CN113627648A - Task allocation method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a task allocation method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring initial task paths corresponding to a plurality of target objects in a target grid map, wherein the initial task path corresponding to each target object comprises an initial task point of each target object; carrying out smoothing processing on the initial task path corresponding to each target object to obtain a first optimized path corresponding to each target object; determining a task point to be optimized in initial task points of a plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path; performing task optimization on the plurality of target objects based on the task points to be optimized to obtain second optimization paths corresponding to the plurality of target objects, wherein the corresponding second optimization paths comprise the target task points; and controlling each target object to traverse the target task point along the corresponding second optimization path. By the aid of the technical scheme, task allocation can be more reasonable, and accordingly conveying efficiency of warehouse logistics is improved.
Description
Technical Field
The present application relates to the field of path planning technologies, and in particular, to a method, an apparatus, a device, and a storage medium for task allocation.
Background
An AGV (automated Guided vehicle), namely an automatic navigation vehicle, is an important tool for developing and utilizing ground resources and saving labor cost. At present, AGV is often used for storage logistics neighborhood, and AGV can realize article transport and the full automatization of handling process through own automatic handling mechanism and navigation head. In order to realize efficient operation of warehouse logistics, task allocation of multiple AGVs is particularly important. Initial task allocation is based on the distance of the AGVs to the target points, and initially allocates each target point reasonably and accurately to an AGV.
However, the initial task path generated based on the initial task allocation result may have more turns, and the path is not smooth, which affects the smooth driving of the AGV; in addition, the energy reserve of the AGVs is not considered in the initial task allocation, if some AGVs have less energy reserve, the AGVs are allocated to task points that are too far away or too many assigned task points, and the AGVs cannot complete the allocated tasks due to the self energy limitation. Therefore, it is desirable to provide a more scientific and efficient task assignment method.
Disclosure of Invention
The application provides a task allocation method, a device, equipment and a storage medium, which can enable task allocation to be more reasonable and improve the transportation efficiency of warehouse logistics on the basis of improving energy utilization, and the application technical scheme is as follows:
in one aspect, a task allocation method is provided, and the method includes:
acquiring initial task paths corresponding to a plurality of target objects in a target grid map, wherein the initial task path corresponding to each target object comprises an initial task point of each target object;
performing smoothing processing on the initial task path corresponding to each target object to obtain a first optimized path corresponding to each target object;
determining a task point to be optimized in initial task points of the plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path;
performing task optimization on the target objects based on the task point to be optimized to obtain second optimization paths corresponding to the target objects, wherein the second optimization path corresponding to each target object comprises the target task point of each target object;
and controlling each target object to traverse the target task point along the corresponding second optimization path.
In another aspect, a task assigning apparatus is provided, the apparatus including:
the system comprises an initial task path acquisition module, a task path selection module and a task path selection module, wherein the initial task path acquisition module is used for acquiring initial task paths corresponding to a plurality of target objects in a target grid map, and the initial task path corresponding to each target object comprises an initial task point of each target object;
the smoothing module is used for performing smoothing processing on the initial task path corresponding to each target object to obtain a first optimized path corresponding to each target object;
the to-be-optimized task point determining module is used for determining to-be-optimized task points in initial task points of the plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path;
the task optimization module is used for performing task optimization on the target objects based on the task points to be optimized to obtain second optimization paths corresponding to the target objects, wherein the second optimization path corresponding to each target object comprises the target task point of each target object;
and the traversing module is used for controlling each target object to traverse the target task point along the corresponding second optimization path.
In another aspect, a task allocation device is provided, the device includes a processor and a memory, the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the task allocation method as described above.
In another aspect, a computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the task allocation method as described above.
The task allocation method, the device, the equipment and the storage medium have the following technical effects:
by means of the technical scheme, the initial task path is subjected to smoothing processing by eliminating redundant points in the initial task path, smoothness of the path is improved on the basis of shortening the path, and stable running of a target object is guaranteed; and in addition, from the perspective of energy storage, based on the energy storage amount of the target object, the task points needing to be optimized in all the initial task points of the target objects are confirmed, the task points to be optimized are redistributed, on the basis of improving energy utilization, the task distribution is more reasonable, and the transportation efficiency of warehouse logistics is improved.
Drawings
In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart of a task allocation method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a smoothing method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of determining a redundant point in an inflection point of each target object according to an embodiment of the present application;
fig. 4 is a schematic flowchart of determining a task point to be optimized from among initial task points of the plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path according to an embodiment of the present application;
FIG. 5 is a schematic flowchart of obtaining a plurality of second target sub-objects according to an embodiment of the present disclosure;
fig. 6 is a flowchart illustrating task optimization performed on the target objects based on the task point to be optimized to obtain second optimized paths corresponding to the target objects according to the embodiment of the present application;
FIG. 7 is a flowchart illustrating another task allocation method according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a task assigning apparatus according to an embodiment of the present application;
fig. 9 is a hardware block diagram of a server of a task allocation method according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
A task allocation method provided in the embodiment of the present application is described below, and fig. 1 is a schematic flow chart of the task allocation method provided in the embodiment of the present application. It is noted that the present specification provides the method steps as described in the examples or flowcharts, but may include more or less steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system or article of manufacture may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded processing environment) according to the methods described in the examples or figures. Specifically, as shown in fig. 1, the method may include:
s101, acquiring initial task paths corresponding to a plurality of target objects in a target grid map, wherein the initial task path corresponding to each target object comprises an initial task point of each target object.
In the embodiments of the present specification, the target grid map is generally obtained by rasterizing the target planar map. The target plane map may include, but is not limited to, a warehouse plane map, and specifically, the warehouse plane map may include a location of the target cargo, a parking location of a warehouse AGV (Automated Guided Vehicle), and a location of an obstacle.
Specifically, the target objects may be a plurality of warehousing AGVs, the task point may be a position where the target goods are located, and the initial task point may be a position where each target object is allocated to the target goods to be transported. The initial task path may be an initial path generated based on a traversal order of initial task points of the target object, the initial path being composed of path points, which may be represented by a grid in the target grid map.
In a specific embodiment, task allocation of a plurality of target objects and a plurality of task points can be realized based on the biological heuristic neural network after the distance ratio is blended, so that an initial task path is generated. Firstly, task allocation is carried out on a plurality of target objects in a constructed biological heuristic neural network, then according to the distance ratio among task points, task allocation adjustment is carried out on individual task points needing to be reallocated, and path planning is carried out on the target objects with the task points allocated.
In practical applications, a biological enlightened Neural network (GBNN) is an adaptive artificial Neural network and is composed of many interconnected neurons. Each neuron transmits the output neuron activity information to other neurons through the connection weight, and simultaneously receives the activity information transmitted by other neurons.
And S103, smoothing the initial task path corresponding to each target object to obtain a first optimized path corresponding to each target object.
In an embodiment of this specification, as shown in fig. 2, fig. 2 is a schematic flow chart of a smoothing method provided in an embodiment of this application, and specifically, the smoothing the initial task path corresponding to each target object to obtain a first optimized path corresponding to each target object may include:
s201, obtaining an inflection point in the initial task path corresponding to each target object.
Specifically, the inflection point may be a path point that is not collinear with two adjacent path points before and after the corresponding initial task path, and the inflection point does not include the initial task point.
S203, determining a redundant point in the inflection point of each target object.
In a specific embodiment, as shown in fig. 3, the determining the redundant point in the inflection point of each target object may include:
and S301, acquiring the position of the obstacle in the target grid map.
In practical application, the position of the obstacle in the target grid map can be determined based on the position of the obstacle in the warehouse plan map.
And S303, traversing the inflection point of each target object.
S305, based on the initial task path corresponding to each target object, two adjacent inflection points before and after the currently traversed inflection point are determined.
And S307, adding the currently traversed inflection point into the redundant point when the connecting line of the front inflection point and the rear inflection point does not pass through the position of the obstacle.
S205, eliminating the redundant point from the initial task path corresponding to each target object, and generating a first optimized path corresponding to each target object.
Specifically, two adjacent inflection points before and after the redundant point in the corresponding initial task path may be directly connected to obtain the corresponding first optimized path.
It can be seen from the above embodiments that, by eliminating redundant points in the initial task path and smoothing the initial task path, the smoothness of the path can be improved, and the smooth driving of the target object is ensured on the basis of shortening the path.
And S105, determining task points to be optimized in the initial task points of the plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path.
Specifically, the energy reserve amount may be an amount of energy reserved by the target object, and the energy reserve amount may be set based on a maximum amount of energy that can be actually reserved by the target object. Alternatively, the energy types of the target object may include, but are not limited to: and (4) a storage battery.
In some embodiments, the plurality of target objects may include a first target sub-object and a second target sub-object, and specifically, the first target sub-object may be a target object whose energy reserve cannot support a corresponding initial task path, and the second target sub-object may be a target object whose energy reserve can support a corresponding initial task path.
In a specific embodiment, as shown in fig. 4, the determining the task point to be optimized from among the initial task points of the plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path may include:
s401, traversing the plurality of target objects.
And S403, calculating the target energy reserve amount required by the currently traversed target object to reach the last initial task point along the corresponding first optimized path.
S405, judging whether the energy reserve of the currently traversed target object is smaller than the target energy reserve.
And S407, if the determination is yes, taking the currently traversed target object as the first target sub-object.
Specifically, a target object in which the energy reserve amount is smaller than the target energy reserve amount is set as the first target child object.
S409, when traversing the plurality of target objects is finished, obtaining a plurality of first target sub-objects.
And S411, taking an initial task point which cannot be reached when each first target sub-object advances along the corresponding first optimization path based on the energy reserve as the task point to be optimized.
In another specific embodiment, E may be setiIs a target object AiCan support the target object AiLength of advance, target object AiE of (A)iWith the target object AiIs compared with the length of the initial task path to judge the target object AiThe task of all initial task points can be qualified. Suppose a target object AiInitial task path ofi＝{L1，L2，…，LDiIn total, DiA path point, wherein L1And LDiFor starting and end points, using LkRepresenting the k-th waypoint. L | |1-LDi| | represents the target object aiInitial task path ofiLength of (d).
Wherein, if the target object AiE of (A)i≥||L1-LDiIf, then the target object AiAll initial task points assigned to it can be traversed; when the target object AiE of (A)i＜||L1-LDiWhen | l, the target object AiAnd if the target object is limited by the energy reserve and is not enough to traverse all the initial task points allocated to the target object, taking the target object as a first target sub-object, putting the initial task points which cannot be traversed into the task points to be optimized, and waiting for reallocation.
As can be seen from the above embodiments, from the perspective of energy reserves, the task point that needs to be optimized among all the initial task points of the multiple target objects is determined, and the first target sub-object, that is, the target object with insufficient energy reserves, of the multiple target objects is obtained by comparing the energy reserve amount of the target object with the target energy reserve amount, so as to determine the task point to be optimized.
In another specific embodiment, as shown in fig. 5, the method may further include:
s413, if the determination result is no, setting the currently traversed target object as the second target sub-object.
Specifically, a target object in which the energy reserve amount is greater than or equal to the target energy reserve amount is set as the second target sub-object.
S415, when traversing the plurality of target objects is finished, obtaining a plurality of second target sub-objects.
As can be seen from the above embodiments, the second target sub-object, i.e. the target object with sufficient energy reserve, among the plurality of target objects is obtained by comparing the energy reserve amount of the target object with the target energy reserve amount.
And S107, performing task optimization on the plurality of target objects based on the task points to be optimized to obtain second optimized paths corresponding to the plurality of target objects, wherein the second optimized path corresponding to each target object comprises the target task point of each target object.
Specifically, the target task point may be a task point that the target object needs to traverse in the corresponding second optimization path.
In a specific embodiment, as shown in fig. 6, the performing task optimization on the plurality of target objects based on the task point to be optimized to obtain the second optimized paths corresponding to the plurality of target objects may include:
s601, taking the initial task point which does not belong to the task point to be optimized in the initial task points of each first target sub-object as the target task point of each first target sub-object.
Specifically, an initial task point that can be reached when each first target sub-object proceeds along the corresponding first optimized path based on the energy reserve amount may be used as the target task point of each first target sub-object.
And S603, generating a second optimization path corresponding to each first target sub-object based on the target task point of each first target sub-object.
Specifically, a traversal order of the target task points is obtained, and based on the traversal order, a path between adjacent target task points in the target task points is determined, so that a corresponding second optimized path is generated.
S605, performing task allocation on the plurality of second target sub-objects based on the task point to be optimized, and determining a current task point of each second target sub-object.
In a particular embodiment, the task assignment may be performed for a plurality of second target sub-objects based on a bio-heuristic neural network.
Specifically, it is assumed that there are G task points to be optimized in the set of task points to be optimized. And re-assigning the plurality of second target sub-objects to the G task points to be optimized by using the biological heuristic neural network according to the current positions of the plurality of second target sub-objects. And when a second target sub-object wins competing a task point to be optimized, the second target sub-object is redistributed to the task point to be optimized, so that the current task point of each second target sub-object which is redistributed is determined.
S607, determining the target task point of each second target sub-object based on the initial task point and the current task point of each second target sub-object.
Specifically, the target task point of each target sub-object is obtained based on the initial task point initially allocated to each second target sub-object and the current task point newly allocated.
And S609, generating a second optimization path corresponding to each second target sub-object based on the target task point of each second target sub-object.
Specifically, the traversal order of the target task points of the second target sub-object is determined based on the biological heuristic neural network, so that a path between adjacent target task points in the target task points is determined, and a corresponding second optimized path is generated.
It can be seen from the above embodiments that the task points to be optimized of the first target sub-object with less energy reserve are reallocated to the second target sub-object with more energy reserve, so that the task allocation is more reasonable and the transportation efficiency of the warehouse logistics is improved on the basis of improving the energy utilization.
In an optional embodiment, as shown in fig. 7, after performing task optimization on the plurality of target objects based on the task point to be optimized to obtain second optimization paths corresponding to the plurality of target objects, the method may further include:
s111, updating the plurality of target objects based on the plurality of second target sub-objects.
And S113, updating the first optimization path corresponding to each update target object based on the second optimization path corresponding to each update target object.
And S115, based on the energy reserve amount of each update target object and the corresponding update first optimization path, repeatedly executing the energy reserve amount of each update target object and the corresponding first optimization path, and determining a task point to be optimized in the initial task points of the plurality of target objects to perform task optimization on the plurality of target objects based on the task point to be optimized to obtain a second optimization path corresponding to the plurality of target objects until the number of the current task points to be optimized is zero or the number of the current second target sub-objects is zero.
Specifically, whether the energy reserve of each second target sub-object can support a corresponding second optimization path is judged, when the energy reserve of the second target sub-object cannot support the corresponding second optimization path exists, the task points to be optimized and the second target sub-objects are updated, and task optimization is performed on the updated second target sub-objects based on the updated task points to be optimized until the number of the current task points to be optimized is zero, that is, all the task points in the target grid map are optimized and completed;
or, until the number of the current second target sub-objects is zero, that is, some task points are far away from all the target objects, there is no current second target sub-object that can reach the task points, and the task points are marked as tasks that cannot be completed, thereby ending the task allocation.
It can be seen from the above embodiments that, based on the energy reserve of the second target sub-object, it is determined whether the second target sub-object can support the corresponding second optimized path, that is, whether all target task points can be traversed, and then task allocation is performed again on the target task points that the second target sub-object cannot reach, so as to ensure that the task points in the target grid map can be reasonably allocated to the target object as far as possible, and improve the reasonability of task allocation, thereby improving the transportation efficiency of warehouse logistics.
And S109, controlling each target object to traverse the target task point along the corresponding second optimization path.
In practical application, the entity AGV trolley of each target object can be controlled to traverse the target task points along the corresponding second optimization paths so as to complete the logistics transportation task corresponding to the target task points.
As can be seen from the foregoing embodiments of the present specification, according to the technical solution provided by the embodiments of the present specification, the initial task path is smoothed by eliminating redundant points in the initial task path, and on the basis of shortening the path, the smoothness of the path is improved, and the stable driving of the target object is ensured; and in consideration of energy storage, task points needing to be optimized in all initial task points of a plurality of target objects are confirmed based on the energy storage amount of the target objects, the task points to be optimized of the first target sub-object with less energy storage amount are redistributed to the second target sub-object with more energy storage amount, on the basis of improving energy utilization, the task allocation is more reasonable, and the transportation efficiency of warehouse logistics is improved.
An embodiment of the present application provides a task allocation apparatus, as shown in fig. 8, where the apparatus includes:
an initial task path obtaining module 810, configured to obtain initial task paths corresponding to multiple target objects in a target grid map, where an initial task path corresponding to each target object includes an initial task point of each target object;
a smoothing module 820, configured to perform smoothing on the initial task path corresponding to each target object to obtain a first optimized path corresponding to each target object;
a task point to be optimized determining module 830, configured to determine a task point to be optimized in initial task points of the multiple target objects based on the energy reserve of each target object and the corresponding first optimization path;
a task optimization module 840, configured to perform task optimization on the multiple target objects based on the task point to be optimized to obtain second optimization paths corresponding to the multiple target objects, where the second optimization path corresponding to each target object includes the target task point of each target object;
and a traversing module 850, configured to control each target object to traverse the target task point along the corresponding second optimized path.
In this embodiment, the smoothing module 820 may include:
an inflection point obtaining unit, configured to obtain an inflection point in an initial task path corresponding to each target object;
a redundant point determining unit for determining a redundant point in the inflection point of each target object;
and a redundant point eliminating unit, configured to eliminate the redundant point from the initial task path corresponding to each target object, and generate a first optimized path corresponding to each target object.
In a specific embodiment, the redundancy point determining unit may include:
the obstacle position acquisition unit is used for acquiring the position of an obstacle in the target grid map;
an inflection point traversing unit, configured to traverse an inflection point of each target object;
an adjacent inflection point determining unit, configured to determine, based on the initial task path corresponding to each target object, two adjacent inflection points before and after a currently traversed inflection point;
and the redundant point unit is used for adding the currently traversed inflection point into the redundant point when the connecting line of the front inflection point and the rear inflection point does not pass through the position of the obstacle.
In a specific embodiment, the to-be-optimized task point determining module 830 may include:
a target object traversing unit for traversing the plurality of target objects;
the target energy reserve calculation unit is used for calculating the target energy reserve required by the currently traversed target object to reach the last initial task point along the corresponding first optimized path;
a judging unit, configured to judge whether the energy reserve of the currently traversed target object is smaller than the target energy reserve;
a first target sub-object determining unit, configured to, when the determination result is yes, take the currently traversed target object as the first target sub-object;
the first target sub-object acquisition unit is used for obtaining a plurality of first target sub-objects when traversing the plurality of target objects is finished;
and the to-be-optimized task point determining unit is used for taking an initial task point which cannot be reached when each first target sub-object advances along the corresponding first optimization path based on the energy reserve as the to-be-optimized task point.
In another specific embodiment, the apparatus may include:
a second target sub-object determining unit, configured to, when determining that the target object is a second target sub-object, take the currently traversed target object as the second target sub-object;
and the second target sub-object acquisition unit is used for acquiring a plurality of second target sub-objects when traversing the plurality of target objects is finished.
In a specific embodiment, the task optimization module 840 may include:
a first target task point determining unit, configured to use an initial task point, which does not belong to the task point to be optimized, in the initial task points of each first target sub-object as a target task point of each first target sub-object;
a first target sub-object optimization path generating unit, configured to generate a second optimization path corresponding to each first target sub-object based on the target task point of each first target sub-object;
a current task point determining unit, configured to perform task allocation on the plurality of second target sub-objects based on the task point to be optimized, and determine a current task point of each second target sub-object;
a second target task point determining unit, configured to determine a target task point of each second target sub-object based on the initial task point and the current task point of each second target sub-object;
and the second target sub-object optimization path generating unit is used for generating a second optimization path corresponding to each second target sub-object based on the target task point of each second target sub-object.
In an optional embodiment, the apparatus may further include:
a target object updating unit configured to update the plurality of target objects based on the plurality of second target sub-objects;
a first optimization path updating unit, configured to update a first optimization path corresponding to each update target object based on a second optimization path corresponding to each update target object;
and a repeated execution unit, configured to repeatedly execute the energy reserve amount and the corresponding first optimization path based on each update target object, and determine a task point to be optimized in the initial task points of the multiple target objects to perform task optimization on the multiple target objects based on the task point to be optimized, so as to obtain a second optimization path corresponding to the multiple target objects, until the number of the current task points to be optimized is zero or the number of the current second target sub-objects is zero.
The embodiment of the present application provides a task allocation apparatus, which includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the task allocation method provided in the above method embodiment.
The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to the use of the above-described apparatus, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, a server, or a similar computing device, that is, the computer device may include a mobile terminal, a computer terminal, a server, or a similar computing device. Taking an example of the application running on a server, fig. 9 is a hardware structure block diagram of the server of the task allocation method provided in the embodiment of the present application. As shown in fig. 9, the server 900 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 910 (the processor 910 may include but is not limited to a Processing device such as a micro processor MCU or a programmable logic device FPGA), a memory 930 for storing data, and one or more storage media 920 (e.g., one or more mass storage devices) for storing applications 923 or data 922. Memory 930 and storage media 920 may be, among other things, transient or persistent storage. The program stored in the storage medium 920 may include one or more modules, each of which may include a series of instruction operations in a server. Still further, the central processor 910 may be configured to communicate with the storage medium 920, and execute a series of instruction operations in the storage medium 920 on the server 900. The server 900 may also include one or more power supplies 960, one or more wired or wireless network interfaces 950, one or more input-output interfaces 940, and/or one or more operating systems 921, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and so forth.
The input/output interface 940 may be used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the server 900. In one example, the input/output Interface 940 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the input/output interface 940 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
It will be understood by those skilled in the art that the structure shown in fig. 9 is merely illustrative and is not intended to limit the structure of the electronic device. For example, server 900 may also include more or fewer components than shown in FIG. 9, or have a different configuration than shown in FIG. 9.
The present application further provides a storage medium, where the storage medium may be disposed in a server to store at least one instruction or at least one program for implementing a task allocation method in one of the method embodiments, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the task allocation method provided in the method embodiment.
Alternatively, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, which can store program codes.
As can be seen from the embodiments of the task allocation method, device, equipment, or storage medium provided by the present application, by eliminating redundant points in the initial task path by using the technical solution provided by the present application, the initial task path is smoothed, and on the basis of shortening the path, the smoothness of the path is improved, and the stable driving of the target object is ensured; and in consideration of energy storage, task points needing to be optimized in all initial task points of a plurality of target objects are confirmed based on the energy storage amount of the target objects, and task points to be optimized of a first target sub-object with less energy storage amount are redistributed to a second target sub-object with more energy storage amount, so that the task allocation is more reasonable on the basis of improving the energy utilization, and the transportation efficiency of warehouse logistics is improved.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and portions that are similar to each other in the embodiments are referred to each other, and each embodiment focuses on differences from other embodiments. In particular, for the apparatus, device and storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.
It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be performed by hardware, or may be performed by a program to instruct relevant hardware to perform the steps, where the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk, an optical disk, or the like.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method of task allocation, the method comprising:
acquiring initial task paths corresponding to a plurality of target objects in a target grid map, wherein the initial task path corresponding to each target object comprises an initial task point of each target object;
performing smoothing processing on the initial task path corresponding to each target object to obtain a first optimized path corresponding to each target object;
determining a task point to be optimized in initial task points of the plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path;
performing task optimization on the target objects based on the task point to be optimized to obtain second optimization paths corresponding to the target objects, wherein the second optimization path corresponding to each target object comprises the target task point of each target object;
and controlling each target object to traverse the target task point along the corresponding second optimization path.
2. The method according to claim 1, wherein the smoothing the initial task path corresponding to each target object to obtain the first optimized path corresponding to each target object comprises:
acquiring an inflection point in an initial task path corresponding to each target object, wherein the inflection point is a path point which is not collinear with two adjacent path points in the corresponding initial task path;
determining a redundant point in the inflection point of each target object;
and eliminating the redundant points from the initial task path corresponding to each target object, and generating a first optimized path corresponding to each target object.
3. The method of claim 2, wherein the determining redundant points in the inflection points of each target object comprises:
acquiring the position of an obstacle in the target grid map;
traversing the inflection point of each target object;
determining two adjacent inflection points before and after the currently traversed inflection point based on the initial task path corresponding to each target object;
and when the connecting line of the front and the back two adjacent inflection points does not pass through the position of the obstacle, adding the currently traversed inflection point into the redundant point.
4. The method of claim 1, wherein the plurality of target objects comprises a first target sub-object, and wherein determining the task point to be optimized from among the initial task points of the plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path comprises:
traversing the plurality of target objects;
calculating the target energy reserve amount required by the currently traversed target object to reach the last initial task point along the corresponding first optimization path;
judging whether the energy reserve of the currently traversed target object is smaller than the target energy reserve;
when the judgment result is yes, taking the currently traversed target object as the first target sub-object;
obtaining a plurality of first target sub-objects when traversing the plurality of target objects is finished;
and taking an initial task point which cannot be reached when each first target sub-object advances along the corresponding first optimization path based on the energy reserve as the task point to be optimized.
5. The method of claim 4, wherein the plurality of target objects further comprises a second target sub-object, the method further comprising:
when the judgment result is negative, taking the currently traversed target object as the second target sub-object;
and obtaining a plurality of second target sub-objects when traversing the plurality of target objects is finished.
6. The method according to claim 5, wherein the performing task optimization on the plurality of target objects based on the task point to be optimized to obtain second optimized paths corresponding to the plurality of target objects comprises:
taking the initial task point which does not belong to the task point to be optimized in the initial task points of each first target sub-object as the target task point of each first target sub-object;
generating a second optimization path corresponding to each first target sub-object based on the target task point of each first target sub-object;
performing task allocation on the plurality of second target sub-objects based on the task point to be optimized, and determining the current task point of each second target sub-object;
determining a target task point of each second target sub-object based on the initial task point and the current task point of each second target sub-object;
and generating a second optimization path corresponding to each second target sub-object based on the target task point of each second target sub-object.
7. The method according to claim 6, wherein after the task optimizing the target objects based on the task point to be optimized to obtain second optimized paths corresponding to the target objects, the method further comprises:
updating the plurality of target objects based on the plurality of second target sub-objects;
updating the first optimization path corresponding to each updating target object based on the second optimization path corresponding to each updating target object;
and based on the energy reserve amount of each updated target object and the corresponding updated first optimization path, repeatedly executing the energy reserve amount based on each target object and the corresponding first optimization path, and determining a task point to be optimized in the initial task points of the plurality of target objects to perform task optimization on the plurality of target objects based on the task point to be optimized to obtain a second optimization path corresponding to the plurality of target objects until the number of the current task points to be optimized is zero or the number of the current second target sub-objects is zero.
8. A task assigning apparatus, characterized in that the apparatus comprises:
the system comprises an initial task path acquisition module, a task path selection module and a task path selection module, wherein the initial task path acquisition module is used for acquiring initial task paths corresponding to a plurality of target objects in a target grid map, and the initial task path corresponding to each target object comprises an initial task point of each target object;
the smoothing module is used for smoothing the initial task path corresponding to each target object to obtain a first optimized path corresponding to each target object;
the to-be-optimized task point determining module is used for determining to-be-optimized task points in initial task points of the plurality of target objects based on the energy reserve of each target object and the corresponding first optimization path;
the task optimization module is used for performing task optimization on the target objects based on the task points to be optimized to obtain second optimization paths corresponding to the target objects, wherein the second optimization path corresponding to each target object comprises the target task point of each target object;
and the traversing module is used for controlling each target object to traverse the target task point along the corresponding second optimization path.
9. A task assigning device comprising a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the task assigning method according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which at least one instruction or at least one program is stored, which is loaded and executed by a processor to implement the task assigning method according to any one of claims 1 to 7.
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