the estimated cost from the current node to the end point.
As a further improvement of the technical scheme, the warehouse data comprise warehouse terrain, warehouse aisles and aisle sizes.
As a further improvement of the technical scheme, the global path coordination unit comprises a path planning module, a cargo size identification module, a multi-robot path distribution module, a task group distribution module and a path selection module;
the path planning module is used for planning a path by using the cooperative scheduling module according to the current position of the robot and the position of the object to be picked, and generating a plurality of possible paths, wherein each path is a sequence formed by a plurality of continuous straight line segments;
the goods size recognition module is used for measuring the size of each object to be picked, recording the length, the width and the height of each object to be picked and calculating a size vector;
the multi-robot path distribution module is used for grouping all the objects which can be carried according to the maximum size which can be carried by the robot according to the size vector of the goods to be picked, the residual capacity of the robot on the path and the position information of the robot, so as to obtain a plurality of task groups;
the task group distribution module is used for calculating the width size of the straight line segment on each path, respectively comparing the width size with the maximum sizes corresponding to a plurality of task groups, selecting paths with the maximum sizes smaller than the width sizes as accommodating paths of the task groups, and calculating the time required by each path to reach a destination;
the path selection module is used for comparing the time required by each path to reach the destination calculated by the task group allocation module through a numerical comparison technology, and selecting a group of optimal paths to be allocated to the robot on the basis of the principle that the path arrival time is shortest.
As a further improvement of the technical scheme, the global path coordination unit further comprises an aisle local dividing module, wherein the aisle local dividing module is used for dividing the width capacity of the aisle when detecting that a plurality of robots relatively travel in the same path aisle, so that the dividing capacity of the aisle meets the size vector of the robots, and the robots travel in the matched dividing capacity.
As a further improvement of the technical scheme, the aisle local dividing module adopts an aisle local dividing algorithm and comprises the following steps:
according to the planned number of robots and the robot size vector, the maximum feasible width of the aisle is calculated in advance; when the path selection module determines a path, and a plurality of robots relatively run in the same path aisle, the path aisle width is divided into a plurality of small areas, for each small area, the small areas which the robots need to pass through are calculated according to the current position and the destination of each robot, the robots can only occupy corresponding widths when advancing, and when the robots reach the small areas, the robots send messages to other robots so that the robots know that the areas are occupied, and the method comprises the following gestures:
the small area left in the current aisle does not meet the size vectors of other robots, and the other robots need to wait for the area to be idle and then move;
and the small area left in the current aisle meets the size vectors of other robots, and the other robots travel from the small area left.
As a further improvement of the technical scheme, the motion control unit comprises a driving running module, a parameter configuration module and a virtual field module;
the driving running module is used for controlling the robot to execute running instructions and carrying instructions according to the path determined by the path selection module; the parameter configuration module is used for setting corresponding motion parameters for different robots to adapt to different working environments; the virtual field of view module is used for introducing the mixed reality technology and combining the robot field of view with the virtual reality technology.
Compared with the prior art, the application has the beneficial effects that:
1. in the parameter configuration and collaborative scheduling platform of the large-scale robot picking system, the robots which work efficiently are distributed by means of the task distribution unit according to the priority of the tasks, meanwhile, warehouse data placed in the current logistics are identified by the warehouse data identification unit, follow-up auxiliary robot picking and path planning are facilitated, then, the overall path collaboration unit is used for cooperatively picking the cargo data and the warehouse data based on the warehouse data and the data for identifying the picked cargo, the size and the path of the articles carried by the robots are distributed, after the optimal path is determined, the robots are driven to operate by the motion control unit, collision caused when the robots pick or carry the cargoes is avoided, and safety is improved while efficient operation is met.
2. In the parameter configuration and collaborative scheduling platform of the large-scale robot picking system, when the aisle local dividing module detects that a plurality of robots relatively run in the same path aisle, the aisle width capacity is divided, so that the divided capacity of the aisle meets the robot size vector, the robots run in the matched divided capacity, the aisle size can be analyzed when the robots pass through the aisle, the robots can conveniently pass through, storage congestion caused by all waiting is avoided, and the picking efficiency is improved.
Drawings
FIG. 1 is a schematic block diagram showing the overall structure of embodiment 1 of the present application;
FIG. 2 is a schematic block diagram of a task allocation unit according to embodiment 1 of the present application;
FIG. 3 is a schematic block diagram of a global path coordination unit according to embodiment 1 of the present application;
fig. 4 is a schematic block diagram of a motion control unit according to embodiment 1 of the present application.
The meaning of each reference sign in the figure is:
100. a task allocation unit; 110. a priority allocation module; 120. a robot state recognition module; 130. a cooperative scheduling module;
200. a warehouse data identification unit;
300. a global path cooperation unit; 310. a path planning module; 320. a cargo size identification module; 330. a multi-robot path allocation module; 340. a task group allocation module; 350. a path selection module; 360. the aisle local dividing module;
400. a motion control unit; 410. driving the driving module; 420. a parameter configuration module; 430. and a virtual field module.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1-4, a first embodiment of the present application is shown, and the present embodiment provides a parameter configuration and co-scheduling platform of a large-scale robotic picking system, including a task allocation unit 100, a warehouse data identification unit 200, a global path co-operation unit 300, and a motion control unit 400;
the task allocation unit 100 is used for establishing a task allocation model, identifying a state input model of the robot, and outputting the robot to be worked;
the task allocation unit 100 includes a priority allocation module 110, a robot state recognition module 120, and a co-scheduling module 130;
the priority allocation module 110 is used for typing in priority data of the logistic goods to be picked, determining the emergency degree of logistic picking, and enabling the goods with high priority to be picked earlier, so that the robot can pick the emergency goods preferentially according to the priority data; the robot state recognition module 120 is used for recognizing robot state data; the collaborative scheduling module 130 is configured to schedule a subsequent logistics picking job of the robot based on the robot state recognition module 120, obtain an optimal path plan, and determine a robot to be worked.
Specifically, the state data of the robot state recognition module 120 includes electric quantity data, a working state, a working progress, position data and a working time length;
the electric quantity data are used for reminding the identification robot of the electric quantity when the electric quantity is insufficient; the working state is used for judging whether the robot is idle or working, so that cooperative scheduling is conveniently carried out on the idle robot, the working progress is used for predicting the working time length, so that the working progress is judged according to the working time length, meanwhile, the time required for completing subsequent work can also be judged, the cooperative scheduling is conveniently extracted when the current work is finished, the position data are used for identifying the position of the current robot and the position of a picking place, the positions of a plurality of robots to the picking place are conveniently identified, the robots with short distances are conveniently selected cooperatively for working, and the high efficiency of logistics picking is improved;
therefore, when the task allocation unit 100 works, the priority allocation module 110 determines the logistics position to be selected, and at the same time, identifies the robot closest to the picking destination and in the idle state to the robot state identification module 120, so that the cooperative scheduling module 130 controls the robot to quickly move to the working area according to the positioning of the destination, thereby realizing more accurate robot control and more efficient task execution.
In the prioritization module 110, prioritization is performed using the following formula:
P = W * D
wherein P represents the priority of logistics cargoes, W represents the importance degree of the articles, D represents the distance between the articles and the robot, and the articles with higher priority can be selected earlier;
the cooperative scheduling module 130 adopts a path planning algorithm, and the expression is:
f(n) = g(n) + h(n)
wherein f (n) represents the valuation function value of the current node, g (n) represents the cost from the starting point to the current node, h (n) represents the estimated cost from the current node to the end point, and the optimal robot position from the destination is obtained.
The warehouse data identification unit 200 is used for identifying warehouse data of logistics placement;
the storage data comprises storage topography, storage aisle and aisle size;
the storage corridor is a route corridor of the whole storage, and the passing corridor size is a width size through which the corridor can pass.
The global path coordination unit 300 is configured to coordinate the picked goods data and the stocked data based on the stocked data identified by the stocked data identification unit 200 and the data identifying the picked goods, and allocate the size and path of the robot for carrying the goods;
the global path coordination unit 300 includes a path planning module 310, a cargo size identification module 320, a multi-robot path allocation module 330, a task allocation module 340, and a path selection module 350;
the path planning module 310 is configured to perform path planning using the cooperative scheduling module 130 according to a current position of the robot and a position of the item to be picked, and generate a plurality of possible paths, each path being a sequence formed by a plurality of consecutive straight line segments;
the cargo size recognition module 320 is configured to measure the size of each item to be picked, record the length, width and height of the item, and calculate a size vector;
the multi-robot path allocation module 330 is configured to group all the objects that can be carried according to the maximum size that can be carried by the robot according to the size vector of the goods to be picked, the remaining capacity of the robot on the path, and the position information of the robot, so as to obtain a plurality of task groups;
the task group allocation module 340 is configured to calculate a width dimension of a straight line segment on each path, respectively perform numerical comparison with maximum dimensions corresponding to a plurality of task groups, select a path with a maximum dimension smaller than the width dimension as a receiving path of the task group, and calculate a time required for each path to reach a destination;
the path selection module 350 is configured to compare the time required for each path to reach the destination calculated by the task group allocation module 340 by using a numerical comparison technique, select a group of optimal paths based on the principle that the path arrival time is shortest, allocate the optimal paths to the robots, and then perform status update and movement control on each robot by using the motion control unit 400, so as to cooperatively complete the handling process of the items to be picked until all the items are completely handled;
working principle: for each object i, measuring a size vector v (i), calculating a set S= { S1, S2, …, sn } formed by a group of paths on a map by using a path planning algorithm with the current position of the robot as a starting point, calculating the maximum carrying size wj of each robot j, dividing S into a plurality of groups, and each group consists of a plurality of objects, wherein each object i meets v (i). Ltoreq.wj; for each task group G [ k ], screening out all path sets S' meeting G [ k ] from S, and calculating the arrival time t (S) of each path; for each robot j, selecting a path set Sj with the shortest arrival time in S' so that Σt (S) is the smallest; and for each robot j, completing the carrying task according to the planned path in Sj.
The motion control unit 400 is configured to configure motion parameters according to the robot route operated by the global path coordination unit 300;
the motion control unit 400 includes a driving traveling module 410, a parameter configuration module 420, and a virtual field of view module 430;
the driving running module 410 is used for controlling the robot to execute running instructions and carrying instructions according to the path determined by the path selecting module 350; the parameter configuration module 420 is configured to set corresponding motion parameters for different robots to adapt to different working environments; the virtual field module 430 is used for introducing a mixed reality technology, combining the robot field with the virtual reality technology, and improving the operation precision and efficiency of operators;
specifically, the virtual field of view module 430 operates on the following principle: a virtual reality scene is designed, which is similar to a robot reality environment and comprises information such as a model and textures of an actual environment object, then the virtual reality scene is fused with the robot reality environment by utilizing a mixed reality technology, a sensor, a camera and the like carried by the robot are adopted, real environment information is acquired in real time, the virtual reality scene is projected into a visual field of the robot in an over-reality mode, then an image and a video are processed by utilizing a computer vision technology, information such as the position and the direction of a key object is extracted, the virtual reality scene is updated on the basis of the information, so that an over-reality effect is realized, when the robot moves, the virtual reality scene is updated in real time by utilizing the mixed reality technology, the robot is helped to acquire more scene information, the perception effect of the robot is improved, then the robot is helped to carry out path planning by simulating and observing the motion trail of the robot by utilizing the virtual reality technology, and the possible risk is predicted, and therefore the path planning algorithm is improved, and the autonomous decision making capability of the robot is improved.
In summary, the present application allocates robots that work efficiently by means of the task allocation unit 100 according to the priorities of tasks, and at the same time, recognizes the warehouse data placed by the current logistics by the warehouse data recognition unit 200, facilitates the subsequent auxiliary robot picking and path planning, and then, the global path coordination unit 300 coordinates the picking and storage data based on the warehouse data and the data for recognizing the picked goods, allocates the sizes and paths of the articles carried by the robots, and after determining the optimal path, drives the robots to perform the work by the motion control unit 400.
Since the width in the aisle is limited, if the size vectors of the robots satisfy the aisle width capacity, but since the local traveling positions are not allocated, the robots and the cargoes collide, a second embodiment of the present application is shown, which is different from the first embodiment in that: the global path cooperation unit 300 further includes an aisle local dividing module 360, where the aisle local dividing module 360 is configured to divide the width capacity of an aisle when detecting that a plurality of robots relatively travel in the same path aisle, so that the divided capacity of the aisle satisfies the robot size vector, and the plurality of robots travel in the adapted divided capacity.
Specifically, the aisle local division module 360 adopts an aisle local division algorithm, including the following steps:
according to the planned number of robots and the robot size vector, the maximum feasible width W of the aisle is calculated in advance, namely W=n×v (j), wherein n represents the number of robots, and v (j) represents the size vector of a single robot; when the path selection module 350 determines that a plurality of robots relatively run in the same path aisle, dividing the width of the path aisle into a plurality of small areas, and for each small area, dividing the small area according to the size vector of the robot, assuming that the length of the small area is L, dividing the small area into m sections, wherein the width of each section is wi=L/m; for each robot, calculating a small area which the robot needs to pass through according to the current position and the destination, wherein the robot can only occupy corresponding width when advancing, and when the robot reaches the small area, sending a message to other robots to let the robots know that the area is occupied, and the method comprises the following gestures:
the small area left in the current aisle does not meet the size vectors of other robots, and the other robots need to wait for the area to be idle and then move;
the size vectors of other robots are met by the small areas left in the second gesture and the current aisle, and the other robots travel from the small areas left, so that the size of the aisle can be analyzed when a plurality of robots pass through the aisle, the robots can conveniently pass through the aisle, storage congestion caused by all waiting is avoided, and the picking efficiency is improved.
The foregoing has shown and described the basic principles, principal features and advantages of the application. It will be understood by those skilled in the art that the present application is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present application, and are not intended to limit the application, and that various changes and modifications may be made therein without departing from the spirit and scope of the application as claimed. The scope of the application is defined by the appended claims and equivalents thereof.
Claims (5)
1. The parameter configuration and cooperative scheduling platform of the large-scale robot picking system is characterized in that: comprises a task allocation unit (100), a warehouse data identification unit (200), a global path coordination unit (300) and a motion control unit (400);
the task allocation unit (100) is used for establishing a task allocation model, identifying a state input model of the robot and outputting the robot to be operated; the warehouse data identification unit (200) is used for identifying warehouse data of logistics placement; the global path coordination unit (300) is used for coordinating the picking goods data and the storage data based on the storage data identified by the storage data identification unit (200) and the data for identifying the picking goods, and distributing the sizes and the paths of the articles carried by the robot; the motion control unit (400) is used for configuring motion parameters according to the robot route operated by the global path coordination unit (300);
the task allocation unit (100) comprises a priority allocation module (110), a robot state identification module (120) and a cooperative scheduling module (130);
the priority allocation module (110) is used for typing in priority data of the logistics goods to be picked; the robot state recognition module (120) is used for recognizing robot state data; the cooperative scheduling module (130) is used for scheduling the subsequent logistics picking work of the robot based on the robot state identification module (120) to obtain an optimal path plan and determine the robot to be worked;
the storage data comprise storage topography, storage aisle and aisle size;
the global path coordination unit (300) comprises a path planning module (310), a cargo size identification module (320), a multi-robot path allocation module (330), a task group allocation module (340) and a path selection module (350);
the path planning module (310) is used for planning a path by using the cooperative scheduling module (130) according to the current position of the robot and the position of the object to be picked, and generating a plurality of possible paths, wherein each path is a sequence formed by a plurality of continuous straight line segments;
the goods size recognition module (320) is used for measuring the size of each object to be picked, recording the length, the width and the height of each object to be picked, and calculating a size vector;
the multi-robot path distribution module (330) is used for grouping all the objects which can be carried according to the maximum size which can be carried by the robot according to the size vector of the goods to be picked, the residual capacity of the robot on the path and the position information of the robot, so as to obtain a plurality of task groups;
the task group allocation module (340) is configured to calculate a width dimension of a straight line segment on each path, respectively perform numerical comparison with maximum dimensions corresponding to a plurality of task groups, select a path with a maximum dimension smaller than the width dimension as a receiving path of the task group, and calculate a time required for each path to reach a destination;
the path selection module (350) is used for comparing the time required by each path to reach the destination calculated by the task allocation module (340) through a numerical comparison technology, and selecting a group of optimal paths to be allocated to the robot on the basis of the optimal principle that the path arrival time is shortest;
the global path cooperation unit (300) further comprises an aisle local dividing module (360), wherein the aisle local dividing module (360) is used for dividing the width capacity of the aisle when detecting that a plurality of robots relatively travel in the same path aisle, so that the dividing capacity of the aisle meets the size vector of the robots, and the robots travel in the matched dividing capacity.
2. The large-scale robotic picking system parameter configuration and co-scheduling platform according to claim 1, wherein: specifically, the state data of the robot state identification module (120) includes electric quantity data, a working state, a working progress, position data and a working time length.
3. The massive robotic picking system parameter configuration and co-scheduling platform according to claim 2, wherein: in the prioritization module (110), prioritization is performed using the following formula:
P = W * D
wherein P represents the priority of logistics goods, W represents the importance degree of the goods, and D represents the distance between the goods and the robot;
the cooperative scheduling module (130) adopts a path planning algorithm, and the expression is as follows:
f(n) = g(n) + h(n)
where f (n) represents the valuation function value of the current node, g (n) represents the cost from the start point to the current node, and h (n) represents the estimated cost from the current node to the end point.
4. A massive robotic picking system parameter configuration and co-scheduling platform according to claim 3, wherein: the aisle local dividing module (360) adopts an aisle local dividing algorithm, and comprises the following steps:
according to the planned number of robots and the robot size vector, the maximum feasible width of the aisle is calculated in advance; when the path selection module (350) determines a path, and a plurality of robots relatively run in the same path aisle, the path aisle width is divided into a plurality of small areas, for each small area, the small areas required to be passed by the robots are calculated according to the current position and the destination of each robot, the robots can only occupy corresponding widths when advancing, and when the robots reach the small areas, a message is sent to other robots, so that the robots know that the areas are occupied, and the method comprises the following gestures:
the small area left in the current aisle does not meet the size vectors of other robots, and the other robots need to wait for the area to be idle and then move;
and the small area left in the current aisle meets the size vectors of other robots, and the other robots travel from the small area left.
5. The massive robotic picking system parameter configuration and co-scheduling platform according to claim 4, wherein: the motion control unit (400) comprises a driving running module (410), a parameter configuration module (420) and a virtual field module (430);
the driving running module (410) is used for controlling the robot to execute running instructions and carrying instructions according to the path determined by the path selection module (350); the parameter configuration module (420) is used for setting corresponding motion parameters for different robots to adapt to different working environments; the virtual field of view module (430) is used to introduce a mixed reality technique, combining the robot field of view with the virtual reality technique.
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