Multi-AGV path planning method based on improved cuckoo optimization algorithm

Abstract

The invention discloses a multi-AGV path planning method based on an improved cuckoo optimization algorithm, and belongs to the technical field of AGV scheduling, and the method comprises the following steps: improving the cuckoo algorithm based on Tent chaotic mapping, adaptive Levy flight and Cauchy variation; an AGV simulation operation network is constructed; in the AGV simulation operation network, performing iterative optimization on a multi-AGV planning path based on an improved cuckoo optimization algorithm; and obtaining an optimal planning path of each AGV based on an optimization result. The invention aims to improve the path planning efficiency of the AGV cluster, reduce the calculation time of the algorithm and improve the working efficiency of the AGV.

Description

Technical Field

[0001] The invention belongs to the technical field of AGV scheduling, and in particular relates to a multi-AGV path planning method based on an improved cuckoo optimization algorithm. Background Art

[0002] Unmanned driving has been one of the research hotspots in the field of intelligent transportation in recent years. As a highly automated and intelligent means of transport, the Automated Guided Vehicle (AGV) is essentially an application form of unmanned driving technology. With the in-depth development of intelligent system and intelligent control research, the application scenarios of AGV have also been expanding. In the scenario of AGV operation, there are usually not only multiple AGVs in driving at the same time, so there may be conflicts in the original paths of some AGVs. At this time, the scheduling system will randomly re-plan new paths for the conflicting AGVs to avoid traffic jams and collisions. However, this scheduling method does not take into account the status of the AGV, so the operating efficiency of the AGV may be affected by the re-planning of the new path.

[0003] In response to the above problems, the existing patent 1 "An AGV vehicle scheduling method and device" discloses and authorizes an AGV vehicle scheduling method, which is based on scheduling of two AGVs, that is, the method is only applicable to a system with two AGVs. However, modern warehousing systems often require the coordinated operation of multiple AGVs. AGVs generally have three actions: loading, unloading, and charging. Therefore, it is necessary to plan the scheduling of multiple AGVs. Therefore, the actual applicability of this method is not strong, it cannot meet more needs, and the efficiency of warehousing is also very low.

[0004] Existing patent 2 "A multi-AGV path planning method considering conflict avoidance" discloses the use of the A* algorithm to plan the paths of each AGV separately, find the conflicts of each AGV planning scheme and avoid them one by one, and obtain the initial path population; use a genetic algorithm and introduce an enhanced elite strategy to calculate the initial path population and obtain the optimal path plan with the shortest completion time.

[0005] Reference 1 “Study on multi-AGV path planning and obstacle avoidance based on improved A* algorithm” proposes a method for path planning using the A* algorithm improved by load balancing.

[0006] Reference 2 “Research on multi-AGV path planning based on genetic algorithm in factory logistics” designed AGV vehicle path planning based on genetic algorithm.

[0007] Since the algorithms used in the existing patent 2 and documents 1 and 2 have limitations, if the number of AGVs increases, a lot of computing time will be consumed, resulting in the inefficiency of the AGVs moving back and forth repeatedly.

[0008] In view of the problems in the existing technology, it is urgent to propose a multi-AGV path planning method based on the improved cuckoo optimization algorithm. Summary of the invention

[0009] In order to solve the above technical problems, the present invention proposes a multi-AGV path planning method based on an improved cuckoo optimization algorithm to solve the problems existing in the above prior art.

[0010] To achieve the above object, the present invention provides a multi-AGV path planning method based on an improved cuckoo optimization algorithm, comprising the following steps:

[0011] The cuckoo algorithm is improved based on Tent chaos mapping, adaptive Levy flight and Cauchy mutation;

[0012] Build AGV simulation operation network;

[0013] In the AGV simulation operation network, multiple AGV planning paths are iteratively optimized based on the improved cuckoo optimization algorithm;

[0014] Based on the optimization results, the optimal planning path for each AGV is obtained.

[0015] Optionally, the iterative process of the improved cuckoo optimization algorithm includes:

[0016] S1. Set the population size, nest abandonment rate, problem boundary and maximum number of iterations;

[0017] S2. Randomly generate a certain number of populations in the problem domain and calculate the objective function value of each individual to find the current optimal value and optimal solution;

[0018] S3, determine whether the number of iterations reaches the maximum number of iterations, if so, exit the loop and output the optimal value and optimal solution; otherwise, go to step S4;

[0019] S4, update the bird's nest;

[0020] S5, calculating the objective function value of the new bird's nest, if it is superior to the previous one, replacing the function value and the corresponding bird's nest, and recording the optimal solution;

[0021] S6, randomly generate a number between [0,1] and compare it with the nest abandonment rate. If it is less than the abandonment rate, keep the nest; otherwise, update a new nest;

[0022] S7, recalculate the objective function value of the new bird's nest, if it is better than the previous one, replace the function value and the corresponding bird's nest, and record the optimal solution;

[0023] S8. Until the maximum number of iterations is reached, the optimal value and the optimal solution are output.

[0024] Optionally, the formula for initializing the population using the Tent chaotic map in S1 is as follows:

[0025] nest=repmat(Lb,n,1)+chaos(1,n,nd).*repmat((Ub-Lb),n,1),

[0026] Among them, Ub and Lb are the upper and lower boundaries of the problem, n is the population size, and chaos is the chaotic mapping method.

[0027] Optionally, the formula for updating the bird's nest in S4 is as follows:

[0028]

[0029] in, They represent the positions of nest i in the t+1th and tth generations respectively; best represents the best position in the current nest; step represents the step length generated by Levy flight, ν,u∈N(0,1).

[0030] Optionally, The calculation process is shown as follows:

[0031]

[0032] Among them, Γ represents Euler's second integral, and the value of β is 1.5.

[0033] Optionally, the formula for updating the new bird's nest in S6 is as follows:

[0034]

[0035] Among them, a and c are non-repeating random integers in the tth iteration, and rr is the random number generated by Cauchy mutation.

[0036] The present invention also provides an electronic device, comprising: a memory and a processor; the memory is used to store a program; the processor is used to execute the program to implement various steps of a multi-AGV path planning method based on an improved cuckoo optimization algorithm.

[0037] The present invention also provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of the multi-AGV path planning method based on the improved cuckoo optimization algorithm is implemented.

[0038] Compared with the prior art, the present invention has the following advantages and technical effects:

[0039] The present invention improves the cuckoo algorithm based on Tent chaos mapping, adaptive Levy flight and Cauchy mutation, and iteratively optimizes the multi-AGV planning path based on the improved cuckoo optimization algorithm, aiming to improve the path planning efficiency of the AGV cluster, while reducing the algorithm calculation time and improving the working efficiency of the AGV. The running time of the algorithm will not rise as sharply as some improved algorithms that add reverse learning, greedy strategy, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0041] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0042] Figure 2 1 is a comparison diagram of the optimization effects of various algorithms in the embodiments of the present invention, wherein (a) is a comparison diagram of the optimization effects of F1-F4, (b) is a comparison diagram of the optimization effects of F5-F8, and (c) is a comparison diagram of the optimization effects of F9-F10;

[0043] Figure 3 This is the AGV simulation operation network of the embodiment of the present invention. DETAILED DESCRIPTION

[0044] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0045] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0046] Embodiment 1

[0047] like Figure 1 As shown, this embodiment provides a multi-AGV path planning method based on an improved cuckoo optimization algorithm, comprising the following steps:

[0048] The cuckoo algorithm is improved based on Tent chaos mapping, adaptive Levy flight and Cauchy mutation;

[0049] Build AGV simulation operation network;

[0050] In the AGV simulation operation network, multiple AGV planning paths are iteratively optimized based on the improved cuckoo optimization algorithm;

[0051] Based on the optimization results, the optimal planning path for each AGV is obtained.

[0052] As a specific implementation method, the cuckoo search algorithm was proposed by Yang et al. in 2009. It is a cluster intelligence optimization algorithm that imitates the cuckoo's nest-finding and egg-laying activities. The cuckoo search algorithm has a good ability to jump out of the local optimum, but the Levy flight mechanism in the algorithm is relatively complex to implement.

[0053] The cuckoo search algorithm inherits the advantages of the swarm intelligence algorithm and has a strong optimization capability. Compared with other swarm intelligence algorithms, the cuckoo search algorithm has fewer parameters and is easy to operate and control. The implementation process of the cuckoo search algorithm has the following principles:

[0054] (1) There is only one cuckoo egg in the other bird’s nest. Cuckoos do not build nests for their own chicks, so each bird’s nest and each cuckoo egg can be considered a solution.

[0055] (2) Bird nests and eggs with better quality will enter the next iteration.

[0056] (3) The number of bird nests is fixed. When other birds find that the eggs in the nest are not theirs, they will abandon the current nest and build a new one.

[0057] The iterative process of the cuckoo search algorithm can refer to formula (1)

[0058]

[0059]

[0060]

[0061] Where X g，i represents the location of the i-th bird's nest in the g-th generation, represents the step size, and L(β) represents Levy random search. The calculation formula of the step size can refer to formula (2), and the calculation formula of L(β) can refer to formula (3), β is 1.5, ν, u∈N(0,1).

[0062] In formula (2), is the step size factor, X best represents the current best position. In formula (3), The calculation process can refer to formula (4):

[0063]

[0064] In formula (4), Γ represents the second Euler integral, and the value of β is 1.5. When other birds find that the eggs are not their own, they abandon the current nest and build a new one. The process of changing nests can be referred to formula (5).

[0065] X g+1,i =X g，i +r×(X g，i -X g，k ) (5)

[0067] Where X g，i represents the location of the i-th bird's nest in the g-th generation, X g，i and X g，k Indicates the location of the bird's nest selected in this generation.

[0068] Levi Flight:

[0069] In nature, many animals walk randomly when looking for food. Under normal circumstances, each process of looking for food has a certain purpose, and the next walking direction and moving distance are closely related to the current location and environment. Scientists have established a mathematical model based on this behavior of animals, which is Levy flight. After long-term research, scientists have found that the flight of many birds, insects and the movement of many animals all conform to the characteristics of Levy flight.

[0070] When animals move, they mostly move randomly over short distances, but occasionally they move over long distances and turn at large angles. This ensures that animals or insects do not search back and forth in a small area or go around in circles, so that they can expand their search range and find more food.

[0071] The probability density function of Levy flight is:

[0072]

[0073] In formula 6, β=1.

[0074] The jump distribution probability density function with α=1 / 2 and β=1 is:

[0075] λ(x)≈|x| -1-α , 0<α<2 (7)

[0077] The basic principle and operation steps of Cuckoo Search Algorithm (CS):

[0078] The cuckoo's reproduction is parasitic, and its nest-finding behavior conforms to the Lévy flight. Based on the cuckoo's brooding behavior and the Lévy flight, scientists proposed the cuckoo search algorithm.

[0079] The CS algorithm has the following three assumptions in theory:

[0080] Assumptions 1. Each cuckoo can only lay one egg at a time, and the mother bird has no pattern in choosing a nest, but randomly chooses a nest to lay an egg;

[0081] Assumption 2. Good nests will be preserved for the next generation;

[0082] Assumption 3. The number of host nests that can be used to hatch the next generation of bird eggs is fixed, and its value is n. The probability that the original owner of the bird nest can find the invading cuckoo eggs is pa, and the value range of pa is [0,1]. When the original owner of the bird nest finds the invading cuckoo eggs, he will choose to destroy the cuckoo eggs or abandon the nest and find a new location to rebuild the nest.

[0083] The third assumption is that n*pa bird nests will be discovered and new bird nests will be created.

[0084] In this process, the bird's nest is equivalent to the bird's eggs, which represent the solution to the problem to be solved. In path planning, each solution is a feasible path. Whether the eggs can be successfully hatched and grown is the only criterion for measuring the quality of the bird's nest, that is, the only criterion for judging the quality of the solution, and also the criterion for judging the quality of the path. The quality of the bird's nest indicates the quality of the solution. The process of the cuckoo looking for a nest and laying eggs is the process of finding a solution.

[0085] This embodiment improves the cuckoo algorithm as follows:

[0086] Based on the basic cuckoo algorithm, the Tent chaotic map is used to initialize the diversity of the population:

[0087] Initialize the population nest = repmat(Lb,n,1)+chaos(1,n,nd).*repmat((Ub-Lb),n,1);

[0088] Among them, Ub and Lb are the upper and lower boundaries of the problem, n is the population size, and chaos is the chaotic mapping method.

[0089] Adopt Levy flight with adaptive step size to improve the formula for updating the bird's nest:

[0090] In view of the shortcomings of the standard cuckoo search algorithm, such as slow convergence speed and low optimization accuracy, the adaptive Levy flight mechanism is used for global search: the Levy flight step size decreases with the iteration. The improved algorithm has a larger step size factor in the early stage of optimization, thereby expanding the search space in the early stage of the algorithm and improving the global search capability; during the optimization process, the step size decreases, improving the local search performance of the algorithm. It is changed from 0.01 in the standard algorithm to the following formula: α = 0.5*exp(-t / tmax).

[0091] Where α = 0.5*exp(-t / tmax);

[0092] Before the random walk, Cauchy mutation is used to update the nest position:

[0093] Generate a random number rr=cauchyrnd(0,1,[size(nest,1),1]) using Cauchy mutation;

[0094] Use rr to replace r, and the new bird's nest generation method is:

[0095]

[0096] Where: a, c are random integers that do not repeat in the tth iteration.

[0097] The Cauchy distribution is similar to the standard normal distribution. It is a continuous probability distribution with a small value at the origin, a relatively flat and long end, and a slow approach to zero. Therefore, it can produce greater disturbances than the previous random walk strategy. Therefore, the Cauchy mutation is used to perturb the nest position, thereby expanding the search scale of the cuckoo algorithm and improving the algorithm's ability to escape from the local optimum.

[0098] Algorithm performance test:

[0099] Tested on the CEC2005 function set, such as Figure 2 As shown, CALCS is the improved cuckoo algorithm proposed in this embodiment, CS is the original cuckoo optimization algorithm, GWO is the grey wolf optimization algorithm, and PSO is the particle swarm optimization algorithm. The algorithm is iterated 1000 times, and the number of particles of each algorithm is set to 30.

[0100] From the tests of single-peak function and multi-peak function, it can be seen that the cuckoo optimization algorithm integrating Cauchy mutation and adaptive Lévy flight proposed in this embodiment has good optimization effect.

[0101] Algorithm application AGV scheduling:

[0102] Figure 3The network for AGV simulation operation is shown. The network shows the roads that AGV can move. Each circle represents a node, which is an intersection or midpoint. Each arrow represents a directed connection between two nodes. The length of each arrow is one meter. The square nodes represent pickup points or drop-off points that can be the source and destination of AGVs. In the factory, 20 AGVs are performing tasks.

[0103] The iterative process of the improved cuckoo optimization algorithm is shown as follows:

[0104] 1) Set the population size, nest abandonment rate, problem boundary and maximum number of iterations.

[0105] 2) Randomly generate a certain number of populations in the problem domain and calculate the objective function value of each individual to find the current optimal value and optimal solution.

[0106] 3) Determine whether the number of iterations reaches the maximum number of iterations. If so, exit the loop and output the optimal value and optimal solution; otherwise, go to step 4).

[0107] 4) Update the bird's nest according to the following formula.

[0108]

[0109]

[0110]

[0111] The calculation process is shown as follows:

[0112]

[0113] Among them, Γ represents Euler's second integral, and the value of β is 1.5.

[0114] in, They represent the positions of nest i in the t+1th and tth generations respectively; best represents the best position in the current nest; step represents the step length generated by Levy flight, ν,μ∈N(0,1).

[0115] 5) Calculate the objective function value of the new bird's nest. If it is better than the previous one, replace the function value and the corresponding bird's nest, and record the optimal solution.

[0116] 6) Randomly generate a number between [0,1] and compare it with the nest abandonment rate. If it is less than that, keep the nest; otherwise, generate a new nest according to the formula.

[0117]

[0118] a and c are non-repeating random integers in the tth iteration, and rr is the random number generated by Cauchy mutation.

[0119] 7) Recalculate the objective function value of the new bird's nest. If it is better than the previous one, replace the function value and the corresponding bird's nest, and record the optimal solution.

[0120] 8) Determine the function value and the optimal value. If they are less than each other, replace the optimal value and the optimal solution. The algorithm goes to step 3) for the next iteration.

[0121] After 500 iterations, the simulation results are shown in Table 1:

[0122] Table 1

[0123] GWO Algorithm CS algorithm CALCS algorithm Number of completed tasks 402 371 689 Completion rate (%) 72.1 67.8 96.2

[0124] It can be seen that the improved cuckoo algorithm proposed in this embodiment can improve the path planning efficiency of the AGV cluster, while reducing the calculation time of the algorithm and improving the working efficiency of the AGV.

[0125] Embodiment 2

[0126] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store a program; the processor is used to execute the program to implement each step of the multi-AGV path planning method based on the improved cuckoo optimization algorithm.

[0127] Embodiment 3

[0128] This embodiment also provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of the multi-AGV path planning method based on the improved cuckoo optimization algorithm is implemented.

[0129] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-AGV path planning method based on an improved cuckoo optimization algorithm, characterized in that: The following steps are involved: The cuckoo algorithm is improved based on Tent chaos mapping, adaptive Levy flight and Cauchy mutation; Build AGV simulation operation network; In the AGV simulation operation network, multiple AGV planning paths are iteratively optimized based on the improved cuckoo optimization algorithm; Based on the optimization results, the optimal planning path for each AGV is obtained.

2. The method according to claim 1, characterized in that The iterative process of the improved cuckoo optimization algorithm includes: S1. Set the population size, nest abandonment rate, problem boundary and maximum number of iterations; S2. Randomly generate a certain number of populations in the problem domain and calculate the objective function value of each individual to find the current optimal value and optimal solution; S3, determine whether the number of iterations reaches the maximum number of iterations, if so, exit the loop and output the optimal value and optimal solution; otherwise, go to step S4; S4, update the bird's nest; S5, calculating the objective function value of the new bird's nest, if it is superior to the previous one, replacing the function value and the corresponding bird's nest, and recording the optimal solution; S6, randomly generate a number between [0,1] and compare it with the nest abandonment rate. If it is less than the abandonment rate, keep the nest; otherwise, update a new nest; S7, recalculate the objective function value of the new bird's nest, if it is better than the previous one, replace the function value and the corresponding bird's nest, and record the optimal solution; S8. Until the maximum number of iterations is reached, the optimal value and the optimal solution are output.

3. The method according to claim 2, characterized in that The formula for initializing the population using Tent chaotic map in S1 is as follows: nest=repmat(Lb,n,1)+chaos(1,n,nd).*repmat((Ub-Lb),n,1), Among them, Ub and Lb are the upper and lower boundaries of the problem, n is the population size, and chaos is the chaotic mapping method.

4. The method according to claim 2, characterized in that: The formula for updating the bird's nest in S4 is as follows: in, They represent the positions of nest i in the t+1th and tth generations respectively; best represents the best position in the current nest; step represents the step length generated by Levy flight, ν,u∈N(0,1).

5. The method according to claim 4, characterized in that The calculation process is shown as follows: Among them, Γ represents Euler's second integral, and the value of β is 1.

5.

6. The method according to claim 2, characterized in that The formula for updating the new bird's nest in S6 is as follows: Among them, a and c are non-repeating random integers in the tth iteration, and rr is the random number generated by Cauchy mutation.

7. An electronic device, characterized in that: include: Memory and processor; The memory is used to store programs; The processor is used to execute the program to implement the various steps of the multi-AGV path planning method based on the improved cuckoo optimization algorithm as described in any one of claims 1 to 6.

8. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the multi-AGV path planning method based on the improved cuckoo optimization algorithm as described in any one of claims 1 to 6 is implemented.

Images