CN114240076A - Multi-AGV task allocation method based on improved particle swarm algorithm - Google Patents
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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
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, centralized allocation and distributed allocation are mainly adopted, wherein the centralized 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, but the AGV task allocation belongs to NP-hard problem, the calculation amount is increased explosively along with the increase of the number of AGVs and the number of tasks, so the calculation is generally carried out by adopting an intelligent algorithm, wherein a particle swarm algorithm is a meta-heuristic algorithm and is also one of the main research algorithms of the current group intelligence, a particle swarm is far away from the foraging rule of a cluster of flying birds, each flying bird in the group is a particle representing a solution, the solution flies in a solution space at a certain speed and direction, the speed and the direction are adjusted continuously according to the information of the optimal solution individual and the global individual shared in the group, and the solution approaches to the optimal direction, the method has the advantages that the groups are gradually approached to the optimal solution in the solution space, but in the existing multi-AGV task allocation, although the example group algorithm is used, the algorithm is poor in stability, slow in optimization speed and low in solution quality, and 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 algorithm, which has the advantages of high stability, capability of improving the algorithm search efficiency, the optimization searching capability and the 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 loading station and a to-be-emptied tray processing station, and setting particle swarm parameters;
s2, encoding each particle;
s3, setting an initial solver to check each particle after being coded;
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 determining the optimal position in the 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 step S1, the AGV information and the task condition are acquired on the premise that the processing station number, each AGV station number and the two-dimensional plane rectangular coordinate system thereof are determined according to the manufacturing workshop map, and the AGV road length and the road driving rule under the map scene are determined.
In S1, AGV information includes total number of cars, vehicle number, the initial station and the electric quantity of every car, the AGV task includes waiting to deliver goods task information, waiting to get empty tray task information, the AGV numbers according to natural number order.
In S1, the particle swarm parameters include: c. C1，c2Is an acceleration constant, r1，r2Is 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 step 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 the tasks to be delivered and the number of the AGVs needs to be judged, and if the number of the tasks to be delivered is more than or equal to the number of the AGVs, the 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
If the number of the tasks of the tray to be emptied is less than the number of the tasks to be delivered, the AGV indicates that the existing AGV does not need to deliver the goodsAnd after the goods are unloaded, the tray is emptied, and the distance is replaced by the distance from the loading station to be loaded to the AGV platform.
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 best position fitness, and the historical best position is updated if the current position fitness value is lower. 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, the updating of the velocity and the position of each particle depends on a velocity and position formula, which is as follows:
wherein,
a d-dimension component representing a velocity vector of a particle i at the k-th iteration;
a d-dimension component representing a location vector of a particle i at the k-th iteration; w represents an inertial weight; c. C1,c2Represents an acceleration constant; r is1,r2Representing a random parameter; pbestidD-dimension individual optimal value representing particle i; gbestidRepresenting a d-th dimension global optimum.
And in order to prevent the degradation of the solution, the S6 checks 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, the particle before updating is reserved, if the algorithm ending condition is not met, the S4 is returned to continue iterative calculation, if the algorithm ending condition is met, the algorithm is ended, and the group optimal position 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 particle swarm optimization encoding, and 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.
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FIG. 1 is a flowchart of a method for distributing multiple AGV tasks based on an improved particle swarm algorithm according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an initial solution inspection operator of a multiple AGV task allocation method based on an improved particle swarm algorithm according to an embodiment of the present invention;
fig. 3 is a flowchart of an update solution verification operator of the multiple AGV task allocation method based on the improved particle swarm optimization 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.
Example (b):
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 solver to check each particle after being coded;
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 determining the optimal position in the 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 processing station and the processing station are determined according to road travel rules to determine a calculation function, and finally, the total travel distance L of each AGV (i is 1,2, …, n) is obtained through summationiAnd the maximum driving distance of a certain AGV is the fitness, and the algorithm target is to obtain the task allocation scheme with the lowest fitness. The multi-AGV task allocation algorithm aims to achieve balance of the AGV tasks and achieve overall optimization. The objective function is to minimize the AGV travel distance with the longest travel distance, minmaxLi，i＝1,2,…,n。
In the step S1, the AGV information and the task condition are acquired on the premise that the processing station number, each AGV station number and the two-dimensional plane rectangular coordinate system thereof are determined according to the manufacturing workshop map, and the AGV road length and the road driving rule under the map scene are determined.
In S1, AGV information includes total number of cars, vehicle number, the initial station and the electric quantity of every car, the AGV task includes waiting to deliver goods task information, waiting to get empty tray task information, the AGV numbers according to natural number order.
In S1, the particle swarm parameters include: c. C1，c2Is an acceleration constant, r1，r2Is 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 S1, according to the sequence of the AGV executing tasks, the AGV first starts from the initial platform to the processing station for delivering goods, then starts to the processing station requiring emptying, and finally returns to the processing stationReturning to the initial station. 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
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, is a real number, represents the maximum serial number of the AGV, and takes an integer downwards to be the serial number of the AGV. 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], shows that the AGV1 performs task 5 before task 1, the AGV2 performs task 2 before task 3, and the 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 the historical best position is updated if the current position fitness value is lower. 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, the updating of the velocity and the position of each particle depends on a velocity and position formula, which is as follows:
wherein,
a d-dimension component representing a velocity vector of a particle i at the k-th iteration;
a d-dimension component representing a location vector of a particle i at the k-th iteration; w represents an inertial weight; c. C1,c2Represents an acceleration constant; r is1,r2Representing a random parameter; pbestidD-dimension individual optimal value representing particle i; gbestidRepresenting a d-th dimension global optimum.
As shown in fig. 3, in order to prevent the degradation of the solution, in S6, an update solution checker is used to check whether each particle after update contains all AGV numbers that need to execute tasks, if any particle does not meet the requirement, the particle before update is retained, if the algorithm end condition is not met, the iteration is continued by returning to S4, and if the algorithm end condition is met, the algorithm is ended, and the group optimal position is the optimal solution obtained by the algorithm.
The following tables 1 and 2 are results of task allocation by using an unmodified particle swarm algorithm and an improved particle swarm algorithm respectively, the same manufacturing workshop map is used as an example for the two algorithms, and the route driving rule, the station and the AGV station coordinate 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 follow station 1 and AGV4-AGV6 all follow station 2 and start, return to 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
TABLE 2 results of using the distribution method of the invention
As can be seen from the table, the AGV running distances in the AGV task allocation scheme obtained by the improved particle swarm optimization are close, and the running distances are more balanced. In addition, the total travel distance of each AGV 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, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (9)
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 solver to check each particle after being coded;
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 determining the optimal position in the 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.
2. The improved particle swarm algorithm-based multiple AGV task allocation method according to claim 1, wherein in S1, the AGV information and task conditions are obtained on the premise that the processing station numbers, the AGV station numbers and the two-dimensional rectangular coordinate system thereof are determined according to a manufacturing workshop map, and the AGV road length and the road driving rules under the map scene are determined.
3. The improved particle swarm algorithm-based multiple AGV task allocation method according to claim 2, wherein in S1, the AGV information includes total number of vehicles, vehicle number, starting station of each vehicle and electric quantity, the AGV tasks include task information to be delivered and task information to be unloaded, and the AGVs are numbered according to a natural number sequence.
4. The method of claim 3, wherein in said S1, the parameters of the particle swarm comprise: c. C1，c2Is an acceleration constant, r1，r2Is a random parameter, w is an inertial weightQ 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.
5. The improved particle swarm algorithm-based multiple AGV task allocation method according to claim 3, wherein in S1, before task matching is performed on the to-be-delivered loading station and the to-be-emptied tray processing station, the relationship between the number of tasks to be delivered and the number of AGVs needs to be judged, and if the number of tasks to be delivered is greater than or equal to the number of AGVs, matching is performed directly; 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
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.
6. The improved particle swarm algorithm based multiple AGV task allocation method according to claim 1, wherein the particle encoding is a real number encoding.
7. The method of claim 1, wherein in step S5, the fitness of each particle' S current position is compared with the historical best position fitness, and if the current position fitness 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.
8. 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:
wherein,
a d-dimension component representing a velocity vector of a particle i at the k-th iteration;
a d-dimension component representing a location vector of a particle i at the k-th iteration; w represents an inertial weight; c. C1,c2Represents an acceleration constant; r is1,r2Representing a random parameter; pbestidD-dimension individual optimal value representing particle i; gbestidRepresenting a d-th dimension global optimum.
9. The method of claim 1, wherein in order to prevent degradation of the solution, the S6 checks whether each particle after updating contains all AGV numbers that need to execute the task using an update solution checker, if any particle does not meet the requirement, the particle before updating is retained, if the algorithm end condition is not met, the algorithm is returned to S4 to continue the iterative computation, and if the end condition is met, the algorithm is ended, and the group optimal position is the optimal solution obtained by the algorithm.
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| CN115100365B (en) * | 2022-08-25 | 2023-01-20 | 国网天津市电力公司高压分公司 | Camera optimal baseline acquisition method based on particle swarm optimization |
| CN116205474A (en) * | 2023-05-06 | 2023-06-02 | 深圳市森歌数据技术有限公司 | AGV task allocation method and device for parking lot, electronic equipment and storage medium |
| CN117930842A (en) * | 2024-01-22 | 2024-04-26 | 无锡学院 | Multi-AGV task allocation method |
| CN117930842B (en) * | 2024-01-22 | 2024-07-26 | 无锡学院 | Multi-AGV task allocation method |
| CN118014168A (en) * | 2024-04-10 | 2024-05-10 | 沈阳德成软件技术有限公司 | Particle swarm optimization-based enterprise operation management optimization method |
| CN118014168B (en) * | 2024-04-10 | 2024-06-21 | 沈阳德成软件技术有限公司 | Particle swarm optimization-based enterprise operation management optimization method |
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