CN114240076B - Multi-AGV task allocation method based on improved particle swarm algorithm - Google Patents

Multi-AGV task allocation method based on improved particle swarm algorithm Download PDF

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CN114240076B
CN114240076B CN202111421120.5A CN202111421120A CN114240076B CN 114240076 B CN114240076 B CN 114240076B CN 202111421120 A CN202111421120 A CN 202111421120A CN 114240076 B CN114240076 B CN 114240076B
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徐菱
于童
周军
龙羽
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Abstract

The invention discloses a multi-AGV task allocation method based on an improved particle swarm algorithm, which is used for solving the problem of AGV task allocation in an intelligent processing and manufacturing workshop and realizing overall scheduling optimization of multiple AGVs. The method mainly comprises the following steps: firstly, an objective function is set according to an objective with the highest overall efficiency of the AGV. And then designing a fitness calculation formula based on actual scene parameters of the manufacturing shop, encoding the particles in a real number encoding mode, and setting an initial solution checking operator according to a target function to generate a high-quality initial solution. And finally, updating the particles according to a speed and position formula, and setting an update solution checking operator to avoid the degradation of the solution. And comparing the individual optimal position with the global optimal position, and stopping iteration when the algorithm is finished to obtain the AGV optimal task distribution result.

Description

Multi-AGV task allocation method based on improved particle swarm algorithm
Technical Field
The invention relates to the field of multi-robot task allocation, in particular to a multi-AGV task allocation method based on an improved particle swarm algorithm.
Background
With the continuous development of intelligent robot technology, more and more processing workshops and logistics warehouses are transformed to intellectualization, and the robot agent is used for manually bearing tasks such as automatic carrying, loading and unloading, so that the problem of how to coordinate the work division cooperation of a plurality of robots becomes an important research problem, and the proper task allocation scheme can reduce the traveling distance of the AGV on the whole so as to improve the working efficiency.
At present, aiming at multi-AGV task allocation, a centralized mode and a distributed mode are mainly adopted, wherein the centralized mode is that the multi-AGV task allocation is allocated to each robot after a scheme is calculated, the advantage is that information can be integrated uniformly, communication service cost is reduced, AGV task allocation belongs to an NP-hard problem, and the calculation amount is increased explosively along with the increase of the number of AGVs and the number of tasks, so that the calculation is generally performed by adopting an intelligent algorithm, wherein a particle swarm algorithm is a meta-heuristic algorithm and is also one of main research algorithms of group intelligence at present, a particle swarm is far away from a foraging rule of a cluster of flying birds, each flying bird in the cluster is a particle representing solution, the particle flying space flies at a certain speed and direction, the speed and the direction are continuously adjusted according to information of an optimal solution individual and a global individual shared in the cluster, the optimal direction is approached, the cluster is gradually approached to the optimal solution in the solution space, however, in the existing multi-AGV task allocation, although the algorithm is used, the stability is poor, the optimizing speed is slow, the solving quality is low, and the particle swarm is not suitable for the existing production requirements.
Disclosure of Invention
Aiming at the problems, the invention provides the multi-AGV task allocation method based on the improved particle swarm optimization, which has the advantages of high stability, capability of improving the algorithm searching efficiency, optimizing capability and calculation stability, and capability of improving the solving quality.
The technical scheme of the invention is as follows:
a multi-AGV task allocation method based on an improved particle swarm algorithm comprises the following steps:
s1, acquiring AGV information and task information, performing task matching on a to-be-delivered goods loading station and a to-be-emptied tray processing station, and setting particle swarm parameters;
s2, encoding each particle;
s3, setting an initial operator for checking each encoded particle;
s4, decoding the checked qualified particles to generate a driving path corresponding to the AGV, and calculating the fitness of each particle according to a fitness function;
s5, determining the optimal position of each particle according to the fitness of each particle, and simultaneously determining the optimal position in a particle population;
and S6, updating the speed and the position of each particle to obtain an individual optimal solution and a global optimal solution of each particle.
In the S1, the processing station number, each AGV station number and a two-dimensional plane rectangular coordinate system thereof are determined according to a manufacturing workshop map on the premise of acquiring the AGV information and the task condition, and the AGV road length and the road running rule under the map scene are determined.
In S1, the AGV information comprises the total number of vehicles, the number of the vehicles, the initial station of each vehicle and the electric quantity, the AGV task comprises the task information of waiting for goods delivery and the task information of waiting for empty goods taking, and the AGV numbers according to the natural number sequence.
In S1, the particle swarm parameters include: c. C 1 ，c 2 Is an acceleration constant, r 1 ，r 2 Is a random parameter, w is an inertia weight, q is the number of particles, m is the number of iterations, and d is the particle dimension, which is equal to the number of tasks to be picked.
In the S1, before task matching is carried out on the loading position to be delivered and the processing position of the tray to be emptied, the relation between the number of tasks to be delivered and the number of AGV needs to be judged, and if the number of tasks to be delivered is more than or equal to the number of AGV, direct matching is carried out; if the number of the tasks to be delivered is smaller than the number of the AGVs, starting from the AGV with the largest electric quantity, keeping the AGVs corresponding to the number of the tasks to be delivered in the sequence from large to small, and continuing to match, wherein the matching calculates a function according to the running distance to obtain a task combination with the smallest running distance of the total AGVs, namely
Figure GDA0003837109750000031
If the number of the tasks of the trays to be emptied is smaller than that of the tasks of the pallets to be delivered, the fact that the trays are emptied after the existing AGV does not need to deliver the pallets is indicated, and the distance is replaced by the distance from the loading station to be loaded to the AGV station.
The particle coding adopts a real number coding mode.
In S5, the fitness of the current position of each particle is compared with the historical optimal position fitness, and if the current position fitness value is lower, the historical optimal position is updated. Similarly, the best position fitness in the group is compared with the historical group best position fitness, and if the current position fitness value is lower, the historical best position of the group is updated.
In S6, updating the velocity and position of each particle depends on a velocity and position formula, which is as follows:
Figure GDA0003837109750000032
Figure GDA0003837109750000033
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003837109750000034
a d-dimension component representing a velocity vector of a particle i at the k-th iteration;
Figure GDA0003837109750000035
a d-dimension component representing a location vector of a particle i at the k-th iteration; w represents an inertial weight; c. C 1 ,c 2 Represents an acceleration constant; r is 1 ,r 2 Representing a random parameter; pbest id D-dimension individual optimal value representing particle i; gbest id Representing a d-th dimension global optimum.
And S6, in order to prevent the degradation of the solution, checking whether each particle after updating contains all AGV numbers needing to execute the task by using an updating solution checking operator, if any particle does not meet the requirement, keeping the particle before updating, if the algorithm ending condition is not met, returning to S4 to continue iterative computation, if the algorithm ending condition is met, and the optimal position of the group is the optimal solution obtained by the algorithm.
The invention has the beneficial effects that:
a real number encoding mode which is easier to understand and calculate is adopted in the particle swarm algorithm encoding, so that particle optimization in subsequent steps is facilitated. The particles of the particle swarm algorithm move in the solution space towards the directions of the population optimal solution and the individual optimal solution, so that the population optimal solution has an important influence on the update of the particles. Based on the characteristics, the objective is solved by combining the task allocation problem, considering that the optimal solution is that each AGV has task allocation, designing an initial solution inspection operator to generate a high-quality initial solution containing all AGV numbers, and determining a good search direction for the particles in a solution space at the beginning of the algorithm; meanwhile, an updated particle inspection operator is designed to process irregular particles generated in the algorithm iteration process, so that degradation of the solution is avoided. The improved particle swarm optimization can improve the searching efficiency, optimizing capability and calculation stability of the algorithm and improve the solving quality as a whole.
Drawings
FIG. 1 is a flowchart of a method for distributing multiple AGV tasks according to an embodiment of the present invention;
FIG. 2 is a flowchart of an initial solution checking operator of the method for distributing multiple AGV tasks based on the improved particle swarm optimization algorithm according to the embodiment of the present invention;
FIG. 3 is a flowchart of an update solution checking operator of the AGV task allocation method based on the improved particle swarm optimization algorithm according to the embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be further described with reference to the accompanying drawings.
The embodiment is as follows:
as shown in fig. 1-2, a multiple AGV task allocation method based on an improved particle swarm algorithm includes the following steps:
s1, acquiring AGV information and task information, performing task matching on a to-be-delivered loading station and a to-be-emptied tray processing station, and setting particle swarm parameters;
s2, encoding each particle;
s3, setting an initial operator for checking each encoded particle;
s4, decoding the checked qualified particles to generate a driving path corresponding to the AGV, and calculating the fitness of each particle according to a fitness function;
s5, determining the optimal position of each particle according to the fitness of each particle, and simultaneously determining the optimal position in a particle population;
and S6, updating the speed and the position of each particle to obtain an individual optimal solution and a global optimal solution of each particle.
The calculation of the algorithm fitness function is based on map scene parameters, the travel distance of the AGV between the station and the processing station and the travel distance between the AGV and the processing station are determined according to the road travel rule to determine the calculation function, and finally the total travel distance L of each AGV (i =1,2, …, n) is obtained through summation i Wherein the maximum travel distance of a certain AGVFor fitness, the algorithm aims to obtain a task allocation scheme with the lowest fitness. The multi-AGV task allocation algorithm aims to achieve the balance of the AGV tasks and achieve the overall optimization. The objective function is the AGV travel distance that minimizes the longest travel distance, i.e., min (max (L) i ))，i＝1,2,…,n。
In the S1, the processing station number, each AGV station number and a two-dimensional plane rectangular coordinate system thereof are determined according to a manufacturing workshop map on the premise of acquiring the AGV information and the task condition, and the AGV road length and the road running rule under the map scene are determined.
In S1, the AGV information comprises the total number of vehicles, the number of the vehicles, the initial station of each vehicle and the electric quantity, the AGV task comprises the task information of waiting for goods delivery and the task information of waiting for empty goods taking, and the AGV numbers according to the natural number sequence.
In S1, the particle swarm parameters include: c. C 1 ，c 2 Is an acceleration constant, r 1 ，r 2 Is a random parameter, w is an inertia weight, q is the number of particles, m is the number of iterations, and d is the particle dimension, which is equal to the number of tasks to pick.
In the S1, according to the sequence of the tasks executed by the AGV, the AGV starts from the initial platform to the processing station for delivering goods, then arrives at the processing station where the empty tray needs to be taken, and finally returns to the initial platform. In order to achieve the target of minimum total distance, the relation between the number of tasks to be delivered and the number of AGV needs to be judged before task matching is carried out on the processing position of the tray to be delivered and the processing position of the tray to be emptied, and if the number of tasks to be delivered is more than or equal to the number of AGV, matching is directly carried out; if the number of the tasks to be delivered is smaller than the number of the AGVs, starting from the AGV with the largest electric quantity, keeping the AGVs corresponding to the number of the tasks to be delivered in the sequence from large to small, and continuing to match, wherein the matching calculates a function according to the running distance to obtain a task combination with the smallest running distance of the total AGVs, namely
Figure GDA0003837109750000061
If the number of the tasks of the trays to be emptied is smaller than the number of the tasks to be delivered, the fact that the trays are emptied after the fact that the AGV does not need to deliver goods is indicated, and the distance is replaced by the distance from the loading station to be fetched to the AGV platform.
The particle coding adopts a real number coding mode, and the natural number rounded downwards is the AGV number. Each particle has dimension which is equal to the total number of tasks to be delivered, the real number at the position indicates that the tasks are executed by the AGV with the corresponding number, and the sequence of executing the tasks by the same AGV is determined by the sequence of arranging the real numbers from small to large. For example, the code for a particle is: [1.6523,2.3948,2.4721,3.4820,1.3922,3.3945] indicates that AGV1 performs task 5 before task 1, AGV2 performs task 2 before task 3, and AGV3 performs task 6 before task 3.
In S5, the fitness of the current position of each particle is compared with the historical best position fitness, and if the current position fitness value is lower, the historical best position is updated. Similarly, the best position fitness in the group is compared with the historical group best position fitness, and if the current position fitness value is lower, the historical best position of the group is updated.
In S6, updating the velocity and the position of each particle depends on a velocity and position formula, which is as follows:
Figure GDA0003837109750000071
Figure GDA0003837109750000072
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003837109750000073
a d-dimension component representing a velocity vector of a particle i at the k-th iteration;
Figure GDA0003837109750000074
a d-dimension component representing a k-th iteration particle i position vector; w represents an inertial weight; c. C 1 ,c 2 Represents an acceleration constant; r is 1 ,r 2 Representing a random parameter; pbest id D-dimension individual optimal value representing particle i; gbest id Representing a d-th dimension global optimum.
As shown in fig. 3, in step S6, in order to prevent degradation of the solution, an update solution checker is used to check whether each updated particle includes all AGV numbers that need to execute the task, if any particle does not satisfy the requirement, the particle before updating is retained, if the algorithm end condition is not satisfied, the iteration is returned to step S4 to continue the iteration calculation, and if the algorithm end condition is satisfied, the optimal position of the group is the optimal solution obtained by the algorithm.
Tables 1 and 2 below show the results of task allocation by using an unmodified particle swarm algorithm and an improved particle swarm algorithm respectively, the two algorithms use the same manufacturing workshop map as an example, and the route driving rule, the stations and the AGV station coordinates are known. The task allocation scenario is: there are 10 tasks to be delivered and 10 tasks to be emptied of pallets, and the number of the tasks to be delivered to the AGVs is: 2,5,10,16,17,31,46,50,66,70; the task number of the tray to be emptied is as follows: 7,19,22,38,49,51,60,80,82,89. Wherein AGV1-AGV3 all start from station 1, AGV4-AGV6 all start from station 2, return same station after finishing the task, wherein the algorithm parameter is: 40 particles, and 100 times of iterative calculation. The column of assigned tasks in the table is the chain of tasks assigned by each AGV calculated by the algorithm, wherein "[ ]" indicates the task to be delivered and the task to empty the tray executed by the AGV, and the connection with the next task is represented by "-". The total distance traveled column in the table is the distance the AGV traveled under the task chain.
Table 1 task assignment results using the prior art
Figure GDA0003837109750000081
TABLE 2 results of using the distribution method of the present invention
Figure GDA0003837109750000082
As can be seen from the table, the AGV running distances in the AGV task allocation scheme obtained through the improved particle swarm optimization are close, and the running distances are more balanced. In addition, the total travel distance of all AGVs in the task allocation scheme calculated by the improved particle swarm algorithm is 3916.4m, which is smaller than 3966m calculated by the prior art. And the maximum AGV driving distance in the task allocation scheme of the improved particle swarm algorithm is smaller. According to the algorithm running time, two algorithms are run on the same computer, the algorithm running time for obtaining the calculation results in the table 1 is 4.33s, the algorithm running time for obtaining the calculation results in the table 2 is 4.39s, and the calculation time is almost the same. By combining the comparative analysis of the calculation results, the improved particle swarm algorithm has higher solving quality in the same time, and the overall operation effect of the calculated multi-AGV task allocation scheme is better.
The above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention.

Claims (5)

1. A multi-AGV task allocation method based on an improved particle swarm algorithm is characterized by comprising the following steps:
s1, acquiring AGV information and task information, performing task matching on a to-be-delivered loading station and a to-be-emptied tray processing station, and setting particle swarm parameters;
s2, encoding each particle;
s3, setting an initial operator for checking each encoded particle;
s4, decoding the checked qualified particles to generate a driving path corresponding to the AGV, and calculating the fitness of each particle according to a fitness function;
s5, determining the optimal position of each particle according to the fitness of each particle, and simultaneously determining the optimal position in a particle population;
s6, updating the speed and the position of each particle to obtain an individual optimal solution and a global optimal solution of each particle,
the calculation of the algorithm fitness function is based on map scene parameters, and the running of the AGV between the station and the processing station and between the processing station and the processing station is determined according to the road running ruleDetermining a calculation function for the distance, and finally summing to obtain the total travel distance L of each AGV (i =1,2, …, n) i The maximum driving distance of a certain AGV is fitness, the objective of the algorithm is to obtain a task allocation scheme with the lowest fitness, the objective of the multi-AGV task allocation algorithm is to achieve the balance of the task quantity of each AGV and realize the overall optimization, and the objective function is to minimize the AGV driving distance with the longest driving distance, namely min (max (L) i ))，i＝1,2,…,n；
In the S1, the processing station numbers, the AGV station numbers and the two-dimensional plane rectangular coordinate system are determined according to a manufacturing workshop map on the premise of acquiring the AGV information and the task condition, and according to the AGV road length and the road running rule under the map scene, in the S1, the relation between the number of tasks to be delivered and the number of the AGV needs to be judged before task matching is carried out on the processing station of the pallet to be delivered and the processing station of the pallet to be emptied, and if the number of tasks to be delivered is more than or equal to the number of the AGV, direct matching is carried out; if the number of the tasks to be delivered is smaller than the number of the AGVs, starting from the AGV with the largest electric quantity, keeping the AGVs corresponding to the number of the tasks to be delivered in the sequence from large to small, and continuing to match, wherein the matching calculates a function according to the running distance to obtain a task combination with the smallest running distance of the total AGVs, namely
Figure FDA0003849570660000021
If the number of the tasks of the trays to be emptied is smaller than the number of the tasks to be delivered, the fact that the empty trays are taken after the fact that the AGV does not need to deliver goods is shown, the distance is replaced by the distance from the loading station to be taken to the AGV station, the particle codes adopt a real number coding mode, S6 is used for preventing the degradation of the solution, an updating solution checking operator is used for checking whether each particle after updating contains all AGV numbers needing to execute the tasks, if the particles do not meet the requirements, the particles before updating are reserved, if the algorithm ending conditions are not met, S4 is returned to continue iterative calculation, if the algorithm ending conditions are met, the group optimal position is the optimal solution obtained by the algorithm.
2. The improved particle swarm algorithm-based multiple AGV task allocation method according to claim 1, wherein in S1, AGV information includes total number of vehicles, vehicle number, starting station of each vehicle and electric quantity, AGV tasks include task information to be delivered and task information to be unloaded, and AGV numbers according to natural number sequence.
3. The method of claim 2, wherein in S1, the parameters of the particle swarm comprise: c. C 1 ，c 2 Is an acceleration constant, r 1 ，r 2 Is a random parameter, w is an inertia weight, q is the number of particles, m is the number of iterations, and d is the particle dimension, which is equal to the number of tasks to be picked.
4. The method of claim 1, wherein in step S5, the fitness of the current position of each particle is compared with the historical best position fitness, and if the current position fitness is lower, the historical best position is updated, and similarly, the best position fitness in the group is compared with the historical group best position fitness, and if the current position fitness is lower, the historical best position of the group is updated.
5. The method of claim 1, wherein in step S6, the updating of the speed and position of each particle depends on a speed and position formula, which is as follows:
Figure FDA0003849570660000031
Figure FDA0003849570660000032
wherein the content of the first and second substances,
Figure FDA0003849570660000033
represents the k-th iterationA d-dimension component of the velocity vector of the surrogate particle i;
Figure FDA0003849570660000034
a d-dimension component representing a location vector of a particle i at the k-th iteration; w represents an inertial weight; c. C 1 ,c 2 Represents an acceleration constant; r is a radical of hydrogen 1 ,r 2 Representing a random parameter; pbest id D-dimension individual optimal value representing particle i; gbest id Representing a d-th dimension global optimum.
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