A method of task allocation for a mobile robot

Abstract

The application discloses a kind of handling robot task allocation methods, belong to workshop intelligent logistics and handling robot task scheduling technical field.The method includes obtaining handling robot set and to-be-allocated task set;Task urgency, waiting time and material importance level are collected, and task comprehensive priority is constructed;Robot-to-task point distance, task point-to-task point transfer distance and path channel congestion coefficient are obtained;Ant colony algorithm parameters and pheromone matrix are initialized, and task priority, congestion suppression factor and load balancing factor are introduced in task selection probability;Task allocation evaluation function and pheromone updating mechanism are iteratively optimized, and the task allocation scheme with minimum total cost is output.The application can improve task allocation efficiency, reduce congestion influence, improve key task response speed and robot resource utilization.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics and task scheduling technology for handling robots in workshops, and particularly to a method for task allocation for handling robots. Background Technology

[0002] With the development of intelligent manufacturing, digital workshops, and flexible production lines, the production pace within workshops is constantly accelerating, and the frequency of material delivery and the number of handling tasks are increasing significantly. Handling robots are increasingly being applied to workshop logistics processes such as material transfer, empty-load retrieval, semi-finished product transfer, finished product warehousing, and production line replenishment. Compared to manual handling, handling robots can handle repetitive, high-frequency, and continuous material handling tasks, reducing the intensity of manual handling, improving the level of automation in workshop logistics, and to a certain extent ensuring the timeliness of material supply to the production line and the continuous operation of the production process.

[0003] However, in real-world workshop environments, material handling tasks are typically characterized by a large number of tasks, complex types, unpredictable task generation times, varying levels of urgency, significant differences in material importance, and dynamic changes in transport path conditions. Furthermore, when multiple handling robots operate collaboratively in the same area, they may be affected by factors such as narrow passageways, intersection conflicts, temporary obstacles, personnel movement, and equipment obstructing aisles. If tasks are assigned solely based on the shortest distance or task generation order, it can easily lead to problems such as delayed response to urgent tasks, delayed delivery of critical materials, overloading of some robots, prolonged idleness of some robots, and concentrated congestion along transport paths. These issues ultimately impact overall workshop logistics efficiency, continuous equipment operation, and timely completion of production tasks.

[0004] In recent years, scholars both domestically and internationally have conducted research on multi-robot task allocation, AGV scheduling, and ant colony algorithm optimization, for example: Existing public documents "S. Wang, Y. Liu, Y. Qiu, Q. Zhang, F. Huo, Y. Huangfu, C. Yang and J. Zhou, "Cooperative Task Allocation for Multi-Robot Systems Based on Multi-Objective Ant Colony System," in IEEE AccessThe paper, "Multi-robot Cooperative Task Allocation Method Based on Multi-Objective Ant Colony System," published in 2022 (vol. 10, pp.56375-56387), proposes a method for multi-robot cooperative task allocation. First, the multi-robot task allocation problem is modeled as a multi-objective multi-traveling salesman problem. Then, a multi-objective ant colony system is used to search for task allocation schemes. Finally, the task allocation result is obtained with the objectives of reducing the total robot cost, maximizing the cost, and balancing the robot workload. However, this method mainly focuses on path cost and load balancing in multi-robot task allocation, and does not adequately consider the task urgency, waiting time, material importance level, and aisle congestion in shop floor handling tasks.

[0005] The existing public document "C. Wu, Y. Xiao and X. Zhu, "Research on OptimizationAlgorithm of AGV Scheduling for Intelligent Manufacturing Company: Taking theMachining Shop as an Example," in Processes The paper, "[Title of paper](vol. 11, no. 9, article 2606, 2023)," proposes an optimization algorithm for AGV scheduling in the machining workshop of an intelligent manufacturing enterprise. First, an optimization model for intelligent manufacturing workshop scheduling considering AGV path planning is established. Then, the heuristic information and pheromone evaporation methods are improved in the ant colony algorithm, and path optimization is combined with the dynamic window method. Finally, the optimization of AGV scheduling paths and the improvement of operational smoothness are achieved. However, this method focuses more on AGV path optimization and scheduling path smoothing, and does not adequately consider the comprehensive relationship between the urgency of different handling tasks, waiting time, material importance level, and multi-robot load balancing.

[0006] In summary, the existing technology has the following shortcomings: 1. Existing task allocation methods for handling robots mainly focus on transportation distance, path cost, or task completion time as optimization objectives, while not giving sufficient consideration to task urgency, task waiting time, and material importance, which can easily lead to delays in response to critical tasks. 2. Existing methods do not adequately consider channel congestion during the task allocation phase, which can easily lead to multiple handling robots choosing congested paths, increasing waiting time and the risk of path conflicts. 3. Existing methods lack effective constraints on multi-robot task load balancing, which can easily lead to some robots having too many tasks while others are idle, reducing the utilization rate of the robot group. 4. In the scenario of task allocation in workshop transportation, the traditional ant colony algorithm has a relatively simple task selection probability and pheromone update mechanism, which makes it difficult to simultaneously take into account transportation efficiency, task priority, path access status and robot load balancing. Summary of the Invention

[0007] The technical problem to be solved by the present invention is the deficiency of the prior art. The present invention provides a task allocation method for handling robots. By introducing a comprehensive task priority, a channel congestion coefficient and a robot load balancing factor into the ant colony algorithm, the task allocation process of handling robots simultaneously considers transportation distance, task importance, path congestion status and robot load status, thereby improving the rationality of multi-robot task allocation in the workshop and the efficiency of logistics scheduling.

[0008] The technical solution adopted in this invention is as follows.

[0009] A method for task allocation in a handling robot includes the following steps: Step 1: Obtain the set of handling robots R and the set of handling tasks in the workshop. T : R = { R 1 ,R 2 ,...,R j ,...,R m}, T ={ T 1 ,T 2 ,...,T i ,...,T n}, in, m The total number of transport robots, R j For the first j Taiwanese transport robot n The total number of transport tasks. T i For the first i One transport task; j∈m , i∈n ; Step 2: Obtain task attribute information for each handling task. This task attribute information includes task urgency, task waiting time, and material importance level. Based on this task attribute information, construct a comprehensive priority for each handling task. The first... i The overall priority of each handling task is denoted as: P i ; Step 3: Obtain the initial transport distance from each transport robot to each transport task point and the transfer distance between different transport task points, and construct robot-task distance matrices respectively. D and task transfer distance matrix L , , , For the first j The current position of the transport robot is [number]. i The transportation distance between each transport task point For the first i The first transport task point and the first k The transfer distance between each transport task point k≠i ; Step 4: Obtain the channel congestion coefficient for each handling task's corresponding transportation path, and characterize the traffic flow status of the transportation path corresponding to the handling task based on the channel congestion coefficient; wherein, the first... i The channel congestion coefficient for each transport task is denoted as: ; Step 5: Initialize the improved ant colony algorithm parameters and the robot-task pheromone matrix. τ , , For the first j Taiwan handling robot selection number i The pheromone concentration for each transport task; the parameters of the improved ant colony algorithm include the number of ants, maximum number of iterations, pheromone influence factor, distance heuristic factor, task priority factor, congestion suppression factor, pheromone volatility coefficient, and pheromone intensity coefficient; Step 6: Obtain the optimal task allocation scheme for the transport robot based on the improved ant colony algorithm. Specifically, in each iteration, for the set of unallocated transport tasks, comprehensively consider pheromone concentration, transport distance, task priority, channel congestion coefficient and robot load balancing factor to calculate the probability of the transport robot selecting a transport task, and determine the robot-task matching relationship based on the task selection probability. The selected transport task is added to the task execution sequence of the corresponding transport robot until all transport tasks are allocated, generating a multi-robot multi-task execution sequence. Then, based on the multi-robot multi-task execution sequence, a task allocation evaluation function is constructed, and the pheromone matrix is ​​updated using a pheromone update mechanism guided by task comprehensive priority. The task selection, scheme evaluation, and pheromone update process are repeated until the maximum number of iterations is reached or the convergence condition is met, thus obtaining the optimal handling robot task allocation scheme, that is, the handling robot task allocation scheme with the minimum total cost.

[0010] Preferably, the handling task includes at least one of the following: material transfer task, empty-load recovery task, semi-finished product transfer task, finished product warehousing task, and production line replenishment task.

[0011] Preferably, the process of constructing the comprehensive priority of each handling task in step 2 is as follows: The urgency of a task is determined based on production process requirements, order delivery time, equipment shortage status, or task deadline; the waiting time for a task is determined based on the waiting time between the creation of the task and the current scheduling time; and the importance of materials is classified based on the degree of impact of the transported materials on the continuity of workshop production. Let the first i The urgency of each transport task is: The task waiting time is Material importance level is ; Regarding the urgency of the task Task waiting time Material importance level After normalization, the expressions are as follows:

[0012] in, , , These are the normalized task urgency, task waiting time, and material importance level, respectively. , , They are respectively n The maximum values ​​of task urgency, task waiting time, and material importance level in a single handling task; Based on the normalization results, the first... i Overall priority of each transport task P i :

[0013] in, These are the normalized task urgency levels. Task waiting time Material importance level The weighting coefficients.

[0014] Preferably, the robot-task distance matrix described in step 3 D and task transfer distance matrix L The applicable method is as follows: when the handling robot performs its first handling task, the robot-task distance matrix is ​​used.D Calculate the transport distance between the current position of the handling robot and the handling task point; when the handling robot performs multiple handling tasks consecutively, use the task transfer distance matrix. L Calculate the transport distance between adjacent handling task points.

[0015] Preferably, the probability of the transport robot selecting a transport task in step 6 is:

[0016] in, For the first Taiwan handling robot selection number The probability of a transport task. As the pheromone influencing factor, As a distance heuristic factor, As a task priority factor, As a congestion-inhibiting factor, For the first The load balancing factor of the transport robot. This is the set of currently unassigned transport tasks. h The number of the transport task in the current unassigned task set. For the first Taiwan handling robot selection number h The pheromone concentration of an unassigned transport task For the first The current position of the transport robot is [number]. h The transport distance of each unassigned transport task point For the first h The overall priority of each unassigned handling task. For the first h The congestion coefficient of each unassigned transport task channel.

[0017] Preferably, the first Load balancing factor of the handling robot According to the The number of tasks currently assigned to a handling robot is determined to reduce the probability that a handling robot with a large number of assigned tasks will receive new handling tasks, and to avoid multiple handling tasks being concentrated on the same handling robot.

[0018] Preferably, in step 6, the method for determining the robot-task matching relationship based on the task selection probability is to use a roulette wheel method. Then, the selected handling task is added to the task execution sequence of the corresponding handling robot, and the handling task is deleted from the unassigned task set. The task selection process is repeated until all handling tasks are assigned.

[0019] Preferably, the task allocation evaluation function in step 6 is:

[0020] in, F The total cost of the task allocation scheme. The total transport distance. For the overall cost of congestion, For task priority benefits, This is a penalty for robot task load balancing. Congestion penalty coefficient Priority reward coefficient And robot task load balancing penalty item The weighting coefficients.

[0021] Preferably, the pheromone update mechanism guided by the comprehensive task priority in step 6 includes pheromone evaporation and incremental pheromone update, wherein the pheromone evaporation formula is:

[0022] in, t For the first Taiwan handling robot selection number The current moment of a transport task t +1 is the first Taiwan handling robot selection number The next moment of the transportation task The pheromone evaporation coefficient; The pheromone increment is expressed as:

[0023] in, For the first Taiwan handling robot selection number The pheromone increment of a transport task The pheromone intensity coefficient. For the first The overall priority of each transport task. F t The total cost of the task allocation scheme at the current moment.

[0024] The beneficial effects of this invention include: 1. This invention constructs a comprehensive task priority model consisting of task urgency, task waiting time, and material importance level, enabling the task allocation process of the handling robot to reflect the importance and urgency of production tasks, thus avoiding the problem of delayed response to critical tasks caused by traditional methods that only allocate tasks based on transportation distance or task order.

[0025] 2. This invention introduces a channel congestion coefficient into the task selection probability, and reduces the probability of selecting tasks on highly congested paths by using a congestion suppression factor. This enables the task allocation scheme to proactively reduce the tendency to select tasks on congested paths, reduce robot waiting and path conflicts, and improve the efficiency of logistics passage in the workshop.

[0026] 3. This invention introduces a robot load balancing factor and a load balancing penalty term to make the task allocation among multiple handling robots more balanced, avoid the task being concentrated on a small number of robots, and improve the overall utilization and operational stability of the handling robot group.

[0027] 4. This invention adopts a pheromone update mechanism guided by task comprehensive priority, which enables high-priority tasks to obtain stronger pheromone reinforcement in subsequent iterations, thereby increasing the probability of key tasks being prioritized and enhancing the adaptability of the ant colony algorithm in the dynamic task allocation scenario in the workshop.

[0028] 5. The task allocation evaluation function of this invention comprehensively considers the total transportation distance, path congestion cost, task priority benefit and load balancing degree, and can output a task allocation scheme for handling robots with lower total cost, more reasonable task response and more efficient resource utilization. Attached Figure Description

[0029] Figure 1 This is an overall block diagram of the task allocation method of the present invention; Figure 2 This is a flowchart of the task overall priority calculation process in this invention; Figure 3 This is a flowchart of the probability calculation process for congestion perception task selection in this invention; Figure 4 This is a flowchart of the iterative optimization process of the improved ant colony algorithm in this invention.

[0030] Figure 5 This is a simplified flowchart of the task allocation method of the present invention. Detailed Implementation

[0031] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0032] Figure 1 This is an overall block diagram of the task allocation method for workshop handling robots based on the improved ant colony algorithm of this invention. Figure 2 This is a flowchart of the task overall priority calculation in this invention. Figure 3 This is a flowchart of the congestion perception task selection probability calculation in this invention. Figure 4 This is a flowchart of the iterative optimization process of the improved ant colony algorithm in this invention. Figure 5 This is a simplified flowchart of the task allocation method of the present invention. Figures 1 to 5 As can be seen, the task allocation method for a handling robot of the present invention includes the following steps: Step 1: Obtain the set of handling robots R and the set of handling tasks in the workshop. T : R = { R 1 ,R 2 ,...,R j ,...,R m}, T ={ T 1 ,T 2 ,...,T i ,...,T n}, in, m The total number of transport robots, R j For the first j Taiwanese transport robot n The total number of transport tasks. T i For the first i One transport task; j∈m , i∈n .

[0033] In this embodiment, the handling task includes at least one of the following: material transfer task, empty-load recovery task, semi-finished product transfer task, finished product warehousing task, and production line replenishment task.

[0034] Step 2: Obtain task attribute information for each handling task. This task attribute information includes task urgency, task waiting time, and material importance level. Based on this task attribute information, construct a comprehensive priority for each handling task. The first... i The overall priority of each handling task is denoted as: P i .

[0035] In this embodiment, the process of constructing the overall priority of each handling task is as follows: The urgency of a task is determined based on production process requirements, order delivery time, equipment shortage status, or task deadline; the waiting time for a task is determined based on the waiting time between the creation of the task and the current scheduling time; and the importance of materials is classified based on the degree of impact of the transported materials on the continuity of workshop production. Let the first i The urgency of each transport task is: The task waiting time is Material importance level is ; Regarding the urgency of the task Task waiting time Material importance level After normalization, the expressions are as follows:

[0036] in, , , These are the normalized task urgency, task waiting time, and material importance level, respectively. , , They are respectively n The maximum values ​​of task urgency, task waiting time, and material importance level in a single handling task; Based on the normalization results, the first... i Overall priority of each transport task P i :

[0037] in, These are the normalized task urgency levels. Task waiting time Material importance level The weighting coefficients.

[0038] Step 3: Obtain the initial transport distance from each transport robot to each transport task point and the transfer distance between different transport task points, and construct robot-task distance matrices respectively. D and task transfer distance matrix L , , , For the first j The current position of the transport robot is [number]. i The transportation distance between each transport task point For the first i The first transport task point and the first k The transfer distance between each transport task point k≠i .

[0039] In this embodiment, the robot-task distance matrix D and task transfer distance matrix L The applicable method is as follows: when the handling robot performs its first handling task, the robot-task distance matrix is ​​used. DCalculate the transport distance between the current position of the handling robot and the handling task point; when the handling robot performs multiple handling tasks consecutively, use the task transfer distance matrix. L Calculate the transport distance between adjacent handling task points.

[0040] Step 4: Obtain the channel congestion coefficient for each handling task's corresponding transportation path, and characterize the traffic flow status of the transportation path corresponding to the handling task based on the channel congestion coefficient; wherein, the first... i The channel congestion coefficient for each transport task is denoted as: .

[0041] Step 5: Initialize the improved ant colony algorithm parameters and the robot-task pheromone matrix. τ , , For the first j Taiwan handling robot selection number i The pheromone concentration for each transport task; the parameters of the improved ant colony algorithm include the number of ants, maximum number of iterations, pheromone influence factor, distance heuristic factor, task priority factor, congestion suppression factor, pheromone volatility coefficient, and pheromone intensity coefficient.

[0042] Step 6: Obtain the optimal task allocation scheme for the transport robot based on the improved ant colony algorithm. Specifically, in each iteration, for the set of unallocated transport tasks, comprehensively consider pheromone concentration, transport distance, task priority, channel congestion coefficient and robot load balancing factor to calculate the probability of the transport robot selecting a transport task, and determine the robot-task matching relationship based on the task selection probability. The selected transport task is added to the task execution sequence of the corresponding transport robot until all transport tasks are allocated, generating a multi-robot multi-task execution sequence. Then, based on the multi-robot multi-task execution sequence, a task allocation evaluation function is constructed, and the pheromone matrix is ​​updated using a pheromone update mechanism guided by task comprehensive priority. The task selection, scheme evaluation, and pheromone update process are repeated until the maximum number of iterations is reached or the convergence condition is met, thus obtaining the optimal handling robot task allocation scheme, that is, the handling robot task allocation scheme with the minimum total cost.

[0043] In this embodiment, the probability of the transport robot selecting a transport task in step 6 is:

[0044] in, For the first Taiwan handling robot selection number The probability of a transport task. As the pheromone influencing factor, As a distance heuristic factor, As a task priority factor, As a congestion-inhibiting factor, For the first The load balancing factor of the transport robot. This is the set of currently unassigned transport tasks. h The number of the transport task in the current unassigned task set. For the first Taiwan handling robot selection number h The pheromone concentration of an unassigned transport task For the first The current position of the transport robot is [number]. h The transport distance of each unassigned transport task point For the first h The overall priority of each unassigned handling task. For the first h The congestion coefficient of each unassigned transport task channel.

[0045] The first Load balancing factor of the handling robot According to the The number of tasks currently assigned to a handling robot is determined to reduce the probability that a handling robot with a large number of assigned tasks will receive new handling tasks, and to avoid multiple handling tasks being concentrated on the same handling robot.

[0046] In this embodiment, the method for determining the robot-task matching relationship based on the task selection probability in step 6 is to use a roulette wheel method. Then, the selected handling task is added to the task execution sequence of the corresponding handling robot, and the handling task is deleted from the unassigned task set. The task selection process is repeated until all handling tasks are assigned.

[0047] In this embodiment, the task allocation evaluation function in step 6 is:

[0048] in, F The total cost of the task allocation scheme. The total transport distance. For the overall cost of congestion, For task priority benefits, This is a penalty for robot task load balancing. Congestion penalty coefficient Priority reward coefficient And robot task load balancing penalty item The weighting coefficients.

[0049] In this embodiment, the pheromone update mechanism guided by task priority in step 6 includes pheromone evaporation and incremental pheromone update. The pheromone evaporation formula is:

[0050] Where t is the th Taiwan handling robot selection number The current time of the transport task, t+1 is the time of the transport task. Taiwan handling robot selection number The next moment of the transportation task The pheromone evaporation coefficient; The pheromone increment is expressed as:

[0051] in, For the first Taiwan handling robot selection number The pheromone increment of a transport task The pheromone intensity coefficient. For the first The overall priority of each transport task. F t The total cost of the task allocation scheme at the current moment.

[0052] In this embodiment, by incorporating task priority, channel congestion coefficient, and load balancing factor into the traditional ant colony algorithm, the task allocation result for the handling robot can take into account the urgency of production tasks, path accessibility, and robot resource utilization. Compared with task allocation methods that only consider distance or task order, this invention can reduce waiting time for urgent tasks, decrease the probability of selecting tasks on highly congested paths, and improve the efficiency of multi-robot collaborative handling, making it suitable for intelligent task scheduling in complex logistics scenarios in workshops.

Claims

1. A task allocation method for a handling robot, characterized in that, Includes the following steps: Step 1: Obtain the set of handling robots R and the set of handling tasks in the workshop. T : R = { R 1 ,R 2 ,...,R j ,...,R m}， T ={ T 1 ,T 2 ,...,T i ,...,T n}， in, m The total number of transport robots, R j For the first j Taiwanese transport robot n The total number of transport tasks. T i For the first i One transport task; j∈m , i∈n ; Step 2: Obtain task attribute information for each handling task. This task attribute information includes task urgency, task waiting time, and material importance level. Based on this task attribute information, construct a comprehensive priority for each handling task. The first... i The overall priority of each handling task is denoted as: P i ; Step 3: Obtain the initial transport distance from each transport robot to each transport task point and the transfer distance between different transport task points, and construct robot-task distance matrices respectively. D and task transfer distance matrix L , , , For the first j The current position of the transport robot is [number]. i The transport distance between each transport task point For the first i The first transport task point and the first k The transfer distance between each transport task point k≠i ; Step 4: Obtain the channel congestion coefficient for each handling task's corresponding transportation path, and characterize the traffic flow status of the transportation path corresponding to the handling task based on the channel congestion coefficient; wherein, the first... i The channel congestion coefficient for each transport task is denoted as: ; Step 5: Initialize the improved ant colony algorithm parameters and the robot-task pheromone matrix. τ , , For the first j Taiwan handling robot selection number i The pheromone concentration for each transport task; the parameters of the improved ant colony algorithm include the number of ants, maximum number of iterations, pheromone influence factor, distance heuristic factor, task priority factor, congestion suppression factor, pheromone volatility coefficient, and pheromone intensity coefficient; Step 6: Obtain the optimal task allocation scheme for the transport robot based on the improved ant colony algorithm. Specifically, in each iteration, for the set of unallocated transport tasks, the probability of the transport robot selecting a transport task is calculated by comprehensively considering pheromone concentration, transport distance, overall task priority, channel congestion coefficient and robot load balancing factor. The robot-task matching relationship is determined according to the task selection probability. The selected transport task is added to the task execution sequence of the corresponding transport robot until all transport tasks are allocated, generating a multi-robot multi-task execution sequence. Then, based on the multi-robot multi-task execution sequence, a task allocation evaluation function is constructed, and the pheromone matrix is ​​updated using a pheromone update mechanism guided by task comprehensive priority. The task selection, scheme evaluation, and pheromone update process are repeated until the maximum number of iterations is reached or the convergence condition is met, thus obtaining the optimal handling robot task allocation scheme, that is, the handling robot task allocation scheme with the minimum total cost.

2. The task allocation method for a handling robot according to claim 1, characterized in that, The handling tasks include at least one of the following: material transfer tasks, empty-load recovery tasks, semi-finished product transfer tasks, finished product warehousing tasks, and production line replenishment tasks.

3. The task allocation method for a handling robot according to claim 1, characterized in that, The process of constructing the overall priority of each handling task in step 2 is as follows: The urgency of a task is determined based on production process requirements, order delivery time, equipment shortage status, or task deadline; the waiting time for a task is determined based on the waiting time between the creation of the material handling task and the current scheduling time; and the importance of materials is classified based on the degree of impact of the material handling on the continuity of workshop production. Let the first i The urgency of each transport task is: The task waiting time is Material importance level is ; Regarding the urgency of the task Task waiting time Material importance level After normalization, the expressions are as follows: in, , , These are the normalized task urgency, task waiting time, and material importance level, respectively. , , They are respectively n The maximum values ​​of task urgency, task waiting time, and material importance level in a single handling task; Based on the normalization results, the first... i Overall priority of each transport task P i : in, These are the normalized task urgency levels. Task waiting time Material importance level The weighting coefficients.

4. The task allocation method for a handling robot according to claim 1, characterized in that, The robot-task distance matrix described in step 3 D and task transfer distance matrix L The applicable method is as follows: when the handling robot performs its first handling task, the robot-task distance matrix is ​​used. D Calculate the transport distance between the current position of the handling robot and the handling task point; when the handling robot performs multiple handling tasks consecutively, use the task transfer distance matrix. L Calculate the transport distance between adjacent handling task points.

5. The task allocation method for a handling robot according to claim 1, characterized in that, The probability that the transport robot selects a transport task in step 6 is: in, For the first Taiwan handling robot selection number The probability of a transport task. As the pheromone influencing factor, As a distance heuristic factor, As a task priority factor, As a congestion-inhibiting factor, For the first The load balancing factor of the transport robot. This is the set of currently unassigned transport tasks. h The number of the transport task in the current unassigned task set. For the first Taiwan handling robot selection number h The pheromone concentration of an unassigned transport task For the first The current position of the transport robot is [number]. h The transport distance of each unassigned transport task point For the first h The overall priority of each unassigned handling task. For the first h The congestion coefficient of each unassigned transport task.

6. The task allocation method for a handling robot according to claim 5, characterized in that, The first Load balancing factor of the handling robot According to the The number of tasks currently assigned to a handling robot is determined to reduce the probability of a handling robot with a large number of assigned tasks receiving new handling tasks, and to avoid multiple handling tasks being concentrated on the same handling robot.

7. The task allocation method for a handling robot according to claim 1, characterized in that, In step 6, the robot-task matching relationship is determined by a roulette wheel method. The selected handling task is then added to the task execution sequence of the corresponding handling robot, and the handling task is deleted from the unassigned task set. The task selection process is repeated until all handling tasks are assigned.

8. The task allocation method for a handling robot according to claim 1, characterized in that, The task allocation evaluation function described in step 6 is: in, F The total cost of the task allocation scheme. The total transport distance. For the overall cost of congestion, For task priority benefits, This is a penalty for robot task load balancing. Congestion penalty coefficient Priority reward coefficient And robot task load balancing penalty item The weighting coefficients.

9. A task allocation method for a handling robot according to claim 8, characterized in that, Step 6 describes a pheromone update mechanism guided by task priority, which includes pheromone evaporation and incremental pheromone updates. The pheromone evaporation formula is as follows: in, t For the first Taiwan handling robot selection number The current moment of a transport task t +1 is the first Taiwan handling robot selection number The next moment of the transportation task The pheromone evaporation coefficient; The pheromone increment is expressed as: in, For the first Taiwan handling robot selection number The pheromone increment of a transport task The pheromone intensity coefficient. For the first The overall priority of each transport task. F t The total cost of the task allocation scheme at the current moment.

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