Multi-robot task allocation method based on particle swarm optimization

Abstract

The invention relates to a multi-robot task allocation method based on particle swarm optimization, and belongs to the technical field of robot task allocation and collaborative scheduling algorithms. The method comprises the following steps: establishing an intelligent factory map required by the operation of the intelligent transfer robot; parameters of a to-be-executed task are input, and robot parameters and an energy consumption calculation system of the robot for the task are defined; according to the parameters of the to-be-executed task, whether a robot capable of bearing the task weight exists or not is judged, and if yes, the task is distributed through an improved market method; and if not, all the robots are divided into different robot alliances through a robot alliance mechanism, and the most suitable robot alliance is selected in combination with a particle swarm optimization algorithm to complete the task to be executed. According to the method, the reasonability and efficiency of task allocation are improved, and an effective solution is provided for the task allocation problem of the intelligent logistics robot in a complex environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot task allocation and collaborative scheduling algorithms, and relates to a multi-robot task allocation method based on particle swarm optimization applicable to logistics factories. Background Art

[0002] With the rapid growth of e-commerce and modern logistics and warehousing, multi-robot task allocation in smart logistics factories has become a growing focus. As representatives of high efficiency and automation, multi-robot systems can not only handle complex handling tasks but also significantly improve the efficiency and flexibility of logistics operations by optimizing resource utilization. Therefore, achieving more efficient and stable robot scheduling is of great significance to improving the quality of supply chain management.

[0003] Currently, many task allocation algorithms are capable of completing multi-robot scheduling tasks, such as collaborative search algorithms, collaborative allocation methods, and distributed auction task allocation algorithms that add links to adjust the order of task execution. However, the large number of robots and complex factory environments complicate the rationality and stability of allocation schemes. Collaborative search algorithms and collaborative allocation methods can quickly achieve good task allocation results, but they struggle to cope with complex dynamic scenarios and are unsuitable for task allocation in smart factory environments. The existing distributed auction task allocation algorithm that adds links to adjust the order of task execution can simultaneously calculate the current and next plan, and it also incorporates task priorities, which can improve the algorithm's stability and rationality. However, it still suffers from long task execution times and a long tail of residual revenue. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a multi-robot task allocation method based on particle swarm optimization, which enriches and optimizes the initial solution of the robot allocation scheme by adding a robot alliance mechanism, improves the robustness in the face of complex situations by adding a leasing mechanism, and realizes the accuracy and stability of the task allocation scheme.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A multi-robot task allocation method based on particle swarm optimization, the method comprising:

[0007] S1. Build the smart factory map required for the operation of the smart handling robot;

[0008] S2. Input the parameters of the task to be performed, define the robot parameters and define the energy consumption calculation system of the robot for the task;

[0009] S3. Determine whether there is a robot capable of carrying the task weight based on the parameters of the task to be performed. If so, proceed to step S4. If not, divide all robots into different robot alliances through the robot alliance mechanism and proceed to step S5.

[0010] S4. Use a leasing mechanism to replace the auction mechanism in the market method, and then assign tasks to robots through the replaced market method;

[0011] S5. Combine the particle swarm optimization algorithm to select the most suitable robot alliance to complete the task to be performed.

[0012] Furthermore, step S1 includes: building a smart factory model through simulation tools, and designing the layout of containers in the smart factory model to ensure that each container has a clear location, and each container can independently receive and store tasks and work in collaboration with robots.

[0013] Furthermore, in step S2, the inputting of parameters of the task to be executed includes: inputting the starting point and destination point of the task to be executed, and using the container corresponding to the task to be executed as the starting point and destination point; at the same time, setting the income according to the weight of the task to be executed.

[0014] Furthermore, in step S2, defining the robot parameters includes: inputting the load capacity, movement speed, and energy consumption of each robot into the smart factory model based on the actual robot parameters; randomly assigning the initial position of each robot taking into account the robot's working range and the requirements of the handling task, and ensuring that the initial position of each robot does not conflict with the container position or the position of other robots;

[0015] The energy consumption calculation system for defining the robot's task includes defining the robot's no-load energy consumption calculation system and loaded energy consumption calculation system respectively; the no-load energy consumption calculation system is the distance the robot travels from its current position to the container to pick up goods multiplied by the unit distance energy consumption of the robot when it is no-load; the loaded energy consumption calculation system is the distance the robot travels from the container where it picks up goods to the task destination multiplied by the unit distance energy consumption of the robot when it is loaded.

[0016] Furthermore, in step S3, the robot alliance mechanism is used to divide all idle robots into different robot alliances, including dividing all idle robots into different robot alliances, each robot alliance including three robots.

[0017] Furthermore, step S4 includes calculating the energy consumption required for each robot to complete the task to be performed based on the weight of the task to be performed and the energy consumption calculation system of each robot, and subtracting the energy consumption required for each robot to complete the task to be performed from the income corresponding to the task to be performed to obtain the residual income; then judging whether there is a robot whose residual income is greater than 0; if so, selecting the robot with the largest residual income among the robots whose residual income is greater than 0 to complete the task to be performed; if not, selecting the most suitable robot through the leasing mechanism to complete the task to be performed.

[0018] Among them, selecting the most suitable robot to complete the task to be performed through the leasing mechanism includes, when the residual income of the robot is greater than 0, not considering the income of the task to be performed, but only comparing the time required for each robot to complete the task to be performed, and selecting the robot with the shortest time to complete the task to be performed.

[0019] Furthermore, step S5 includes:

[0020] S51. For each robot alliance, assign the tasks to be performed to the robots in the robot alliance by giving priority to robots with heavy loads, and calculate the energy consumption required for each robot in the robot alliance to complete its task.

[0021] S52, individually calculating the residual income of each robot in the robot alliance after completing its own task, and summing them to obtain the total residual income of the robot alliance; recording the time required for each robot alliance to complete the task to be executed;

[0022] S53, performing a weighted summation of the total residual income and time corresponding to each robot alliance, and iteratively optimizing the weights of the total residual income and time using a particle swarm algorithm, and finally calculating an evaluation index based on the optimized weights;

[0023] S54: Select the robot alliance with the largest evaluation index to complete the task to be performed.

[0024] Furthermore, in this method, it is necessary to wait until the previous task is completed before allocating the next task to be executed.

[0025] The beneficial effects of the present invention are as follows: To address the problem of insufficient adaptability to dynamic environments, the present invention introduces a dynamic adjustment strategy that combines a robot alliance mechanism with a leasing mechanism. The alliance mechanism combines heterogeneous robots on demand, effectively addressing the need for collaborative handling in overloaded task scenarios. The leasing mechanism replaces the traditional auction model, avoiding the problem of task allocation pauses caused by special circumstances and shortening task execution time. At the same time, the present invention constructs a dual-dimensional calculation system for no-load and loaded energy consumption, fully considering the differences in the impact of the robot's motion state on energy consumption, and combining it with a dynamic task benefit evaluation mechanism to achieve a precise balance between resource utilization efficiency and economic benefits.

[0026] Furthermore, this invention employs a particle swarm optimization algorithm to perform multi-objective iterative optimization of the robot alliance's comprehensive performance. By dynamically adjusting the weight coefficients of residual revenue and task execution time, and simultaneously optimizing both residual revenue and execution time, the rationality and efficiency of task allocation are improved. While ensuring maximum economic benefits, this approach prioritizes meeting the stringent timeliness requirements of modern logistics. This dual-objective optimization strategy not only effectively mitigates the long-tail residual revenue problem found in traditional algorithms, but also automatically adapts the optimal weight combination based on real-time scenario requirements, significantly enhancing the robustness of task allocation methods in complex scenarios.

[0027] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0029] Figure 1 This is a flow chart of the multi-robot task allocation method based on particle swarm optimization proposed in the present invention;

[0030] Figure 2 A flowchart of a multi-robot task allocation method based on particle swarm optimization is provided for a specific embodiment. DETAILED DESCRIPTION

[0031] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0032] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0033] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0034] In order to solve the problems that existing robot task allocation methods are difficult to cope with complex dynamic scenarios, are not suitable for task allocation in smart factory environments, and have long task execution time and long tail of residual benefits, the present invention proposes a multi-robot task allocation method based on particle swarm optimization.

[0035] This method improves the market-based task allocation algorithm based on a particle swarm optimization algorithm. Specifically, the initial solution for the robot allocation scheme is enriched and optimized by adding a robot alliance mechanism. A leasing mechanism is also added to improve robustness in complex situations. Particle swarm optimization is then used to iteratively optimize the solution obtained from the market-based task allocation algorithm with these two mechanisms. This further improves the robustness of the task allocation while balancing residual revenue and task execution time, achieving an accurate and stable task allocation scheme.

[0036] like Figure 1 FIG. 1 is a multi-robot task allocation method based on particle swarm optimization according to an embodiment of the present invention, and the method includes:

[0037] 1. Build the smart factory map required for the operation of the intelligent handling robot, as follows:

[0038] Use simulation tools to build a smart factory model, including the layout of all containers and other facilities;

[0039] Design the specific layout of the containers in the smart factory model to ensure that each container has a clear location, each container can independently receive and store tasks, and work collaboratively with robots.

[0040] 2. Define the task parameters, robot parameters, and the robot's energy consumption calculation system for the task, including:

[0041] 1) According to each task to be executed, set the starting point and destination of the task in the smart factory model, and use the container corresponding to the task as the starting point and destination;

[0042] 2) Set the revenue according to the weight of the task to be performed;

[0043] 3) Based on the actual robots in the factory, the parameters of these robots are input into the smart factory model. These robots have different load capacities and unique energy consumption characteristics. Specifically, the robot load capacity, movement speed, energy consumption and other parameters are set through simulation tools to ensure that the robot can select the appropriate execution plan based on the task requirements;

[0044] 4) Randomly assign the robot's initial position and ensure that the robot's initial position does not conflict with the container position or other robots' positions; in addition, the selection of the initial position should take into account the robot's working range and task requirements.

[0045] The robot's energy consumption calculation system for a task includes an empty-load energy consumption calculation system and a loaded energy consumption calculation system. The empty-load energy consumption calculation system is applied to the process of the robot starting from its current position and heading to the container to pick up the goods, while the loaded energy consumption calculation system is applied to the process of the robot picking up the goods from the container, loading them, and heading to the task destination. The empty-load energy consumption calculation system is the product of the distance to pick up the goods and the energy consumption per unit distance when the robot is empty, which can be simply expressed as the following formula:

[0046] E1=L1×e1

[0047] Among them, E1 is the energy consumed by the robot in the process of starting from its current position and going to the container to pick up the goods, L1 is the distance between the robot's current position and the destination container, and e1 is the energy consumption per unit distance when the robot is unloaded.

[0048] The load energy consumption calculation system is the delivery distance multiplied by the unit distance energy consumption when the robot is loaded, which can be simply expressed as:

[0049] E2=L2×e2

[0050] Where E2 is the energy consumed by the robot as it travels from the container to the destination, L2 is the distance between the container and the destination, and e2 is the energy consumed per unit distance when the robot is loaded. e2 varies adaptively based on the weight of the task.

[0051] 3. Determine whether there is a robot that can carry the task weight based on the defined task parameters. If so, proceed to the next step and assign the task;

[0052] If it does not exist, all robots will be divided into different robot alliances through the robot alliance mechanism. Each robot alliance includes three robots, and then tasks will be assigned.

[0053] 4. Allocate tasks through the market method. In this method, the auction mechanism is removed and the leasing mechanism is adopted for task allocation. The details are as follows:

[0054] First, each robot calculates the energy consumption of the task according to the energy consumption calculation system, and subtracts the energy consumption of the robot performing the task from the preset benefit of the task to obtain the residual benefit;

[0055] Then determine whether there is a robot with a residual profit greater than 0; if there is a robot with a residual profit greater than 0, select the robot with the highest residual profit to perform the task; if there is no robot with a residual profit greater than 0, convert the task into a rental task and select a suitable robot to perform the task.

[0056] Among them, the method of selecting a suitable robot to perform the task by publishing the rental task is: screening out robots that can bear the task weight, calculating the time required for these robots to perform the task, and then selecting the robot with the shortest execution time to perform the task.

[0057] It should be noted that the above is the case when a single robot can bear the task weight. If there is no robot that can bear the task weight to be performed, the task will be allocated based on the robot alliance, as follows:

[0058] First, within each robot alliance, tasks are assigned to each robot in the alliance, prioritizing robots with larger payloads. The energy consumption of each robot in the alliance is calculated, and the residual benefits of each robot in the alliance are calculated separately. The residual benefits of each robot in the alliance are summed to obtain the total residual benefit of the robot alliance. Furthermore, the time required for the robot alliance to complete the task is recorded. For example, consider a robot alliance R, which includes robots r1, r2, and r3, with decreasing payload capacities. The weight of the task to be performed is M, and the weights of the tasks assigned to robots r1, r2, and r3 are m1, m2, and m3, where m1 > m2 > m3. The residual benefit i1 of robot r1 completing the subtask of weight m1 is calculated. Similarly, the residual benefits i2 and i3 of robots r2 and r3 are calculated, and the total residual benefit of the robot alliance R is summed to obtain I = i1 + i2 + i3. The time T required for the robot alliance to complete the task is also recorded.

[0059] The total residual income of the robot alliance and the time required to complete the task are weighted and summed to obtain an evaluation index. The robot alliance with the highest evaluation index is selected to perform the task to be executed. For example, a = αI + βT, where a is the evaluation index, and α and β are the weights of the total residual income I and time T, respectively. The particle swarm optimization algorithm is used to iteratively optimize the weight α of the total residual income and the weight β of the time, obtaining a more optimal trade-off between the total residual income and time. This makes the task allocation more reasonable, that is, the better robot alliance can be selected to perform the task to maximize energy and time savings.

[0060] It should be noted that when assigning tasks, it is necessary to wait for the previous task to be completed before assigning the next task. Since the same robot cannot perform two different tasks simultaneously, if the next task is assigned while other tasks are being executed, it is possible that a robot currently performing a task is the optimal robot for the next task. In this case, the optimal robot must be abandoned and the suboptimal robot must be selected to perform the task, which is not conducive to reducing energy consumption and time costs in logistics factories. Therefore, the method proposed in this invention sets the wait time for the previous task to be completed before assigning the next task, so as to reduce energy consumption and time costs in logistics factories.

[0061] In one embodiment of the present invention, a smart factory model can be constructed using the simulation tool Matlab. Within this model, a specific layout of containers is designed, ensuring that each container has a clear location, can independently receive and store tasks, and can work collaboratively with robots. A robot model is constructed based on real robots, each with different load capacities and unique energy consumption characteristics. Specifically, the simulation tool is used to set parameters such as the robot's load capacity, travel speed, and energy consumption, ensuring that the robot can select the appropriate execution plan based on the task requirements.

[0062] In one embodiment of the present invention, during the entire process of task allocation and execution, the remaining income, robot trajectory, task allocation plan, etc. can be recorded, and the results can be displayed through visualization tools to facilitate viewing and verification of the final execution status.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-robot task allocation method based on particle swarm optimization, characterized in that: The method includes: S1. Build the smart factory map required for the operation of the smart handling robot; S2. Input the parameters of the task to be performed, define the robot parameters, and define the energy consumption calculation system of the robot for the task; S3. Determine whether there is a robot capable of carrying the task weight based on the parameters of the task to be performed. If so, proceed to step S4. If not, divide all robots into different robot alliances through the robot alliance mechanism and proceed to step S5. S4. Use a leasing mechanism to replace the auction mechanism in the market method, and then assign tasks to robots through the replaced market method; S5. Combine the particle swarm optimization algorithm to select the most suitable robot alliance to complete the task to be performed.

2. The method according to claim 1, characterized in that Step S1 includes: building a smart factory model through simulation tools, and designing the layout of containers in the smart factory model to ensure that each container has a clear location, and each container can independently receive and store tasks and work in collaboration with robots.

3. The method according to claim 1, characterized in that In step S2, the input of the parameters of the task to be executed includes: inputting the starting point and destination point of the task to be executed, and using the container corresponding to the task to be executed as the starting point and destination point; at the same time, setting the profit according to the weight of the task to be executed.

4. The method according to claim 1, wherein In step S2, defining the robot parameters includes: inputting the load capacity, movement speed, and energy consumption of each robot into the smart factory model based on the actual robot parameters; randomly assigning the initial position of each robot based on the robot's working range and the requirements of the handling task, and ensuring that the initial position of each robot does not conflict with the container position or the position of other robots; The energy consumption calculation system for defining the robot's task includes defining the robot's no-load energy consumption calculation system and loaded energy consumption calculation system respectively; the no-load energy consumption calculation system is the distance the robot travels from its current position to the container to pick up goods multiplied by the unit distance energy consumption of the robot when it is no-load; the loaded energy consumption calculation system is the distance the robot travels from the container where it picks up goods to the task destination multiplied by the unit distance energy consumption of the robot when it is loaded.

5. The method according to claim 1, wherein In step S3, the robot alliance mechanism is used to divide all idle robots into different robot alliances, including dividing all idle robots into different robot alliances, each robot alliance includes three robots.

6. The method according to claim 1, characterized in that Step S4 includes calculating the energy consumption required for each robot to complete the task to be performed based on the weight of the task to be performed and the energy consumption calculation system of each robot, and subtracting the energy consumption required for each robot to complete the task to be performed from the income corresponding to the task to be performed to obtain the residual income; then judging whether there is a robot whose residual income is greater than 0; if so, selecting the robot with the largest residual income among the robots whose residual income is greater than 0 to complete the task to be performed; if not, selecting the most suitable robot to complete the task to be performed through the leasing mechanism.

7. The method according to claim 6, characterized in that The selection of the most suitable robot to complete the task to be performed through the leasing mechanism includes, when the residual income of the robot is greater than 0, not considering the income of the task to be performed, but only comparing the time required for each robot to complete the task to be performed, and selecting the robot with the shortest time to complete the task to be performed.

8. The method according to claim 1, characterized in that Step S5 includes: S51. For each robot alliance, assign the tasks to be performed to the robots in the robot alliance by giving priority to robots with heavy loads, and calculate the energy consumption required for each robot in the robot alliance to complete its task. S52, individually calculating the residual income of each robot in the robot alliance after completing its own task, and summing them to obtain the total residual income of the robot alliance; recording the time required for each robot alliance to complete the task to be executed; S53, performing a weighted summation of the total residual income and time corresponding to each robot alliance, and iteratively optimizing the weights of the total residual income and time using a particle swarm algorithm, and finally calculating an evaluation index based on the optimized weights; S54: Select the robot alliance with the largest evaluation index to complete the task to be performed.

9. The method according to claim 1, characterized in that In this method, it is necessary to wait until the previous task is completed before allocating the next task to be executed.

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