Scheduling methods and systems for logistics robots

Abstract

This invention provides a scheduling method for logistics robots, a scheduling system for logistics robots, and a corresponding computer-readable storage medium. The scheduling method for logistics robots includes the following steps: determining an environmental model of the logistics robot and goods based on the initial docking position of the logistics robot and the position of the goods to be transported, and determining an objective function for task scheduling in the logistics environment; determining the permutation code and initial solution set of task nodes at least one point to be transported based on the environmental model and the objective function; determining a fitness evaluation function based on the objective function and the permutation code; and determining a scheduling scheme for the logistics robot based on the fitness evaluation function, the environmental model, and the permutation code.

Description

Technical Field

[0001] This invention relates to the field of logistics robot scheduling, and more particularly to a logistics robot scheduling method, a logistics robot scheduling system, and a corresponding computer-readable storage medium. Background Technology

[0002] Task scheduling for warehouse logistics robots refers to the optimization problem of rationally allocating tasks while satisfying safety and robot dynamics constraints, with the goal of minimizing the total travel distance of multiple robots. Traditional warehouse logistics systems primarily rely on manual handling for inbound and outbound goods, but this method requires a large workforce, is inefficient, and prone to inventory buildup and loss. Although many domestic and international companies have begun using robots in warehouse logistics environments, the industry is still in its early stages of development, with low adoption rates. In practical work, ensuring the efficient operation of the warehouse logistics system requires ensuring the rational allocation of multiple handling tasks to various robots and the safe transport of goods to designated locations. Currently, existing technologies cannot efficiently and accurately formulate scheduling schemes for logistics robots.

[0003] To overcome the aforementioned shortcomings of existing technologies, there is an urgent need in this field for a scheduling method for logistics robots to solve the task scheduling problem of multiple mobile robots in a warehousing and logistics environment. This method should be able to quickly converge to the optimal solution, achieve reasonable task scheduling of multiple robots, and thus realize the accuracy and efficiency of logistics robot scheduling. Summary of the Invention

[0004] The following provides a brief overview of one or more aspects to offer a basic understanding of them. This overview is not an exhaustive summary of all conceived aspects, nor is it intended to identify key or decisive elements of all aspects, nor to define 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 descriptions that follow.

[0005] To overcome the aforementioned deficiencies in the existing technology, this invention provides a scheduling method for logistics robots, a scheduling system for logistics robots, and a corresponding computer-readable storage medium. This method can solve the task scheduling problem of multiple mobile robots in a warehousing and logistics environment, and can quickly converge to obtain the optimal solution, thereby achieving reasonable task scheduling of multiple robots and realizing the accuracy and efficiency of logistics robot scheduling.

[0006] Specifically, the scheduling method for the logistics robot provided by the first aspect of the present invention may include the following steps: determining an environmental model of the logistics robot and the goods to be transported based on the initial docking position of the logistics robot and the position of the goods to be transported, and determining an objective function for logistics environment task scheduling; determining a permutation code and an initial solution set for task nodes of at least one point to be transported based on the environmental model and the objective function for logistics environment task scheduling; determining a fitness evaluation function based on the objective function for logistics environment task scheduling and the permutation code; and determining a scheduling scheme for the logistics robot based on the fitness evaluation function, the environmental model, and the permutation code.

[0007] Furthermore, in some embodiments of the present invention, determining the scheduling scheme of the logistics robot based on the fitness evaluation function, the environment model, and the permutation code includes the following steps: determining a first population based on the fitness evaluation function and the environment model; performing genetic, crossover, and mutation operations on the individual sequences of the first population based on the crossover and mutation operators; determining the fitness value of each individual in the mutated first population's individual sequences relative to a preset threshold; and determining the scheduling scheme of the logistics robot based on the determination result.

[0008] Furthermore, in some embodiments of the present invention, the step of determining the scheduling scheme of the logistics robot based on the judgment result includes: in response to the result that the fitness value of at least one individual in the individual sequence of the mutated first population is greater than a preset threshold, selecting the individual with the largest fitness value as the scheduling scheme of the logistics robot; and in response to the result that the fitness values ​​of all individuals in the individual sequence of the first population after genetics, crossover and mutation are less than the preset threshold, performing the steps of determining the first population, performing genetics, crossover and mutation, judging the fitness value of each individual in the individual sequence of the mutated first population relative to the preset threshold, and determining the scheduling scheme of the logistics robot based on the judgment result again.

[0009] Furthermore, in some embodiments of the present invention, the step of determining the first population based on the fitness evaluation function and the environment model includes: determining the fitness value of the current population based on the fitness evaluation function and the environment model; determining the fitness value of each individual in the current population based on the fitness evaluation function; determining the probability of each individual in the offspring population based on the fitness value of each individual in the current population; and determining the first population based on the probability of each individual in the offspring population.

[0010] Furthermore, in some embodiments of the present invention, the step of performing heredity, crossover, and mutation on the individual sequences of the first population according to the crossover operator and the mutation operator includes: copying and hereditary the gene sequences with high fitness in the first population to the next generation population; for individuals with low fitness in the first population, selecting two gene segments of the same number from random positions in the gene sequences formed by the individual and the task nodes of each point to be transported in the current population, and performing crossover on the selected gene segments; determining the mutation rate according to a preset mutation factor, the current iteration number, and a preset maximum iteration number; and mutating the first population after heredity and crossover according to the mutation rate.

[0011] Furthermore, in some embodiments of the present invention, the step of determining an environmental model of the logistics robot and the goods to be transported based on the initial docking position of the logistics robot and the position of the goods to be transported, and determining an objective function for logistics environment task scheduling, includes: treating both the logistics robot and the goods to be transported as point masses, performing topological graph modeling based on the initial docking position of the logistics robot and the position of the goods to be transported to determine the environmental model; determining a distance objective function for the minimum total path length of movement of each logistics robot based on the initial docking positions of the multiple logistics robots; determining a load balancing objective function for each logistics robot based on the load of the multiple logistics robots; and determining the objective function for logistics environment task scheduling based on the distance objective function for the total path length of movement of each logistics robot and the load balancing function of each logistics robot.

[0012] Furthermore, in some embodiments of the present invention, the constraints of the objective function for task scheduling in the logistics environment include, but are not limited to, one or more of the constraints on robot movement speed and robot minimum and maximum load capacity.

[0013] Furthermore, in some embodiments of the present invention, the step of determining the arrangement code and initialization solution set of task nodes of at least one point to be transported based on the environmental model and the objective function of the logistics environment task scheduling includes: encoding the initial population according to a two-segment encoding method to determine the arrangement code and initialization solution set of task nodes of each point to be transported.

[0014] Furthermore, the logistics robot scheduling system provided according to a second aspect of the present invention includes: a memory; and a processor connected to the memory and configured to implement the logistics robot scheduling method described above.

[0015] Furthermore, according to a third aspect of the present invention, a computer-readable storage medium is provided thereon storing computer instructions, which, when executed by a processor, implement a method for determining the scheduling of a logistics robot. Attached Figure Description

[0016] The above-described features and advantages of the present invention will be better understood after reading the following detailed description of embodiments of the present disclosure in conjunction with the accompanying drawings. In the drawings, components are not necessarily drawn to scale, and components having similar related characteristics or features may have the same or similar reference numerals.

[0017] Figure 1 An architectural diagram of a scheduling system for logistics robots provided according to some embodiments of the present invention is shown.

[0018] Figure 2 A flowchart of a scheduling method for logistics robots according to some embodiments of the present invention is shown.

[0019] Figure 3 A diagram showing the task allocation results provided according to some embodiments of the present invention is illustrated.

[0020] Figure 4 A graph showing the cost variation during the iteration process provided according to some embodiments of the present invention is shown. Detailed Implementation

[0021] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Although the description of the present invention is presented in conjunction with preferred embodiments, this does not mean that the features of the invention are limited to these embodiments. On the contrary, the purpose of describing the invention in conjunction with embodiments is to cover other options or modifications that may be derived based on the claims of the present invention. To provide a thorough understanding of the invention, many specific details will be included in the following description. The invention may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of the invention, some specific details will be omitted in the description.

[0022] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] Furthermore, the terms "upper," "lower," "left," "right," "top," "bottom," "horizontal," and "vertical" used in the following description should be understood as the orientations shown in the relevant paragraphs and accompanying drawings. These relative terms are for illustrative purposes only and do not imply that the described apparatus must be manufactured or operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0024] It is understood that although terms such as "first," "second," and "third" may be used herein to describe various components, regions, layers, and / or parts, these components, regions, layers, and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers, and / or parts. Therefore, the first components, regions, layers, and / or parts discussed below may be referred to as second components, regions, layers, and / or parts without departing from some embodiments of the present invention.

[0025] As mentioned above, task scheduling for warehouse logistics robots refers to the optimization problem of rationally allocating tasks while satisfying safety and robot dynamics constraints, with the goal of minimizing the total travel distance of multiple robots. Traditional warehouse logistics systems primarily rely on manual handling to complete the inbound and outbound operations of goods. However, this method requires a large number of workers, is inefficient, and prone to issues such as goods backlog and loss. Although many domestic and international companies have begun to deploy robots in warehouse logistics environments, the overall development is still in its early stages, and the adoption rate is not high. In practical work, to ensure the efficient operation of the warehouse logistics system, it is crucial to rationally allocate multiple handling tasks to various robots and ensure the safe transport of goods to designated locations. Currently, existing technologies cannot efficiently and accurately formulate scheduling schemes for logistics robots.

[0026] To overcome the aforementioned deficiencies in the existing technology, this invention provides a scheduling method for logistics robots, a scheduling system for logistics robots, and a corresponding computer-readable storage medium. This method can solve the task scheduling problem of multiple mobile robots in a warehousing and logistics environment, and can quickly converge to obtain the optimal solution, thereby achieving reasonable task scheduling of multiple robots and realizing the accuracy and efficiency of logistics robot scheduling.

[0027] In some non-limiting embodiments, the scheduling method for the logistics robot provided in the first aspect of the present invention can be implemented via the scheduling system for the logistics robot provided in the second aspect of the present invention. Specifically, the scheduling system for the logistics robot is equipped with a memory and a processor. The memory includes, but is not limited to, the computer-readable storage medium provided in the third aspect of the present invention, on which computer instructions are stored. The processor is connected to the memory and is configured to execute the computer instructions stored in the memory to implement the scheduling method for the logistics robot provided in the first aspect of the present invention.

[0028] Please refer to the following first. Figure 1 , Figure 1 A schematic diagram of an apparatus for determining the hand movements of a virtual human, according to some embodiments of the present invention, is shown.

[0029] like Figure 1 As shown, the logistics robot scheduling system provided in the second aspect of the present invention may include a communication module 105, a memory, and a processor 102. The memory may include an internal communication bus 101, a 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 enables data communication for the logistics robot scheduling system. The processor 102 can perform judgments and issue prompts. In some embodiments, the processor...

[0030] Device 102 may consist of one or more processors. Communication port 105 enables data transmission and communication between the logistics robot's control system and external input / output devices. In some embodiments, the...

[0031] The logistics robot scheduling system can send and receive information and data from the network via communication port 105. In some embodiments, the logistics robot scheduling system can transmit and communicate with external input / output devices via input / output port 106 in a wired manner. The device for determining the virtual human's hand movements may also include different forms of program storage units and data storage units, such as hard disk 107, read-only memory (ROM) 103 and random access memory (RAM) 104, capable of storing computer processing and / or...

[0032] The communication uses various data files, as well as possible program instructions executed by processor 102. Processor 102 executes these instructions to implement the main part of the method. The results processed by processor 102 are transmitted to an external output device via communication port 105 and displayed on the user interface of the output device.

[0033] The working principle of the scheduling system for the logistics robot 5 described above will be described below with reference to some embodiments of scheduling methods for logistics robots. Those skilled in the art will understand that these embodiments are merely examples.

[0034] The non-limiting embodiments provided in this invention are intended to clearly illustrate the main concepts of the invention and to provide specific solutions that are easy for the public to implement, rather than to limit all functions or all working methods of the logistics robot scheduling method. Similarly, the logistics robot scheduling system is only one aspect of this invention.

[0035] The provided implementation is a non-limiting one and does not restrict the execution subject of each step in the scheduling method for these logistics robots.

[0036] Please refer to Figure 2 . Figure 2 A flowchart of a scheduling method for logistics robots according to some embodiments of the present invention is shown.

[0037] like Figure 2 As shown, the above-mentioned scheduling method for logistics robots can first be based on the initial...

[0038] Based on the docking location and the location of the goods to be transported, an environmental model for the aforementioned logistics robot and goods is determined, and the objective function for task scheduling in the logistics environment is defined. Subsequently, the aforementioned scheduling method can be applied based on...

[0039] The aforementioned environmental model and objective function for task scheduling in the logistics environment determine the permutation code and initial solution set of task nodes at at least one point to be transported. After determining the permutation code and initial solution set, the method can determine the fitness evaluation function based on the objective function for task scheduling in the logistics environment and the permutation code. After determining the fitness evaluation function, the method can determine the scheduling scheme for the logistics robot based on the fitness evaluation function, the environmental model, and the permutation code.

[0040] Furthermore, in some embodiments of the present invention, the above-described logistics robot scheduling method can treat both the logistics robots and the goods to be transported as point masses, and perform topological graph modeling based on the initial docking positions of the logistics robots and the positions of the goods to be transported to determine the environment model. Then, the method can determine a distance objective function for the minimum total path length of movement for each of the multiple logistics robots based on their initial docking positions. After determining the objective function, the method can determine a load balancing objective function for each of the multiple logistics robots based on their loads. After determining the load balancing objective function, the method can determine the objective function for task scheduling in the logistics environment based on the distance objective function for the total path length of movement for each of the logistics robots and the load balancing function for each of the logistics robots.

[0041] Specifically, the above method can first construct a task scheduling model for the warehousing and logistics environment, treating both the goods to be transported and the logistics robots as point masses, determining the initial docking position of each idle logistics robot and the position of the goods to be transported, and then using the topology graph method to model the environment.

[0042] Those skilled in the art will understand that the above-described method of using topological graphs for environmental and massage applications is merely a non-limiting implementation of the present invention, intended to model the aforementioned particles and provide specific solutions that are easy for the public to implement, rather than intended to limit the scope of protection of the present invention.

[0043] Furthermore, after environmental modeling, the above method can abstract warehousing and logistics tasks into how to utilize existing resources to maximize profits while minimizing costs, thus meeting requirements. Subsequently, this method can establish a warehousing and logistics task scheduling model. This model can use the minimum total robot movement path length and robot load balancing as objectives, while satisfying robot dynamics constraints and task allocation constraints, to establish an objective function. The objective function constructed by this method, with the minimum robot movement path length and robot load balancing as objectives, is as follows:

[0044] F = k1 * P + k2 * L

[0045] Here, P represents the minimum total path length for robot movement, L represents the robot load balancing degree, and k1 and k2 represent the corresponding cost trade-off coefficients, which can be manually set for different scenarios.

[0046] The objective function for minimizing the total path length for robot movement is:

[0047]

[0048] Where P iThis represents the total distance traveled by robot i in a single task. The total distance traveled by the robot in each task is expressed as:

[0049]

[0050] Here, c i jk x represents the distance cost for robot i to move from task node j to task node k; i jk This indicates whether a move from task node j to task node k is needed; a value of 1 indicates a move is needed, and a value of 0 indicates a move is needed otherwise. N represents the total number of task nodes. The robot load balancing objective function can be expressed as:

[0051]

[0052] Here, L represents the robot load balancing degree; M represents the total number of robots; y i j This indicates that task node j is completed by the i-th robot. A value of 1 indicates that robot i executes task j, and a value of 0 indicates that robot i does not execute task j.

[0053] Furthermore, in some embodiments of the present invention, after determining the objective function for task scheduling in the logistics environment, the method can encode the initial population using a two-segment coding method to determine the arrangement coding of the task nodes at each of the aforementioned transport points and the initial solution set. Specifically, the method can encode the initial population using a two-segment coding method, dividing the chromosome into two segments. The first segment represents the random arrangement of each transport task node, with a length of N, and each gene number is consistent with the label of the transport task node. The second segment represents the load capacity of each robot, with a length of M. Both the transport task nodes and the assigned robots are randomly combined and arranged.

[0054] Furthermore, after determining the permutation encoding of task nodes at at least one transport point and initializing the solution set, this method can use a fitness evaluation function to calculate the fitness value of the current population, serving as an important criterion for selecting offspring. Here, the fitness evaluation function needs to meet requirements such as universality, non-negativity, and consistency. Based on the characteristics of multi-robot task allocation, it is necessary to ensure that individuals meeting the target requirements are retained during the iteration process. The larger the evaluation value, the more reasonable the task allocation. The individual fitness evaluation function is designed as follows:

[0055]

[0056] Furthermore, after determining the fitness evaluation function, the above method can determine the scheduling scheme of the logistics robot based on the fitness evaluation function, the environmental model, and the permutation code.

[0057] Furthermore, in some embodiments of the present invention, the method can determine a first population based on the fitness evaluation function and the environmental model. Then, the method can perform genetic, crossover, and mutation operations on the individual sequences of the first population using crossover and mutation operators. Next, the method can determine the fitness value of each individual in the mutated first population's sequence relative to a preset threshold. Finally, the method can determine the scheduling scheme for the logistics robot based on the determination result.

[0058] Specifically, this method first determines the crossover and mutation operators to perform crossover and mutation on the individual sequences of the population to ensure the diversity of offspring. The method replicates and inherits gene sequences with high fitness values ​​from the population to the next generation. For individuals with low fitness values, two gene segments of the same number are randomly selected from the gene sequences of the target task points within the current individual, and these selected gene segments are exchanged. This gene sequence exchange further enhances solution diversity and local search capability. Simultaneously, to further increase solution diversity and local search capability, the gene sequences of each individual in the population undergo mutation at a certain mutation rate:

[0059]

[0060] Where ξ is the mutation factor, k is the current iteration number, and K is the maximum iteration number.

[0061] Individuals generated through heredity, crossover, and mutation form a new generation of population. The fitness is then recalculated by substituting these individuals into the objective function until the individual with the highest fitness in the population is found to be the optimal value.

[0062] The following example illustrates this, with the objective function and constraints expressed as follows:

[0063]

[0064] v i min ≤v i ≤v i max

[0065] l i min ≤l i ≤l i max

[0066]

[0067] Furthermore, in some embodiments of the present invention, when the fitness value of at least one individual in the individual sequence of the mutated first population is greater than a preset threshold, the method can select the individual with the highest fitness value as the scheduling scheme for the logistics robot. When the fitness values ​​of all individuals in the individual sequence of the first population after genetics, crossover, and mutation are less than the preset threshold, the method can again execute the steps of determining the first population, performing genetics, crossover, and mutation, judging the fitness value of each individual in the individual sequence of the mutated first population relative to the preset threshold, and determining the scheduling scheme for the logistics robot based on the judgment result. In other words, the method iterates repeatedly until the fitness value of the individual sequence of the first population is greater than the preset threshold. Thus, the method can solve the task scheduling problem of multiple mobile robots in a warehousing and logistics environment, and can quickly converge to obtain the optimal solution, achieving reasonable task scheduling of multiple robots, thereby achieving accuracy and efficiency in logistics robot scheduling.

[0068] Furthermore, in some embodiments of the present invention, the above method can determine the fitness value of the current population based on the fitness evaluation function and the environmental model. Then, the method can determine the fitness value of each individual in the current population based on the fitness evaluation function. Next, the method can determine the probability of each individual in the offspring population. After determining the probabilities, the method can determine the first population based on the probabilities of each individual in the offspring population.

[0069] Specifically, the above method can determine the fitness evaluation function, then determine the probability of an individual's appearance in the offspring population based on its fitness value, and select the individuals to be retained based on this probability, thereby continuously obtaining better solutions. This method can also determine the probability of an individual's appearance in the offspring based on its fitness value, and continuously obtain better offspring based on this probability, selecting individuals with higher fitness values. The selection probability of individual fitness is designed as follows:

[0070]

[0071] Here, p represents the proportion of fitness of individual i to the total fitness of all individuals in the population, and f i represents the fitness of the i-th individual in the population, and m represents the number of individuals in the population.

[0072] Furthermore, in some embodiments of the present invention, the method can replicate and inherit the high-fitness gene sequences from the first population to the next generation. Then, for individuals with low fitness in the first population, the method can select two identical gene segments from random positions in the gene sequences formed by the task nodes of each transport point in the current population, and crossover the selected gene segments. Next, the method can determine the mutation rate based on a preset mutation factor, the current iteration number, and a preset maximum iteration number. Finally, the method can mutate the inherited and crossovered first population according to the aforementioned mutation rate.

[0073] Furthermore, in some embodiments of the present invention, the constraints of the objective function for the above-mentioned logistics environment task scheduling include, but are not limited to, one or more of the constraints on robot movement speed and robot minimum and maximum load capacity.

[0074] Specifically, the state of the robot described above satisfies its own constraints as follows:

[0075] v i min ≤v i ≤v i max

[0076] l u nib ≤l i ≤l i nax

[0077]

[0078] Here, v i v represents the velocity of robot i. i min and v i max Let l represent the upper and lower limits of the movement speed of the i-th robot, respectively. i Let l represent the load capacity of the i-th robot. i min and l i max Let y represent the minimum and maximum load capacities of the i-th robot, respectively. i j This indicates that task node j is completed by the i-th robot, and that there is exactly one robot that completes the same task node.

[0079] Please refer to Figure 3 as well as Figure 4 . Figure 3 A diagram showing the task allocation results provided according to some embodiments of the present invention is illustrated. Figure 4 A graph showing the cost variation during the iteration process provided according to some embodiments of the present invention is shown.

[0080] like Figure 3 As shown, the scheduling plan for the logistics robot can be obtained after the above path planning. The above method can clearly, accurately, and efficiently plan the movement path of the logistics robot. Figure 4 As shown, the loss function of the aforementioned path planning algorithm converges rapidly with increasing iterations. Therefore, compared to conventional techniques in this field, the above-mentioned scheduling method for logistics robots can utilize genetic algorithms to simultaneously process multiple individuals in the population, evaluate multiple solutions in the search space, and reduce the risk of getting trapped in local optima. Its main advantage is that the task allocation model for the warehousing and logistics environment requires no external intervention, automatically optimizing the search direction and finding the global optimum with minimal computational cost. Furthermore, considering the actual warehousing and logistics scenario, the above method employs a heuristic path search algorithm to quickly calculate the cost value for task nodes, making it more suitable for solving warehousing and logistics task scheduling problems.

[0081] Although the methods described above are illustrated and depicted as a series of actions for the sake of simplicity, it should be understood and appreciated that these methods are not limited by the order of the actions, as some actions may occur in a different order and / or concurrently with other actions from the illustrations and descriptions herein or not illustrated and described herein but which may be understood by those skilled in the art, according to one or more embodiments.

[0082] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0083] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0084] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, 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 is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using 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 a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0085] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be readily apparent to those skilled in the art, and

[0086] The universal principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be granted the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A scheduling method for logistics robots, characterized in that, Includes the following steps: The logistics robot and the goods to be transported are both treated as point masses. A topological graph model is performed based on the initial docking position of the logistics robot and the position of the goods to be transported to determine the environment model. Based on the initial docking positions of the multiple logistics robots, determine the distance objective function for the minimum total path length of movement of each logistics robot; Based on the load of the multiple logistics robots, determine the load balancing objective function for each of the logistics robots; The objective function for task scheduling in the logistics environment is determined based on the distance objective function of the total movement path length of each logistics robot and the load balancing function of each logistics robot. Based on the environmental model and the objective function of the logistics environment task scheduling, determine the permutation code and initial solution set of at least one task node to be transported. Based on the objective function of the task scheduling in the logistics environment and the permutation coding, a fitness evaluation function is determined; and The scheduling scheme for the logistics robot is determined based on the fitness evaluation function, the environment model, and the permutation code.

2. The scheduling method according to claim 1, characterized in that, The step of determining the scheduling scheme for the logistics robot based on the fitness evaluation function, the environmental model, and the permutation code includes the following steps: Based on the fitness evaluation function and the environmental model, the first population is determined; Based on the crossover and mutation operators, the individual sequences of the first population are subjected to inheritance, crossover, and mutation. Determine the fitness value of each individual in the mutated first population and compare it to a preset threshold; and Based on the judgment results, a scheduling scheme for the logistics robot is determined.

3. The scheduling method according to claim 2, characterized in that, The step of determining the scheduling scheme for the logistics robot based on the judgment result includes: In response to the result that at least one individual in the sequence of individuals in the mutated first population has a fitness value greater than a preset threshold, the individual with the highest fitness value is selected as the scheduling scheme for the logistics robot; and In response to the result that the fitness values ​​of all individuals in the first population individual sequence after genetics, crossover, and mutation are less than a preset threshold, the steps of determining the first population, performing genetics, crossover, and mutation, judging the fitness values ​​of each individual in the mutated first population individual sequence against the preset threshold, and determining the scheduling scheme of the logistics robot based on the judgment result are executed again.

4. The scheduling method according to claim 2 or 3, characterized in that, The step of determining the first population based on the fitness evaluation function and the environmental model includes: The fitness value of the current population is determined based on the fitness evaluation function and the environmental model. The fitness value of each individual in the current population is determined according to the fitness evaluation function. Based on the fitness values ​​of each individual in the current population, determine the probability of each individual in the offspring population; and The first population is determined based on the probability of each individual in the offspring population.

5. The scheduling method according to claim 2, characterized in that, The steps of performing inheritance, crossover, and mutation on the individual sequences of the first population based on the crossover operator and the mutation operator include: The gene sequences with high fitness in the first population are copied and inherited by the next generation population; For individuals with low fitness in the first population, two gene segments of the same number are selected from random positions in the gene sequence formed by the task nodes of each point to be transported in the current population, and the selected gene segments are crossed. The mutation rate is determined based on the preset mutation factor, the current iteration number, and the preset maximum iteration number; and The first population after genetics and crossover is mutated according to the stated mutation rate.

6. The scheduling method according to claim 1, characterized in that, The constraints of the objective function for task scheduling in the logistics environment include one or more of the constraints on robot movement speed and robot minimum and maximum load capacity.

7. The scheduling method according to claim 1, characterized in that, The step of determining the permutation code and initialization solution set of at least one task node to be transported based on the environmental model and the objective function of the logistics environment task scheduling includes: The initial population is encoded using a two-stage encoding method to determine the arrangement encoding of the task nodes of each transport point and the initial solution set.

8. A scheduling system for logistics robots, characterized in that, include: Memory; as well as A processor, connected to the memory, and configured to implement the scheduling method for the logistics robot as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, the scheduling system method for the logistics robot as described in any one of claims 1 to 7 is implemented.

Images