A collaborative path planning method for drone swarms based on quantum morpho mechanism

Abstract

The present invention discloses a method for collaborative path planning for a swarm of unmanned aerial vehicles (UAVs) based on the quantum Morpho mechanism. The method comprises the following steps: establishing a UAV swarm collaborative path planning model that considers variable speed and simultaneous arrival constraints; establishing a cost function for collaborative path planning for the swarm under these constraints; initializing a swarm of quantum Morpho vehicles and setting parameters; defining and calculating the odor emitted by the quantum Morpho vehicles; sorting all quantum Morpho vehicles according to the odor values ​​emitted by the quantum Morpho vehicles; sequentially executing linear and curved escape sequences, and using a simulated quantum revolving door to evolve the quantum positions of the quantum Morpho vehicles during the escape sequence. A greedy selection strategy is applied to determine the quantum position of the next generation of quantum Morpho vehicles. The evolution is terminated, and a UAV swarm path and speed matrix is ​​output. The method, while also considering variable speed and simultaneous arrival constraints under obstacle avoidance requirements, achieves rapid convergence, high convergence accuracy, and is simple to implement with a reduced number of parameters.

Description

Technical Field

[0001] The present invention belongs to the field of cooperative control of drone clusters, and relates to a cooperative path planning method for drone clusters based on the quantum Morpho mechanism, and in particular to a cooperative obstacle avoidance path planning method for drone clusters based on the quantum Morpho mechanism under the constraints of variable speed and simultaneous arrival. Background Art

[0002] Unmanned aerial vehicles (UAVs) are autonomously controlled by programs or wirelessly remotely controlled. In modern warfare, they often operate in swarms and form a collaborative network. They are a crucial enabler of innovative military technologies and new weapon platforms. Compared to single-machine path planning methods, the paths generated by collaborative UAV swarm path planning may not be optimal for each individual drone, but they are optimal for the swarm.

[0003] In recent years, some scholars have conducted some research on the collaborative path planning method of UAV clusters. Wang Yihu et al. (Wang Yihu, Wang Siming. UAV path planning based on improved particle swarm algorithm [J]. Computer Engineering and Science, 2020, 42(09): 1690-1696.) For the three-dimensional obstacle avoidance path planning problem of a single UAV, the chemotaxis and migration operations in the bacterial foraging algorithm were introduced to improve the global optimization ability of the existing particle swarm algorithm, and finally the improved particle swarm algorithm was used to solve the problem. Xia Rui et al. (Xia Rui, Zhao Lei, Wu Shuyu, Li Jun. UAV collaborative path planning based on artificial bee colony algorithm [J]. Wireless Internet Technology, 2018, 15(13): 13-21.) For the three-dimensional obstacle avoidance path planning problem of multiple UAVs, non-deterministic search and bidirectional planning mechanisms were introduced to improve the food production method in the artificial bee colony algorithm, and then the flyable route was searched under two environmental models. Yan Fei et al. (Yan Fei, Zhu Xiaoping, Zhou Zhou, Tang Yang. Real-time task allocation of multiple heterogeneous UAVs considering simultaneous attack constraints [J]. Chinese Science: Information Sciences, 2019, 49(05): 555-569.) aimed at the search and attack mission planning problem of reconnaissance / attack UAV clusters in the war zone. They considered the kinematic constraints of the UAVs and the requirement of simultaneous attack targets at the trajectory planning level and proposed an algorithm based on a combination of cooperative particle swarms, cooperative functions, and cooperative variables to solve the problem. The results of the existing literature search show that the existing UAV cluster collaborative path planning methods rarely consider the simultaneous arrival constraints of the cluster UAVs and the invariable speed of the UAVs. However, in actual scenarios, the cluster UAVs are often required to arrive at the predetermined target at the same time to perform the corresponding tasks. In addition, the swarm intelligence optimization algorithms used in the existing UAV cluster collaborative path planning methods still have problems such as high computational complexity, slow convergence speed, poor convergence accuracy, and easy to fall into local extreme values. Summary of the Invention

[0004] In response to the above-mentioned existing technologies, the technical problem to be solved by the present invention is to provide a collaborative path planning method for drone clusters based on the quantum Morpho mechanism, which additionally considers variable speed and simultaneous arrival constraints under the obstacle avoidance requirements, and has fast convergence speed and high convergence accuracy.

[0005] To solve the above technical problems, the present invention provides a method for collaborative path planning of a drone cluster based on the quantum morpho mechanism, comprising the following steps:

[0006] Step 1: Establish a UAV swarm collaborative path planning model considering variable speed and simultaneous arrival constraints;

[0007] Step 2: Establish a cost function for collaborative path planning of UAV clusters considering variable speed and simultaneous arrival constraints;

[0008] Step 3: Initialize the quantum morpho butterfly group and set parameters;

[0009] Step 4: Define and calculate the scent emitted by the quantum morpho butterfly;

[0010] Step 5: Sort all the quantum morpho butterflies according to the scent value they emit;

[0011] Step 6: The quantum morpho butterfly executes the straight-line escape and curve escape processes in sequence, and uses a simulated quantum rotating gate to evolve the quantum position of the quantum morpho butterfly during the escape process.

[0012] Step 7: Apply the greedy selection strategy to determine the quantum position of the next generation of quantum morpho.

[0013] Step 8: Evolution termination judgment, output UAV cluster route and speed matrix.

[0014] Furthermore, the collaborative path planning model for UAV swarms considering variable speed and simultaneous arrival constraints established in step 1 includes:

[0015] Assume that the drone cluster consists of N drones, and the attribute set of the drone cluster is A = {v min ,v max ,α,ρ,m}, where v max and v min are the upper and lower limits of the swarm UAV speed, α is the side length of the swarm UAV activity square area, ρ is the grid equivalent ratio, m = ceil (α / ρ) is the equivalent grid number, ceil (·) is the upward rounding function; the attribute set of UAV n is in, is the initial position of UAV n, is the final position of UAV n, is the equivalent initial position of UAV n, is the equivalent final position of UAV n, is the path matrix of UAV n, is the speed matrix of UAV n, is the number of waypoints that UAV n passes through, p n =d n +2 is the total number of waypoints of UAV n, n=1,2,...,N; for the UAV n route matrix, let the horizontal coordinate of the initial waypoint be Initial waypoint ordinate The horizontal coordinate of the last waypoint The vertical coordinate of the last waypoint Waypoints Among them, if it satisfies Then let τ=1; if Then let τ=2,σ=1,2,...,p n , n=1,2,...,N, so far, there are only co-location variables and There are three coordinated speed variables to be optimized.

[0016] Furthermore, the cost function for collaborative path planning of UAV clusters considering variable speed and simultaneous arrival constraints established in step 2 includes:

[0017] Establishing cluster collaborative path planning cost function Where q=[q1,q2,...,q n ] is the cluster route matrix, v=[v1,v2,…,v N ] is the cluster speed matrix, For the UAV range, is the flight time of the UAV, is the maximum flight time of the cluster, is the minimum flight time of the cluster; r n ={r n (a,b)|r n (a,b)∈{0,1}} m×m is the route grid matrix of UAV n, o={o a,b |o a,b ∈{0,1}} m×m is the obstacle matrix. If a corresponding route or obstacle exists in the grid corresponding to an element, the element value is 1; otherwise, the element value is 0. ⊙ is the matrix dot product summation symbol, λ1 is the simultaneous arrival constraint penalty factor, and λ2 is the obstacle avoidance penalty factor.

[0018] Furthermore, in step 3, initializing the quantum morpho butterfly group and setting parameters include:

[0019] Assume that the size of the quantum morpho butterfly group is h, the maximum number of iterations is G, the number of iterations is an integer g, and at the gth iteration, the quantum position of the i-th quantum morpho butterfly in the u-dimensional search space is The jth dimension of its quantum position i=1,2,...,h,j=1,2,...,u,g=1,2,...,G,u=u1+u2,g=1 in the initial generation,each dimension of the quantum position of the initial generation quantum flash butterfly is initialized to a uniform random number in the interval [0,1].

[0020] Furthermore, the definition and calculation of the scent emitted by the quantum morpho butterfly in step 4 include:

[0021] At the gth iteration, each dimension of all quantum Morpho butterfly quantum positions is mapped into the continuous solution space to obtain the mapping state of quantum Morpho butterfly quantum positions The mapping equation is defined as After cutting and reconstructing the mapping state of the quantum position of the i-th quantum butterfly, it is put into the cluster collaborative path planning cost function to obtain the cost function value superscript Specifically refers to the matrix cutting and reconstruction operation, which first cuts the original matrix into several element sequences as required, then fills all the element sequences into the corresponding drone route or speed matrix as required to form several new matrices, and finally reconstructs the several new matrices into two matrices as required. Among them, the cutting requirements are specifically: Medium Satisfaction The sequence elements are sequentially used as the UAV n route matrix The variables to be optimized in will satisfy The sequence elements are sequentially used as the speed matrix of UAV n The variables to be optimized in n=1,2,...,N; the reconstruction requirements are as follows: reconstruct the path matrix of each aircraft into the cluster path matrix according to the order of the UAV numbers Reconstruct the speed matrix of each aircraft into a cluster speed matrix according to the order of the UAV numbers Substitute the cost function value corresponding to the quantum position mapping state of the i-th quantum butterfly into the odor emission function The odor value emitted by the i-th quantum butterfly at this position can be obtained Among them, γ1 is the emission intensity, and γ2 is the emission pattern. It is stipulated that the stronger the scent emitted by the quantum morpho butterfly, the more effective it is in driving away predators and thus successfully escaping.

[0022] Furthermore, in step 5, all quantum morpho butterflies are sorted according to the odor values ​​emitted by the quantum morpho butterflies, including:

[0023] Arrange all quantum morphos in descending order of the odor they emit, and define the quantum position with the largest odor value as the optimal quantum position of the quantum morpho butterfly group. The quantum position with the smallest odor value is the worst quantum position of the quantum morpho butterfly group.

[0024] Furthermore, in step 6, the quantum morpho butterfly sequentially executes the straight-line escape and curved escape processes, and uses a simulated quantum revolving door to evolve the quantum position of the quantum morpho butterfly during the escape process, including:

[0025] (1) Straight-line escape: The update equation for the j-dimensional quantum position of the i-th quantum butterfly under straight-line escape is defined as in, is the j-dimensional simulated quantum rotation angle of the i-th quantum morpho butterfly in the straight-line escape, is a uniform random number in the interval [0,1].

[0026] (2) Curve escape: The update equation of the j-dimensional quantum position of the i-th quantum butterfly under curve escape is defined as in, The j-dimensional simulated quantum rotation angle of the i-th quantum morpho butterfly under curve escape is: is a standard normal random number, is a uniform random number in the interval [0,1]; after the i-th quantum butterfly escapes, its newly generated quantum position

[0027] Furthermore, in step seven, the greedy selection strategy is applied to determine the quantum position of the next generation of quantum morpho butterflies, including:

[0028] Calculate the odor values ​​emitted by all quantum butterflies at the original quantum position and the newly generated quantum position, and calculate the odor values ​​of all quantum butterflies at the quantum position set. The greedy method selects the quantum position with the larger smell value as the quantum position of the next generation quantum morpho butterfly i=1,2,...,h.

[0029] Furthermore, in step eight, the evolution termination judgment is performed and the output of the drone cluster route and speed matrix includes:

[0030] Determine whether the maximum number of iterations G has been reached. If not, set g = g + 1 and return to step five. If reached, terminate the mechanism evolution and cut and reconstruct the optimal quantum position mapping state of the last generation of quantum morpho butterfly swarm and output it.

[0031] The beneficial effects of the present invention: Compared with existing technologies, this invention takes into account variable speed and simultaneous arrival constraints in addition to obstacle avoidance requirements, designing a low-complexity collaborative obstacle avoidance path planning method for drone swarms. It also establishes a collaborative task path planning model and cost function for drone swarms under variable speed and simultaneous arrival constraints. To achieve subsequent solution of the model, the present invention devises a quantum Morpho optimization mechanism for efficient solution. This mechanism, inspired by the Morpho butterfly's escape process, evolves the quantum state of the Morpho butterfly based on odor emission mechanisms, escape flight routes, and simulated quantum revolving doors, improving the Morpho butterfly swarm's escape probability from predators. It features rapid convergence, high convergence accuracy, simple implementation, and a low number of parameters. The proposed method can plan a reasonable route for each drone in the swarm and determine the optimal speed for each drone's corresponding route segment. Simulation experiments demonstrate the effectiveness of the quantum Morpho mechanism-based collaborative path planning method for drone swarms under variable speed and simultaneous arrival constraints, broadening the application scenarios of existing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the collaborative path planning method for drone clusters based on the quantum morpho mechanism designed in the present invention.

[0033] Figure 2 Planned route map for each drone in the drone swarm.

[0034] Figure 3 The relationship curve between the cost function value and the number of mechanism evolutions of UAV swarm collaborative path planning. DETAILED DESCRIPTION

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] Combine Figure 1 The present invention designs a UAV cluster collaborative path planning method based on the quantum morpho mechanism under the consideration of variable speed and simultaneous arrival constraints, which includes the following steps:

[0037] Step 1: Establish a UAV swarm collaborative path planning model considering variable speed and simultaneous arrival constraints.

[0038] like Figure 2 As shown in the figure, it is assumed that the drone cluster consists of N drones. The attribute set of the drone cluster A = {v min ,v max ,α,ρ,m}, where v max and v min are the upper and lower limits of the swarm UAV speed, α is the side length of the swarm UAV activity square area, ρ is the grid equivalent ratio, m = ceil (α / ρ) is the equivalent grid number, and ceil (·) is the upward rounding function. in, is the initial position of UAV n, is the final position of UAV n, is the equivalent initial position of UAV n, is the equivalent final position of UAV n, is the path matrix of UAV n, is the speed matrix of UAV n, is the number of waypoints that UAV n passes through, p n =d n +2 is the total number of waypoints of UAV n, n=1,2,...,N. For the UAV n route matrix, let the horizontal coordinate of the initial waypoint be Initial waypoint ordinate The horizontal coordinate of the last waypoint The vertical coordinate of the last waypoint Waypoints Among them, if it satisfies Then let τ=1; if Then let τ=2,σ=1,2,...,p n , n=1,2,...,N. So far, the model only has co-location variables and There are three coordinated speed variables to be optimized.

[0039] Step 2: Establish a cost function for collaborative path planning of UAV clusters considering variable speed and simultaneous arrival constraints.

[0040] Establishing cluster collaborative path planning cost function Where q=[q1,q2,...,q n ] is the cluster route matrix, v=[v1,v2,...,v N ] is the cluster speed matrix, For the UAV range, is the flight time of the UAV, is the maximum flight time of the cluster, is the minimum flight time of the cluster. n ={r n (a,b)|r n (a,b)∈{0,1}} m×m is the route grid matrix of UAV n, o={o a,b |o a,b ∈{0,1}} m×m is the obstacle matrix. If a corresponding route or obstacle exists in the grid corresponding to an element, the element value is 1; otherwise, the element value is 0. ⊙ is the matrix dot product summation symbol, λ1 is the simultaneous arrival constraint penalty factor, and λ2 is the obstacle avoidance penalty factor.

[0041] Step 3: Initialize the quantum morpho butterfly swarm and set parameters.

[0042] Assume that the size of the quantum morpho butterfly group is h, the maximum number of iterations is G, and the number of iterations is an integer g. At the g-th iteration, the quantum position of the i-th quantum morpho butterfly in the u-dimensional search space is The jth dimension of its quantum position i=1,2,...,h,j=1,2,...,u,g=1,2,...,G,u=u1+u2。 In the initial generation, g=1, and each dimension of the quantum position of the initial generation quantum butterfly is initialized to a uniform random number in the interval [0,1].

[0043] Step 4: Define and calculate the scent emitted by the quantum morpho butterfly.

[0044] At the gth iteration, each dimension of all quantum Morpho butterfly quantum positions is mapped into the continuous solution space to obtain the mapping state of quantum Morpho butterfly quantum positions The mapping equation is defined as After cutting and reconstructing the mapping state of the quantum position of the i-th quantum butterfly, it is put into the cluster collaborative path planning cost function to obtain the cost function value superscript Specifically refers to the matrix cutting and reconstruction operation, which first cuts the original matrix into several element sequences as required, then fills all the element sequences into the corresponding drone route or speed matrix as required to form several new matrices, and finally reconstructs the several new matrices into two matrices as required. Among them, the cutting requirements are specifically: Medium Satisfaction The sequence elements are sequentially used as the UAV n route matrix The variables to be optimized in will satisfy The sequence elements are sequentially used as the speed matrix of UAV n The variables to be optimized in n=1,2,...,N; the reconstruction requirements are as follows: reconstruct the path matrix of each aircraft into the cluster path matrix according to the order of the UAV numbers Reconstruct the speed matrix of each aircraft into a cluster speed matrix according to the order of the UAV numbers Substitute the cost function value corresponding to the quantum position mapping state of the i-th quantum butterfly into the odor emission function The odor value emitted by the i-th quantum butterfly at this position can be obtained Among them, γ1 is the emission intensity, and γ2 is the emission pattern. It is stipulated that the stronger the scent emitted by the quantum morpho butterfly, the more effective it is in driving away predators and successfully escaping.

[0045] Step 5: Sort all the quantum morpho butterflies according to the scent value they emit.

[0046] Arrange all quantum morphos in descending order of the odor they emit, and define the quantum position with the largest odor value as the optimal quantum position of the quantum morpho butterfly group. The quantum position with the smallest odor value is the worst quantum position of the quantum morpho butterfly group.

[0047] Step 6: The quantum morpho butterfly executes the straight-line escape and curve escape processes in sequence, and uses a simulated quantum rotating gate to evolve the quantum position of the quantum morpho butterfly during the escape process.

[0048] (1) Straight-line escape: The update equation for the j-dimensional quantum position of the i-th quantum butterfly under straight-line escape is defined as in, is the j-dimensional simulated quantum rotation angle of the i-th quantum morpho butterfly in the straight-line escape, is a uniform random number in the interval [0,1].

[0049] (2) Curve escape: The update equation of the j-dimensional quantum position of the i-th quantum butterfly under curve escape is defined as in, The j-dimensional simulated quantum rotation angle of the i-th quantum morpho butterfly under curve escape is: is a standard normal random number, is a uniform random number in the interval [0,1]. After the i-th quantum butterfly escapes, its newly generated quantum position

[0050] Step 7: Apply the greedy selection strategy to determine the quantum position of the next generation of quantum morpho.

[0051] Calculate the odor values ​​emitted by all quantum butterflies at the original quantum positions and the newly generated quantum positions. The greedy method selects the quantum position with the larger smell value as the quantum position of the next generation quantum morpho butterfly

[0052] Step 8: Evolution termination judgment, output UAV cluster route and speed matrix.

[0053] Determine whether the maximum number of iterations G has been reached. If not, set g = g + 1 and return to step five. If reached, terminate the mechanism evolution and cut and reconstruct the optimal quantum position mapping state of the last generation of quantum morpho butterfly swarm and output it.

[0054] In the simulation experiment, we set N=5, v min =10m / s,v max=20m / s, α=100m, ρ=5m / grid, λ1=20,λ2=50,obstacle setting reference Figure 2 The number of experiments was 20 and the experimental results were statistically averaged.

[0055] exist Figure 3 In the paper, the UAV cluster collaborative path planning method based on the quantum flash butterfly mechanism designed by the present invention is denoted as QMBO; the UAV cluster collaborative path planning method based on the improved particle swarm mechanism is denoted as PSOI, specifically referring to: Wang Yihu, Wang Siming. UAV path planning based on improved particle swarm algorithm [J]. Computer Engineering and Science, 2020, 42(09): 1690-1696. In QMBO, set h=20, G=100, γ1=1.5, γ2=-1; in PSOI, set the number of particles to 20, the maximum number of iterations to 100, the migration probability to 0.25, the inertia weight to 0.9, the maximum particle speed to 2, the minimum particle speed to -2, and the number of particle chemotaxis and migration in each iteration to 1. From Figure 2-Figure 3 It can be seen from the figure that the UAV cluster collaborative path planning method based on the quantum morpho mechanism designed in the present invention is simple, efficient, and low-complexity, and can be applied in actual engineering.

Claims

1. A method for collaborative path planning of drone swarms based on the quantum morpho mechanism, characterized in that: The following steps are involved: Step 1: Establish a UAV swarm collaborative path planning model considering variable speed and simultaneous arrival constraints; Step 2: Establish a cost function for collaborative path planning of UAV clusters considering variable speed and simultaneous arrival constraints; Step 3: Initialize the quantum morpho butterfly group and set parameters; Step 4: Define and calculate the scent emitted by the quantum morpho butterfly; Substitute the cost function value corresponding to the quantum position mapping state of the i-th quantum butterfly into the odor emission function Get the odor value emitted by the i-th quantum butterfly at this position Among them, γ1 is the emission intensity, γ2 is the emission mode; The cost function value is obtained by cutting and reconstructing the mapping state of the quantum position of the i-th quantum butterfly and inserting it into the cluster collaborative path planning cost function; superscript Specifically refers to the matrix cutting and reconstruction operation; is the mapping state of the quantum position of the quantum butterfly; g is the number of iterations, Step 5: Sort all the quantum morpho butterflies according to the scent value they emit; Step 6: The quantum morpho butterfly performs the straight-line escape and curved escape processes in sequence, and uses a simulated quantum revolving door to evolve the quantum position of the quantum morpho butterfly during the escape process; Step 7: Apply the greedy selection strategy to determine the quantum position of the next generation of quantum morpho butterfly; (1) Straight-line escape: The update equation for the j-dimensional quantum position of the i-th quantum butterfly under straight-line escape is defined as in, is the j-dimensional simulated quantum rotation angle of the i-th quantum morpho butterfly in the straight-line escape, is the jth dimension of the optimal quantum position of the quantum morpho butterfly group, is the jth dimension of the worst quantum position of the quantum morpho group, is a uniform random number in the interval [0,1]; the newly generated quantum position of the i-th quantum butterfly after it escapes in a straight line is (2) Curve escape: The update equation of the j-dimensional quantum position of the i-th quantum butterfly under curve escape is defined as in, The j-dimensional simulated quantum rotation angle of the i-th quantum morpho butterfly under curve escape is: is a standard normal random number, the maximum number of iterations is G, is a uniform random number in the interval [0,1]; after the i-th quantum butterfly escapes, its newly generated quantum position Step 8: Determine the termination of evolution and output the UAV cluster route and speed matrix.

2. The method for collaborative path planning of a drone swarm based on the quantum morpho mechanism according to claim 1, characterized in that: Step 1 describes the establishment of a UAV swarm collaborative path planning model considering variable speed and simultaneous arrival constraints, including: Assume that the drone cluster consists of N drones, and the attribute set of the drone cluster is A = {v min ,v max ,α,ρ,m}, where v max and v min are the upper and lower limits of the swarm UAV speed, α is the side length of the swarm UAV activity square area, ρ is the grid equivalent ratio, m = ceil (α / ρ) is the equivalent grid number, ceil (·) is the upward rounding function; the attribute set of UAV n is in, is the initial position of UAV n, is the final position of UAV n, is the equivalent initial position of UAV n, is the equivalent final position of UAV n, is the path matrix of UAV n, is the speed matrix of UAV n, is the number of waypoints that UAV n passes through, p n =d n +2 is the total number of waypoints of UAV n, n=1,2,…,N; for the UAV n route matrix, let the horizontal coordinate of the initial waypoint Initial waypoint ordinate Last waypoint horizontal coordinate The vertical coordinate of the last waypoint Waypoints Among them, if it satisfies Then let τ=1; if Then let τ=2,σ=1,2,...,p n , n=1,2,...,N, so far, there are only co-location variables and There are three coordinated speed variables to be optimized.

3. The method for collaborative path planning of a swarm of drones based on the quantum morpho mechanism according to claim 2, characterized in that: Step 2 establishes the cost function for collaborative path planning of UAV clusters under the constraints of variable speed and simultaneous arrival, including: Establishing cluster collaborative path planning cost function Where q=[q1,q2,…,q n ] is the cluster route matrix, v=[v1,v2,…,v N ] is the cluster speed matrix, For the UAV’s range, is the flight time of the UAV, is the maximum flight time of the cluster, is the minimum flight time of the cluster; r n ={r n (a,b)|r n (a,b)∈{0,1}} m×m is the route grid matrix of UAV n, o={o a,b |o a,b ∈{0,1}} m×m is the obstacle matrix. If there is a corresponding route or obstacle in the grid corresponding to an element, the element value is 1; otherwise, the element value is 0. ⊙ is the matrix dot product summation symbol, λ1 is the simultaneous arrival constraint penalty factor, and λ2 is the obstacle avoidance penalty factor.

4. The method for collaborative path planning of a drone swarm based on the quantum morpho mechanism according to claim 3 is characterized by: Step 3 of initializing the quantum butterfly group and setting parameters includes: Assume that the size of the quantum morpho butterfly group is h, the maximum number of iterations is G, the number of iterations is an integer g, and at the gth iteration, the quantum position of the i-th quantum morpho butterfly in the u-dimensional search space is The jth dimension of its quantum position u=u1+u2, g=1 in the initial generation, and each dimension of the quantum position of the initial generation quantum flash butterfly is initialized to a uniform random number in the interval [0,1].

5. The method for collaborative path planning of a drone swarm based on the quantum morpho mechanism according to claim 4 is characterized by: The definition and calculation of the scent emitted by the quantum morpho butterfly described in step 4 include: At the gth iteration, each dimension of all quantum Morpho butterfly quantum positions is mapped into the continuous solution space to obtain the mapping state of quantum Morpho butterfly quantum positions The mapping equation is defined as After cutting and reconstructing the mapping state of the quantum position of the i-th quantum butterfly, it is put into the cluster collaborative path planning cost function to obtain the cost function value superscript Specifically refers to the matrix cutting and reconstruction operation, which first cuts the original matrix into several element sequences as required, then fills all the element sequences into the corresponding drone route or speed matrix as required to form several new matrices, and finally reconstructs the several new matrices into two matrices as required; the cutting requirements are specifically: Medium Satisfaction The sequence elements are sequentially used as the UAV n route matrix The variables to be optimized in will satisfy The sequence elements are sequentially used as the speed matrix of UAV n The variables to be optimized in n=1,2,…,N; the reconstruction requirements are as follows: reconstruct the path matrix of each aircraft into the cluster path matrix according to the order of the UAV numbers Reconstruct the speed matrix of each aircraft into a cluster speed matrix according to the order of the UAV numbers Substitute the cost function value corresponding to the quantum position mapping state of the i-th quantum butterfly into the odor emission function The odor value emitted by the i-th quantum butterfly at this position can be obtained Among them, γ1 is the emission intensity and γ2 is the emission pattern; it is stipulated that the greater the smell emitted by the quantum morpho butterfly, the more conducive it is to driving away predators and successfully escaping.

6. The method for collaborative path planning of a swarm of drones based on the quantum morpho mechanism according to claim 5, characterized in that: Step 5 of sorting all quantum morpho butterflies according to the odor value emitted by the quantum morpho butterflies includes: Arrange all quantum morphos in descending order of the odor they emit, and define the quantum position with the largest odor value as the optimal quantum position of the quantum morpho butterfly group. The quantum position with the smallest odor value is the worst quantum position of the quantum morpho butterfly group.

7. The method for collaborative path planning of a swarm of drones based on the quantum morpho mechanism according to claim 1, characterized in that: The greedy selection strategy described in step 7 is used to determine the quantum position of the next generation of quantum morpho butterflies, including: Calculate the odor values ​​emitted by all quantum butterflies at the original quantum position and the newly generated quantum position, and calculate the odor values ​​of all quantum butterflies at the quantum position set. The greedy method selects the quantum position with the larger smell value as the quantum position of the next generation quantum morpho butterfly 8. The method for collaborative path planning of a drone swarm based on the quantum morpho mechanism according to claim 1, characterized in that: The evolution termination judgment in step 8 and the output of the UAV cluster route and speed matrix include: Determine whether the maximum number of iterations G has been reached. If not, set g = g + 1 and return to step five. If reached, terminate the mechanism evolution and cut and reconstruct the optimal quantum position mapping state of the last generation of quantum morpho butterfly swarm and output it.

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