CN115933664A - Dispatching method and system of logistics robot - Google Patents
Dispatching method and system of logistics robot Download PDFInfo
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- CN115933664A CN115933664A CN202211604155.7A CN202211604155A CN115933664A CN 115933664 A CN115933664 A CN 115933664A CN 202211604155 A CN202211604155 A CN 202211604155A CN 115933664 A CN115933664 A CN 115933664A
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Abstract
The invention provides a dispatching method of a logistics robot, a dispatching system of the logistics robot and a corresponding computer readable storage medium. The dispatching method of the logistics robot comprises the following steps: determining an environment model related to the logistics robot and the goods according to the initial parking position of the logistics robot and the position of the goods to be carried, and determining a target function of logistics environment task scheduling; determining the permutation code and the initialization solution set of the task node of at least one point to be carried according to the environment model and the objective function of the logistics environment task scheduling; determining a fitness evaluation function according to a target function and a permutation code of the logistics environment task scheduling; and determining a dispatching scheme of the logistics robot according to the fitness evaluation function, the environment model and the permutation code.
Description
Technical Field
The present invention relates to the field of logistics robot scheduling, and in particular, to a method and a system for scheduling a logistics robot and a computer-readable storage medium.
Background
The warehouse logistics robot task scheduling refers to an optimization problem that on the premise that safety and robot dynamics constraints are met, tasks are reasonably distributed, and the minimum total moving distance of multiple robots is used as an optimization target. The traditional warehouse logistics system mainly depends on a manual carrying mode to finish the warehousing and ex-warehouse work of goods, but the method has large demand for workers and low efficiency, and the situations of overstock and loss of goods and the like are easy to occur. Although many enterprises at home and abroad have started to put the robot into the warehouse logistics environment, the whole robot is still in the development and starting stage, and the popularization rate is not high. In the actual work, the efficient operation of the warehouse logistics system is ensured, a plurality of carrying tasks are reasonably distributed to each robot, and goods can be safely carried to a specified place. In the prior art in the field, a dispatching scheme of the logistics robot cannot be efficiently and accurately formulated.
In order to overcome the above defects in the prior art, there is a need in the art for a scheduling method for a logistics robot, which is used for solving the problem of task scheduling of multiple mobile robots in a warehouse logistics environment, and can quickly converge to obtain an optimal solution, so as to implement reasonable task scheduling of multiple robots, thereby implementing accuracy and high efficiency of the logistics robot scheduling.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
In order to overcome the defects in the prior art, the invention provides a dispatching method of a logistics robot, a dispatching system of the logistics robot and a corresponding computer readable storage medium, which can solve the problem of task dispatching of multiple mobile robots in a warehouse logistics environment, can quickly converge to obtain an optimal solution, realizes reasonable task dispatching of multiple robots, and further realizes the accuracy and high efficiency of the dispatching of the logistics robot.
Specifically, the method for scheduling a logistics robot according to the first aspect of the present invention may include the following steps: determining an environment model related to the logistics robot and the goods according to the initial parking position of the logistics robot and the position of the goods to be carried, and determining a target function of logistics environment task scheduling; determining the permutation code and the initialization solution set of the task node of at least one point to be carried according to the environment model and the objective function of the logistics environment task scheduling; determining a fitness evaluation function according to the objective function of the logistics environment task scheduling and the permutation code; and determining a dispatching scheme of the logistics robot according to the fitness evaluation function, the environment model and the arrangement code.
Further, in some embodiments of the present invention, the determining a scheduling scheme of the logistics robot according to the fitness evaluating function, the environment model and the ranking code includes: determining a first population according to the fitness evaluation function and the environment model; carrying out heredity, crossover and mutation on the individual sequences of the first population according to a crossover operator and a mutation operator; judging the fitness value and the preset threshold value of each individual of the varied individual sequences of the first population; and determining a dispatching scheme of the logistics robot according to the judgment result.
Further, in some embodiments of the present invention, the step of determining a scheduling scheme of the logistics robot according to the determination result includes: responding to the result that the fitness value of at least one individual of the varied individual sequences of the first population is larger than a preset threshold value, and selecting the individual with the largest fitness value as a dispatching scheme of the logistics robot; and in response to the result that the fitness values of the individuals of the inherited, crossed and mutated individual sequences of the first population are smaller than the preset threshold, executing the steps of determining the first population, performing inheritance, crossing and mutation, judging the fitness values of the individuals of the mutated individual sequences of the first population and the preset threshold again, and determining the dispatching scheme of the logistics robot according to the judgment result.
Further, in some embodiments of the present invention, the step of determining the first population according to the fitness evaluating function and the environment model includes: determining the fitness value of the current population according to the fitness evaluation function and the environment model; determining the fitness value of each individual in the current population according to the fitness evaluation function; determining the probability of each individual in the next generation population according to the fitness value of each individual in the current population; and determining the first population according to the probability of each individual in the offspring population.
Further, in some embodiments of the present invention, the step of inheriting, intersecting and mutating the individual sequences of the first population according to an intersection operator and a mutation operator comprises: copying and transmitting the gene sequence with high fitness in the first population to a next generation population; for individuals with low fitness in the first population, selecting two segments of gene segments with the same quantity from random positions in a gene sequence formed by task nodes of all points to be carried in the current population, and crossing the selected gene segments; determining the variation rate according to a preset variation factor, the current iteration times and a preset maximum iteration time; and performing variation on the inherited and crossed first population according to the variation rate.
Further, in some embodiments of the present invention, the determining an environment model about the logistics robot and the cargo and an objective function of the logistics environment task scheduling according to the initial parking position of the logistics robot and the position of the cargo to be carried includes: regarding the logistics robot and the goods to be carried as particles, and performing topological graph modeling according to the initial parking position of the logistics robot and the position of the goods to be carried to determine the environment model; determining a distance target function of the minimum total path length of the movement of each logistics robot according to the initial stop positions of the logistics robots; determining a load balancing objective function of each logistics robot according to the load of the plurality of logistics robots; and determining an objective function of the logistics environment task scheduling according to a distance objective function of the total moving path length of each logistics robot and a load balancing function of each logistics robot.
Further, in some embodiments of the present invention, the constraints of the objective function of the logistics environment task scheduling include, but are not limited to, one or more of constraints on robot movement speed and constraints on robot minimum and maximum load capacity.
Further, in some embodiments of the present invention, the step of determining a permutation code of task nodes of at least one point to be handled and initializing a solution set according to the environment model and the objective function of the logistics environment task scheduling includes: and coding the initial population according to a two-section coding mode so as to determine the permutation codes and the initialization solution sets of the task nodes of the points to be carried.
Further, a scheduling system of a logistics robot provided according to a second aspect of the present invention includes: a memory; and the processor is connected with the memory and is configured to implement the dispatching method of the logistics robot.
Further, according to a third aspect of the present invention, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement a scheduling method of determining a logistics robot.
Drawings
The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale, and components having similar relative characteristics or features may have the same or similar reference numerals.
Fig. 1 illustrates an architecture diagram of an apparatus of a scheduling system of a logistics robot provided in accordance with some embodiments of the present invention.
Fig. 2 illustrates a flow chart of a scheduling method of a logistics robot provided in accordance with some embodiments of the present invention.
FIG. 3 illustrates a task allocation result graph provided in accordance with some embodiments of the invention.
FIG. 4 illustrates a graph of cost variation in an iterative process provided in accordance with some embodiments of the present invention.
Detailed Description
The following description is given by way of example of the present invention and other advantages and features of the present invention will become apparent to those skilled in the art from the following detailed description. While the invention will be described in connection with the preferred embodiments, there is no intent to limit its features to those embodiments. On the contrary, the invention has been described in connection with the embodiments for the purpose of covering alternatives or modifications as may be extended based on the claims of the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be practiced without these particulars. Moreover, some of the specific details have been omitted from the description in order not to obscure or obscure the focus of the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Also, the terms "upper," "lower," "left," "right," "top," "bottom," "horizontal," "vertical" and the like used in the following description shall be understood to refer to the orientation as it is drawn in this section and the associated drawings. The relative terms are used for convenience of description only and do not imply that the described apparatus should be constructed or operated in a particular orientation and therefore should not be construed as limiting the invention.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, regions, layers and/or sections, these elements, regions, layers and/or sections should not be limited by these terms, but rather are used to distinguish one element, region, layer and/or section from another element, region, layer and/or section. Thus, a first component, region, layer or section discussed below could be termed a second component, region, layer or section without departing from some embodiments of the present invention.
As described above, warehouse logistics robot task scheduling refers to an optimization problem that tasks are reasonably distributed on the premise that safety and robot dynamics constraints are met, and the minimum total moving distance of multiple robots is taken as an optimization target. The traditional warehouse logistics system mainly depends on a manual carrying mode to finish the warehousing and ex-warehouse work of goods, but the method has large demand for workers and low efficiency, and the situations of overstock and loss of goods and the like are easy to occur. Although many enterprises at home and abroad have started to put the robot into the warehouse logistics environment, the whole robot is still in the development and starting stage, and the popularization rate is not high. In actual work, the efficient operation of the warehouse logistics system is ensured, a plurality of carrying tasks are reasonably distributed to each robot, and goods can be safely carried to a specified place. In the prior art in the field, a dispatching scheme of the logistics robot cannot be efficiently and accurately formulated.
In order to overcome the defects in the prior art, the invention provides a dispatching method of a logistics robot, a dispatching system of the logistics robot and a corresponding computer readable storage medium, which can solve the problem of task dispatching of multiple mobile robots in a warehouse logistics environment, can quickly converge to obtain an optimal solution, realize reasonable task dispatching of multiple robots and further realize the accuracy and high efficiency of the dispatching of the logistics robot.
In some non-limiting embodiments, the dispatching method of the logistics robot provided by the first aspect of the invention can be implemented via the dispatching system of the logistics robot provided by the second aspect of the invention. Specifically, the dispatching system of the logistics robot is provided with a memory and a processor. The memory includes, but is not limited to, the above-described computer-readable storage medium provided by the third aspect of the invention having computer instructions stored thereon. The processor is connected with the memory and configured to execute the computer instructions stored on the memory to implement a dispatching method of the logistics robot provided by the first aspect of the invention.
Referring first to fig. 1, fig. 1 illustrates an architecture diagram of an apparatus for determining hand motions of a virtual human provided according to some embodiments of the present invention.
As shown in fig. 1, the scheduling system of the logistics robot provided by the second aspect of the invention can include a communication module 105, a memory, and a processor 102. The memory may include an internal communication bus 101, a processor (processor) 102, a Read Only Memory (ROM) 103, a Random Access Memory (RAM) 104, a communication port 105, and a hard disk 107. The internal communication bus 101 may implement data communication of a scheduling system of the logistics robot. The processor 102 may make the determination and issue the prompt. In some embodiments, processing
The scheduling system of the streaming robot can send and receive information and data from the network through the communication port 105. In some embodiments, the dispatching system of the logistics robot can transmit and communicate data with external input/output devices through the input/output end 106 in a wired manner. The means for determining the hand movements of the avatar may also comprise different forms of program storage units and data storage units, such as a hard disk 107, a0 Read Only Memory (ROM) 103 and a Random Access Memory (RAM) 104, capable of storing computer processing and/or data
Various data files used for communication, and possibly program instructions executed by processor 102. The processor 102 executes these instructions to carry out the main parts of the method. The results of the processing by the processor 102 are communicated to an external output device via the communication port 105 for display on a user interface of the output device.
The working principle of the dispatching system of the logistics robot 5 will be described in the following with reference to some embodiments of the dispatching method of the logistics robot. It will be appreciated by those skilled in the art that these method embodiments are merely examples
The present invention provides some non-limiting embodiments, which are intended to clearly show the main concepts of the present invention and provide some specific solutions for the public to implement, rather than limiting the whole function or the whole working mode of the dispatching method of the logistics robot. Likewise, the dispatching system of the logistics robot is only the invention
The provided non-limiting embodiment does not limit the execution subject of 0 in each step in the dispatching method of the logistics robots.
Please refer to fig. 2. Fig. 2 illustrates a flow chart of a scheduling method of a logistics robot provided in accordance with some embodiments of the present invention.
As shown in fig. 2, the dispatching method of the logistics robot can be firstly based on the initial of the logistics robot
A parking position and a position of the cargo to be carried, an environment 5 model of the logistics robot and the cargo, and an objective function of logistics environment task scheduling. The scheduling method may then be according to
And determining the permutation code and the initialization solution set of the task node of at least one point to be carried by the environment model and the objective function of the logistics environment task scheduling. After determining the permutation code and initializing the solution set, the method may determine a fitness evaluation function according to an objective function of the logistics environment task scheduling and the permutation code. After determining the fitness evaluating function, the method may determine a scheduling scheme of the logistics robot according to the fitness evaluating function, the environment model, and the permutation code.
Further, in some embodiments of the present invention, the logistics robot scheduling method may regard both the logistics robot and the cargo to be transported as particles, and perform topological modeling according to an initial parking position of the logistics robot and a position of the cargo to be transported to determine the environment model. Then, the method may determine a distance objective function of each of the logistics robots moving for a minimum total path length according to initial stop positions of a plurality of the logistics robots. After determining the objective function, the method may determine a load balancing objective function of each of the logistics robots according to loads of a plurality of the logistics robots. After determining the load balancing objective function, the method may determine the objective function of the logistics environment task scheduling according to a distance objective function of a total moving path length of each of the logistics robots and the load balancing function of each of the logistics robots.
Specifically, the method includes the steps of firstly constructing a warehouse logistics environment task scheduling model, regarding goods to be transported and logistics robots as mass points, determining the initial stop positions of the idle logistics robots and the positions of the goods to be transported, and performing environment modeling by using a topological graph method.
It will be appreciated by those skilled in the art that the above-described environment and massage using topological techniques is only one non-limiting embodiment provided by the present invention, and is intended to model the above-described particles and provide some specific solutions for facilitating implementation by the public, and is not intended to limit the scope of the present invention.
Further, after environment modeling is performed, the method can abstract the warehouse logistics task into how to utilize the existing resources, so that benefits are maximized while requirements are met, and cost expenditure is minimized. Then, the method can establish a warehouse logistics task scheduling model. The model can take the minimum total path length of all robots and the load balance degree of the robots as targets, simultaneously satisfy the dynamics constraint of the robots and the task allocation constraint, and establish an objective function. The method can take the minimum total path length of the robot movement and the load balance degree of the robot as targets, and the constructed target function is as follows:
F＝k 1 *P+k 2 *L
where P denotes a minimum total path length for robot movement, L denotes a degree of robot load balancing, and k denotes 1 And k 2 And respectively representing corresponding cost balance coefficients, and can be artificially set for different scenes.
The minimum total path length objective function of the robot movement is as follows:
wherein P is i Representing the total distance the robot i moves in a single task, the total distance the robot moves in the respective tasks is represented as:
herein, c is i jk Representing the distance cost of the robot i moving from the task node j to the task node k; x is a radical of a fluorine atom i jk Whether the mobile terminal needs to move from the task node j to the task node k or not is represented, the value is 1 to represent the need, and otherwise, the value is 0; n represents the number of all task nodes. The robot load balancing objective function can be expressed as
Here, L represents a robot load balance degree; m represents the total number of robots; y is i j And indicating that the task node j is completed by the ith robot, wherein the task j is executed by the robot i when the value is 1, and the task j is not executed by the robot i when the value is 0.
Further, in some embodiments of the present invention, after determining the objective function of the logistics environment task scheduling, the method may encode the initial population according to a two-stage encoding method to determine a permutation code and an initialization solution set of task nodes of each of the to-be-handled points. Specifically, the method can encode the initial population by using a two-section encoding mode, the chromosome is divided into two sections, the first section represents that each task node to be carried is randomly arranged, the length of the first section is N, the number of each gene is consistent with the number of the task node to be carried, the second section of the chromosome represents the load capacity of each robot, and the length of the second section of the chromosome is M. And all the task nodes to be carried and the distributed robots are randomly combined and arranged.
Further, after determining the permutation code of the task nodes of at least one to-be-carried point and initializing the solution set, the method can calculate the fitness evaluation function to calculate the fitness value of the current population as an important standard for screening offspring. Here, the fitness evaluation function needs to meet requirements of universality, nonnegativity, consistency and the like, and according to the characteristics of task allocation of multiple robots, it needs to be ensured that individuals meeting target requirements are reserved in an iteration process, and the larger the evaluation value is, the more reasonable the task allocation is, the more reasonable the individual fitness evaluation function is designed as follows:
further, after determining the fitness evaluation function, the method may determine a scheduling scheme of the logistics robot according to the fitness evaluation function, the environment model, and the permutation code.
Further, in some embodiments of the present invention, the method may determine the first population according to the fitness evaluation function and the environment model. Then, the method can perform heredity, crossover and mutation on the individual sequences of the first population according to a crossover operator and a mutation operator. Then, the method can determine the fitness value of each individual of the mutated individual sequences of the first population and the size of a preset threshold. And then, the method can determine the dispatching scheme of the logistics robot according to the judgment result.
Specifically, the method can firstly determine a crossover operator and a mutation operator to crossover and mutate the sequences of the population individuals so as to ensure the diversity of the offspring population individuals. The method can copy and transmit the gene sequence with high fitness value in the population to the next generation, select two segments of gene segments with the same quantity from random positions for the gene sequence consisting of task points to be carried in the current individual by the individual with low fitness value, exchange the selected gene segments, and exchange the gene sequences. Meanwhile, in order to further increase the diversity and local search capability of the solution, the gene sequences of each individual in the population will have variation with a certain variation rate, and the variation rate is as follows:
where ξ is the variation factor, K is the current iteration number, and K is the maximum iteration number.
And forming a new generation of population through individuals generated by heredity, crossing and variation, and substituting the new generation of population into the objective function to calculate the fitness until the individual with the highest fitness appears in the population and is the optimal value to be found.
In the following, the objective function and the constraint are expressed as follows:
v i min ≤v i ≤v i max
l i min ≤l i ≤l i max
further, in some embodiments of the present invention, when the fitness value of at least one individual of the mutated individual sequence of the first population is greater than a preset threshold, the method may select the individual with the largest fitness value as the scheduling scheme of the logistics robot. When the fitness values of the individuals of the inherited, intersected and mutated individual sequences of the first population are all smaller than the preset threshold, the method may further perform the steps of determining the first population, performing the inheritance, intersection and mutation, determining the fitness values of the individuals of the mutated individual sequences of the first population and the preset threshold, and determining the scheduling scheme of the logistics robot according to the determination result. In other words, the method iterates until the fitness value of the sequence of individuals of the first population is greater than a predetermined threshold. Therefore, the method can solve the problem of task scheduling of the multiple mobile robots in the warehouse logistics environment, can quickly converge to obtain an optimal solution, and realizes reasonable task scheduling of the multiple robots, thereby realizing the accuracy and high efficiency of the logistics robot scheduling.
Further, in some embodiments of the present invention, the method may determine the fitness value of the current population according to the fitness evaluating function and the environment model. Then, the method may determine the fitness value of each individual in the current population according to the fitness evaluation function. Thereafter, the method can determine the probability of each of the individuals in the offspring population. After determining the probabilities, the method can determine the first population based on the probability of each of the individuals in the offspring population.
Specifically, the method can determine the probability of the fitness evaluation function appearing in the offspring population according to the individual fitness value after determining the fitness evaluation function, and select the reserved individuals according to the probability, so as to continuously obtain a more excellent solution. The method can also determine the probability of occurrence in the offspring according to the fitness value of the individual, continuously obtain more excellent offspring according to the probability, select according to the individual fitness value, reserve the individual with larger fitness value, and design the selection probability of the individual fitness as follows:
here, p represents the proportion of fitness of individual i to the total fitness of all individuals in the population, and f represents the proportion of fitness of individual i to the total fitness of all individuals in the population i The fitness of the ith individual in the population is shown, and m represents the population number of the population.
Further, in some embodiments of the present invention, the method may replicate and transmit the high fitness gene sequences of the first population to the next generation population. Then, the method can select two segments of gene segments with the same quantity from random positions in the gene sequence formed by the task nodes of each point to be carried in the current population for the individuals with low fitness in the first population, and cross the selected gene segments. Then, the method can determine the variation rate according to the preset variation factor, the current iteration times and the preset maximum iteration times. Then, the method can perform mutation on the first population after inheritance and crossing according to the mutation rate.
Further, in some embodiments of the present invention, the constraint conditions of the objective function of the logistics environment task scheduling include, but are not limited to, one or more of constraints on the moving speed of the robot and constraints on the minimum and maximum load capacities of the robot.
Specifically, the state of the robot satisfying the self-constraint condition is as follows:
v i min ≤v i ≤v i max
l u nib ≤l i ≤l i nax
here, v i Indicating the velocity, v, of the robot i i min And v i max Respectively represent the upper limit and the lower limit of the moving speed of the ith robot, l i Indicates the load capacity of the ith robot, l i min And l i max Respectively representing the minimum load capacity and the maximum load capacity of the ith robot. y is i j And the task node j is represented to be completed by the ith robot, and the same task node has only one robot.
Please refer to fig. 3 and fig. 4. FIG. 3 illustrates a task allocation result graph provided in accordance with some embodiments of the invention. FIG. 4 illustrates a graph of cost variation in an iterative process provided in accordance with some embodiments of the present invention.
As shown in fig. 3, a scheduling plan diagram of the logistics robot can be obtained after the path planning. The method can clearly, accurately and efficiently plan the moving path of the logistics robot. As shown in fig. 4, when the above path planning algorithm iterates, the loss function may converge quickly as the number of iterations increases. Therefore, compared with the conventional technology in the field, the logistics robot scheduling method can simultaneously process a plurality of individuals in a group by using a genetic algorithm, evaluate a plurality of solutions in a search space, and reduce the risk of trapping in a local optimal solution. The method has the main advantages that the solution of the task allocation model of the warehouse logistics environment does not need external intervention, the searching direction can be automatically optimized, and the global optimal solution can be found with low calculation cost. In addition, the actual warehouse logistics scene is considered for calculating the cost values of the task nodes, the cost values can be quickly calculated by adopting a heuristic path search algorithm, and the method is more suitable for solving the problem of warehouse logistics task scheduling.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and
the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A dispatching method of a logistics robot is characterized by comprising the following steps:
determining an environment model related to the logistics robot and the goods according to the initial parking position of the logistics robot and the position of the goods to be carried, and determining a target function of logistics environment task scheduling;
determining the permutation code and the initialization solution set of the task node of at least one point to be carried according to the environment model and the objective function of the logistics environment task scheduling;
determining a fitness evaluation function according to the objective function of the logistics environment task scheduling and the permutation codes; and
and determining a dispatching scheme of the logistics robot according to the fitness evaluation function, the environment model and the permutation code.
2. The scheduling method of claim 1, wherein the determining the scheduling scheme of the logistics robot according to the fitness evaluation function, the environment model and the ranking code comprises the following steps:
determining a first population according to the fitness evaluation function and the environment model;
according to the crossover operator and the mutation operator, carrying out heredity, crossover and mutation on the individual sequences of the first population;
judging the fitness value and the preset threshold value of each individual of the varied individual sequences of the first population; and
and determining a dispatching scheme of the logistics robot according to the judgment result.
3. The scheduling method according to claim 2, wherein the step of determining the scheduling scheme of the logistics robot according to the determination result comprises:
responding to the result that the fitness value of at least one individual of the varied individual sequences of the first population is larger than a preset threshold value, and selecting the individual with the largest fitness value as a dispatching scheme of the logistics robot; and
and in response to the result that the fitness values of the individuals of the inherited, crossed and mutated individual sequences of the first population are smaller than the preset threshold, executing the steps of determining the first population, performing inheritance, crossing and mutation, judging the fitness values of the individuals of the mutated individual sequences of the first population and the preset threshold again, and determining the dispatching scheme of the logistics robot according to the judgment result.
4. The scheduling method according to claim 2 or 3, wherein the step of determining the first population according to the fitness evaluation function and the environment model comprises:
determining the fitness value of the current population according to the fitness evaluation function and the environment model;
determining the fitness value of each individual in the current population according to the fitness evaluation function;
determining the probability of each individual in the next generation population according to the fitness value of each individual in the current population; and
and determining the first population according to the probability of each individual in the offspring population.
5. The scheduling method of claim 2 wherein the step of inheriting, intersecting and mutating the sequences of individuals of the first population according to an intersection operator and a mutation operator comprises:
copying and transmitting the gene sequence with high fitness in the first population to a next generation population;
for individuals with low fitness in the first population, selecting two segments of gene segments with the same quantity from random positions in a gene sequence formed by task nodes of all points to be carried in the current population, and crossing the selected gene segments;
determining the variation rate according to a preset variation factor, the current iteration times and a preset maximum iteration time; and
and (4) carrying out variation on the first population after inheritance and crossing according to the variation rate.
6. The dispatching method according to claim 1, wherein the step of determining an environment model about the logistics robot and the cargo and determining an objective function of the logistics environment task dispatching according to the initial parking position of the logistics robot and the position of the cargo to be handled comprises:
regarding the logistics robot and the goods to be carried as particles, and performing topological graph modeling according to the initial parking position of the logistics robot and the position of the goods to be carried to determine the environment model;
determining the maximum 22A010 1CNCN of the movement of each logistics robot according to the initial parking positions of the logistics robots
A distance objective function of small total path length;
determining a load balancing objective function of each logistics robot according to the load of the plurality of logistics robots; and
and determining the objective function of the logistics environment task scheduling according to the distance objective function of the total moving path length of each logistics robot and the load balancing function of each logistics robot.
7. The scheduling method of claim 6 wherein the constraints of the objective function of the logistics environment task scheduling include but are not limited to one or more of constraints on robot movement speed and constraints on robot minimum and maximum load capacity.
8. The scheduling method according to claim 1, wherein the step of determining a permutation code and an initialization solution set of task nodes of at least one point to be handled according to the environment model and the objective function of the logistics environment task scheduling comprises:
and coding the initial population according to a two-section coding mode so as to determine the permutation codes and the initialization solution sets of the task nodes of the points to be carried.
9. A dispatching system of a logistics robot is characterized by comprising:
a memory; and
a processor connected to the memory and configured to implement the method of scheduling of the logistics robot of any of claims 1-8.
10. A computer-readable storage medium having stored thereon computer instructions, wherein the computer instructions, when executed by a processor, implement the logistics robot scheduling system method of any one of claims 1 to 8.
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| CN116542493B (en) * | 2023-07-04 | 2023-10-10 | 北京邮电大学 | Centralized scheduling real-time task allocation method and device for high-speed sorting robot |
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