Multi-unmanned aerial vehicle cooperative path planning method based on adaptive multi-population hybrid algorithm

Abstract

The invention provides a multi-unmanned aerial vehicle cooperative path planning method based on an adaptive multi-population hybrid algorithm, and the method comprises the steps: 1, constructing a multi-unmanned aerial vehicle cooperative path planning model which comprises path length limitation, flight height limitation, maneuvering constraint and obstacle avoidance limitation of unmanned aerial vehicles, cooperative constraint between the unmanned aerial vehicles, and an objective function; and 2, applying the adaptive multi-population hybrid algorithm to a multi-unmanned aerial vehicle cooperative path planning problem in a three-dimensional complex scene, and obtaining a multi-unmanned aerial vehicle cooperative path planning scheme. According to the method, the multi-unmanned aerial vehicle cooperative path planning problem in the three-dimensional scene can be solved, and the optimal multi-unmanned aerial vehicle flight path is obtained, so that the purposes of reducing the path planning cost and improving the unmanned aerial vehicle flight path are achieved.

Description

Technical Field

[0001] The present invention belongs to the field of multi-UAV collaborative path planning, and specifically is a multi-UAV collaborative path planning method with an adaptive multi-population hybrid algorithm. Background Art

[0002] Drones have been widely used in military and civil fields such as military attacks, smart agriculture, environmental monitoring, and search and rescue missions in disaster scenarios due to their low cost, high flexibility, and good performance. With the development of the times and the advancement of technology, the tasks that need to be performed by drones are becoming more and more complex. A single drone cannot complete all tasks, so multiple drones are needed to complete the tasks together. Drone path planning is the key to drones completing tasks, especially in military combat environments. Drones must be able to plan paths intelligently, avoid threats such as enemy radars, missiles, and artillery, and complete tasks as quickly as possible while ensuring their own safety. As a result, multi-drone collaborative path planning in complex environments has become one of the hot issues in the field of drone research.

[0003] Multi-UAV collaborative path planning is an NP problem and a key component of military attack missions. The goal of path planning is to reach the mission point safely at the lowest cost. During flight, UAVs must not only avoid obstacles, but also keep a safe distance between multiple UAVs to avoid collisions. Methods for solving UAV path planning problems include classical algorithms and intelligent optimization algorithms. In the early path planning algorithms, classical algorithms are more popular, such as the rapidly explored random tree (RRT) method, Voronoi diagram, probabilistic roadmap (PRM), A* algorithm, and artificial potential field (APF). These methods are relatively simple to implement and can solve path planning problems in simple scenarios. They are suitable for solving path planning problems in two-dimensional scenarios. However, when the planning space becomes complex, especially for path planning problems in three-dimensional environments, the complexity of the solution will be particularly high and it is easy to fall into local optimality.

[0004] As the problem becomes more complex, classical algorithms are no longer suitable for solving UAV path planning problems. Intelligent optimization methods are gradually being used to solve multi-UAV path planning problems in three-dimensional scenarios, such as particle swarm optimization algorithm (PSO), genetic algorithm (GA), ant colony optimization algorithm (ACO), artificial bee colony algorithm (ABC), fruit fly optimization algorithm (FOA), differential evolution algorithm (DE), pigeon-inspired optimization algorithm (PIO), grey wolf optimization algorithm (GWO), etc. However, a single intelligent optimization method still has problems such as slow convergence, easy to fall into local optimality, and poor global search when solving complex multi-constrained optimization problems of multi-UAV collaborative path planning. Summary of the invention

[0005] The present invention aims to solve the shortcomings of the above-mentioned prior art and proposes a multi-UAV collaborative path planning method based on an adaptive multi-population hybrid algorithm, in order to solve the multi-UAV collaborative path planning problem in a three-dimensional scene, thereby ensuring that multiple UAVs safely reach the target point and complete the flight mission, and reducing the cost of multi-UAV collaborative path planning to obtain the optimal multi-UAV path planning solution.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0007] The invention discloses a multi-UAV collaborative path planning method based on an adaptive multi-population hybrid algorithm, which is applied to a multi-UAV collaborative path planning method for a group of N UAVs from a known starting position. Fly to their respective target locations In a three-dimensional flight scene, obstacles with known positions are set in the three-dimensional flight scene, wherein: represents the starting position of the nth drone, represents the target point position of the nth UAV, and the multi-UAV collaborative path planning method is performed according to the following steps:

[0008] Step 1: Construct the objective function of the multi-UAV collaborative path planning model;

[0009] Step 1.1: Let the flight path of the nth drone be ,in, is the number of trajectory points, represents the kth trajectory point of the nth UAV, and The coordinates of , and use formula (1) to establish the flight length model of the nth UAV :

[0010] (1)

[0011] In formula (1), Indicates that the nth drone is from Track points To Track Points Flight distance;

[0012] Step 1.2: Use equations (2) and (3) to construct the flight altitude model of the nth UAV :

[0013] (2)

[0014] (3)

[0015] In formula (2) and formula (3), is the nth drone at k trajectory point The flight altitude model, is the minimum altitude at which the drone can fly. is the maximum altitude at which the drone can fly. Indicates that the nth drone is in Track points The flight altitude, Indicates that the nth drone is in -1 track point Flight altitude;

[0016] Step 1.3: Construct the maneuvering physical constraint model of the nth UAV, including: the deflection angle constraint model of the nth UAV And the climbing angle constraint model of the nth UAV ;

[0017] Step 1.4: Construct the obstacle threat model of the nth UAV, including: the radar threat model of the nth UAV and the missile threat model of the nth drone ;

[0018] Step 1.5: Construct the coordination model of the nth UAV, including: time coordination model and spatial collaboration models ;

[0019] Step 1.6: Use equation (16) to construct the objective function of the multi-UAV collaborative path planning model :

[0020] (16)

[0021] In formula (16), , , , , are 5 weight factors;

[0022] Step 2: Use the adaptive multi-population hybrid algorithm to solve the multi-UAV collaborative path model and obtain the multi-UAV path planning solution with the lowest path planning cost.

[0023] The multi-UAV collaborative path planning method based on the adaptive multi-population hybrid algorithm of the present invention is also characterized in that the step 1.3 includes:

[0024] Step 1.3.1: Use equations (4) and (5) to construct the deflection angle model and climb angle model of the nth UAV respectively:

[0025] (4)

[0026] (5)

[0027] In formula (4) and formula (5), Indicates that the nth drone is at the kth trajectory point The deflection angle, Indicates that the nth drone is at the kth trajectory point The climbing angle, Represents the k+1th trajectory point of the nth drone The coordinates of

[0028] Step 1.3.2: Use equations (6) and (7) to construct the deflection angle constraint model of the nth UAV ;

[0029] (6)

[0030] (7)

[0031] In formula (6) and formula (7), is the nth drone at the kth trajectory point The deflection angle constraint, is the deflection angle of the nth UAV at the kth trajectory point, is the maximum deflection angle of the UAV;

[0032] Step 1.3.3: Use equations (8) and (9) to construct the climb angle constraint model of the nth UAV. :

[0033] (8)

[0034] (9)

[0035] In formula (8) and formula (9), is the nth drone at the kth trajectory point The climb angle constraint, is the climbing angle of the nth UAV at the kth trajectory point, is the maximum climb angle of the UAV.

[0036] Further, the step 1.4 includes:

[0037] Step 1.4.1: Use equation (10) to construct the radar model:

[0038] (10)

[0039] In formula (10), Represents any three-dimensional coordinate in a three-dimensional flight scene, It refers to the area that the radar can cover. is the location of the radar area center, is the radius of the radar;

[0040] Step 1.4.2: Use equation (11) to construct the radar threat model of the nth drone :

[0041] (11)

[0042] In formula (11), Indicates the nth drone Track points and Track points The distance between Indicates the nth drone Track points With Track points The trajectory segment Two intersection points with the radar area and The distance between Indicates the nth drone Track points Second intersection with the radar area The distance between Indicates the nth drone Track points First intersection with the radar area The distance between Represents a trajectory segment The distance from the midpoint to the center of the radar area;

[0043] Step 1.4.3: Use equation (12) to construct the missile model :

[0044] (12)

[0045] In formula (12), It is the area that the missile can cover. is the coordinate of the center of the missile's bottom surface, represents the radius of the missile's bottom surface, Indicates the missile's altitude;

[0046] Step 1.4.4: Use equation (13) to construct the missile threat model:

[0047] (13)

[0048] In formula (13), Indicates the nth drone Track points and Track points The distance between Indicates the nth drone Track points and Track points The trajectory segment Two intersection points with the missile area and The distance between Indicates the nth drone Track points Second intersection with the missile area The distance between Indicates the nth drone Track points First intersection with missile area The distance between express The distance from the midpoint of the missile to the center of the missile bottom circle.

[0049] Furthermore, the step 1.5 includes:

[0050] Step 1.5.1: Use equation (14) to construct the time coordination model of the nth UAV :

[0051] (14)

[0052] In formula (14), is the flight time of the nth drone, Indicates the time it takes for the first drone to reach the corresponding target point. Indicates the buffer time for the remaining drones after the first one completes its flight mission;

[0053] Step 1.5.2: Use equation (15) to construct the spatial coordination model of the nth UAV :

[0054] (15)

[0055] In formula (15), Indicates The kth path point of the UAV With The first drone Waypoints The distance is the penalty coefficient for drones violating the space safety distance, The safe distance between drones.

[0056] Furthermore, the step 2 comprises:

[0057] Step 2.1: Initialize the starting and target locations of the mission, the number of drones N, the locations of radar obstacles, the locations of missiles, and the number of path points K;

[0058] Step 2.2: Initialize the population size to NP, the maximum number of iterations to itermax, the dimension of individuals in the population to D=N×K, define the current number of iterations to t, and initialize t=1;

[0059] Step 2.3: Move N drones from the starting position Fly to their respective target locations The path planning scheme is encoded as an individual, so that the i-th individual in the t-th generation population is obtained ,in, represents the i-th individual The position of the jth dimension of The first drone Waypoint locations; Indicates rounding up, and % indicates remainder;

[0060] Step 2.4: Calculate the i-th individual in the t-th generation population using formula (16): The fitness value of And find the top three wolves with the smallest fitness value from the t-th generation population and record them as the t-th generation population Wolf , Wolf and Wolf ;in, represents the population in the tth generation The j-th dimension position of the wolf individual, represents the population in the tth generation The j-th dimension position of the wolf individual, represents the population in the tth generation The j-th dimension position of the wolf individual;

[0061] Step 2.5: If the individuals in the tth generation population belong to the top three levels of wolves, use formula (17) to update , and , get the updated Wolf individual , updated Wolf individual , updated Wolf individual ;

[0062] (17)

[0063] In formula (17), represents the gray wolf parameter of the tth iteration, and , and represents the j-dimensional position of the r1th and r2th individuals randomly selected from the tth generation population, and ; represents the first random number of the tth generation between 0 and 1;

[0064] If the i-th individual in the t-th generation population does not belong to the top three levels of wolves, then the updated i-th individual is calculated using formula (18): ; Thus we get the tth generation update population ;

[0065] (18)

[0066] In formula (18), , and They are respectively Wolf individual, Wolf wolf individuals and The j-th element of the i-th individual affected by the wolf individual, , and They are respectively Wolf individual, Wolf individuals and The weight of the individual wolf;

[0067] Step 2.6: Use formula (16) to calculate the updated i-th individual The fitness value of ,like < , then As the i-th individual in the intermediate population of the t-th generation Otherwise, As the i-th individual in the intermediate population of the t-th generation ; Thus we get the intermediate population of the tth generation;

[0068] Step 2.7: If t≤(2 / 3)×itermax, the t-th generation renewal population is divided according to the flight heights of the individuals in the t-th generation intermediate population, and the first Δ individuals with the highest flight height are divided into the t-th generation high-altitude sub-population, the last Δ individuals are divided into the t-th generation low-altitude sub-population, and the middle Δ individuals are divided into the t-th generation medium-altitude sub-population; otherwise, the t-th generation renewal population is divided according to the fitness values ​​of the individuals in the t-th generation intermediate population, and the first Δ individuals with the smallest fitness value are divided into the t-th generation better sub-population, the middle Δ individuals are divided into the t-th generation medium sub-population, and the last Δ individuals are divided into the t-th generation worse sub-population;

[0069] Step 2.8: When t≤(2 / 3)itermax, if the updated i-th individual belongs to the high-altitude subpopulation, then according to formula (19), the ith mutant individual of the tth generation is obtained ,in, The j-dimensional position of the i-th mutant individual in the t-generation population represented by;

[0070] (19)

[0071] In formula (19), and are randomly selected from the hollow subpopulation of the t-th generation update population. Individual and The j-th dimension position of each individual, and , is the first randomly selected from the high-altitude population of the t-th generation update population. The j-th dimension position of each individual; represents the second random number of the tth generation between 0 and 1;

[0072] like Belongs to the low-altitude subpopulation, then according to formula (20) we get the ith mutant individual of the tth generation: ;

[0073] (20)

[0074] In formula (20), , ,and is the first randomly selected population from the empty subpopulation of the t-th generation update population. Individual, Individual, The j-th dimension position of an individual, ; represents the third random number of the tth generation between 0 and 1;

[0075] like Belongs to the hollow subpopulation, then according to formula (21) we get the ith mutant individual of the tth generation: :

[0076] (twenty one)

[0077] In formula (21), , ,and is the first randomly selected population from the empty subpopulation of the t-th generation update population. Individual, Individual, The j-th dimension position of an individual, and is the first randomly selected population from the low-space subpopulation of the t-th generation renewal population. Individual, The j-th dimension position of an individual, ; represents the fourth random number of the tth generation between 0 and 1;

[0078] When t>(2 / 3)itermax, if the updated i-th individual belongs to a better subpopulation, then according to formula (22), the ith mutant individual of the tth generation is obtained ;

[0079] (twenty two)

[0080] In formula (22), is the j-th dimension position of the individual with the best fitness value in the t-th generation updated population, and are randomly selected from the better subpopulations of the t-th generation update population. Individual, The j-th dimension position of an individual, , F is the mutation operator;

[0081] like Belongs to the medium subpopulation, then according to formula (23) we get the ith mutant individual of the tth generation ;

[0082] (twenty three)

[0083] In formula (23), It represents the j-th dimension position of the individual with the best fitness value in the medium sub-population of the t-th generation updated population. and are randomly selected from the medium subpopulation of the t-th generation update population. Individual, The j-th dimension position of an individual, ;

[0084] like Belonging to the inferior subpopulation, according to formula (24), the ith mutant individual of the tth generation is obtained :

[0085] (twenty four)

[0086] In formula (24), and are randomly selected from the inferior subpopulation of the t-th generation update population. Individual, The j-th dimension position of an individual, represents the first randomly selected from the tth generation update population The j-th dimension position of an individual, ;

[0087] Step 2.9: Generate the i-th crossover individual of the t-th generation using formula (26) :

[0088] (25)

[0089] In formula (25), is the crossover operator, is a random integer between [1,D], represents the fifth random number of the tth generation between 0 and 1, yes The j-th dimension position in ;

[0090] Step 2.10: Calculate the i-th crossover individual vector according to formula (16): The fitness value of , and thus use formula (26) to get the i-th individual in the t+1 generation population :

[0091] (26)

[0092] Step 2.11: Assign t+1 to t. If t>itermax, it means that the itermax-th generation population is obtained, and the individual with the smallest fitness value is selected from it as the multi-UAV collaborative path planning solution. Otherwise, return to step 2.4 and execute sequentially.

[0093] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the multi-UAV collaborative path planning method, and the processor is configured to execute the program stored in the memory.

[0094] The present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, which is characterized in that when the computer program is run by a processor, the steps of the multi-UAV collaborative path planning method are executed.

[0095] Compared with the prior art, the present invention has the following beneficial effects:

[0096] 1. The present invention proposes a multi-UAV collaborative path planning model. In addition to considering the path length cost, flight altitude limit, UAV maneuver constraints and obstacle avoidance constraints, the model also considers the collaborative constraints between UAVs, and then establishes a complete multi-constraint objective function. Therefore, when dealing with the multi-UAV path planning problem in three-dimensional complex scenes, the model can solve the path planning scheme and complete the multi-UAV path planning task.

[0097] 2. The present invention applies an adaptive multi-population hybrid algorithm to the problem of multi-UAV collaborative path planning. In the algorithm, the position update formula of the gray wolf individual is improved to enhance development. In the early stage of the algorithm, the algorithm is divided into multiple populations based on the characteristics of the solution, and different global search strategies are adopted. The information exchange between sub-populations is coordinated through cooperative pairing strategies, which improves the diversity and exploration of the population. In the later stage of the algorithm, the population is divided based on the fitness value, allowing it to learn from the excellent solution and accelerate convergence. In this way, the multi-UAV collaborative path planning solution can be solved more quickly, the flight path cost of the UAV is reduced, and the UAV is ensured to complete the flight mission safely. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0099] In an embodiment, a multi-UAV collaborative path planning method based on an adaptive multi-population hybrid algorithm is applied to a multi-UAV collaborative path planning method comprising N UAVs starting from a known starting position. Fly to their respective target locations In a three-dimensional flight scene with known obstacles, it is necessary to find a path planning solution with the lowest path planning cost to ensure that the drone can reach the target point efficiently and avoid all obstacles. represents the starting position of the nth drone, Indicates the target point position of the nth UAV, such as Figure 1 As shown, the multi-UAV collaborative path planning method is carried out in the following steps:

[0100] Step 1: Construct the objective function of the multi-UAV collaborative path planning model;

[0101] Step 1.1: Let the flight path of the nth drone be ,in, is the number of trajectory points, represents the kth trajectory point of the nth UAV, and The coordinates of The smaller the flight length of the UAV is, the faster it can complete the flight mission and consume less energy. The flight length model of the nth UAV is established using formula (1): :

[0102] (1)

[0103] In formula (1), Indicates that the nth drone is from Track points To Track Points flight distance.

[0104] Step 1.2: When a drone violates the altitude limit, the risk of completing the mission will increase. Use equations (2) and (3) to build the flight altitude model of the nth drone. :

[0105] (2)

[0106] (3)

[0107] In formula (2) and formula (3), is the nth drone at k trajectory point The flight altitude model, is the minimum altitude at which the drone can fly. is the maximum altitude at which the drone can fly. Indicates that the nth drone is in Track points The flight altitude, Indicates that the nth drone is in -1 track point flight altitude.

[0108] Step 1.3: The UAV will be subject to its own maneuverability constraints during flight. Construct the maneuverability physical constraint model of the nth UAV, including: the deflection angle constraint model of the nth UAV And the climbing angle constraint model of the nth UAV :

[0109] Step 1.3.1: Use equations (4) and (5) to construct the deflection angle model and climb angle model of the nth UAV respectively:

[0110] (4)

[0111] (5)

[0112] In formula (4) and formula (5), Indicates that the nth drone is at the kth trajectory point The deflection angle, Indicates that the nth drone is at the kth trajectory point The climbing angle, Represents the k+1th trajectory point of the nth drone The coordinates of .

[0113] Step 1.3.2: Use equations (6) and (7) to construct the deflection angle constraint model of the nth UAV ;

[0114] (6)

[0115] (7)

[0116] In formula (6) and formula (7), is the nth drone at the kth trajectory point The deflection angle constraint, is the deflection angle of the nth UAV at the kth trajectory point, is the maximum deflection angle of the drone.

[0117] Step 1.3.3: Use equations (8) and (9) to construct the climb angle constraint model of the nth UAV. :

[0118] (8)

[0119] (9)

[0120] In formula (8) and formula (9), is the nth drone at the kth trajectory point The climb angle constraint, is the climbing angle of the nth UAV at the kth trajectory point, is the maximum climb angle of the UAV.

[0121] Step 1.4: The drone should avoid encountering radar and missile obstacles during flight to ensure that it can reach the target point safely. Construct the obstacle threat model of the nth drone, including: radar threat model of the nth drone and the missile threat model of the nth drone :

[0122] Step 1.4.1: Use equation (10) to construct the radar model:

[0123] (10)

[0124] In formula (10), Represents any three-dimensional coordinate in a three-dimensional flight scene, It refers to the area that the radar can cover. is the location of the radar area center, is the radius of the radar.

[0125] Step 1.4.2: Use equation (11) to construct the radar threat model of the nth drone :

[0126] (11)

[0127] In formula (11), Indicates the nth drone Track points and Track points The distance between Indicates the nth drone Track points With Track points The trajectory segment Two intersection points with the radar area and The distance between Indicates the nth drone Track points Second intersection with the radar area The distance between Indicates the nth drone Track points First intersection with the radar area The distance between Represents a trajectory segment The distance from the midpoint of the radar area to the center of the radar area.

[0128] Step 1.4.3: Use equation (12) to construct the missile model :

[0129] (12)

[0130] In formula (12), It is the area that the missile can cover. is the coordinate of the center of the missile's bottom surface, represents the radius of the missile's bottom surface, Indicates the missile's altitude.

[0131] Step 1.4.4: Use equation (13) to construct the missile threat model:

[0132] (13)

[0133] In formula (13), Indicates the nth drone Track points and Track points The distance between Indicates the nth drone Track points and Track points The trajectory segment Two intersection points with the missile area and The distance between Indicates the nth drone Track points Second intersection with the missile area The distance between Indicates the nth drone Track points First intersection with missile area The distance between express The distance from the midpoint of the missile to the center of the missile bottom circle.

[0134] Step 1.5: In multi-UAV path planning, it is necessary to consider the coordination between UAVs, including time coordination and space coordination. Time coordination means that the time for multiple UAVs to reach the corresponding target point should meet certain requirements. When this constraint is violated, time coordination costs will be generated; space coordination refers to the constraints between multiple UAVs in the flight space, which means that UAVs must maintain a safe distance. When this constraint is violated, space coordination costs will be generated. Construct the coordination model of the nth UAV, including: time coordination model and spatial collaboration models .

[0135] Step 1.5.1: Use equation (14) to construct the time coordination model of the nth UAV :

[0136] (14)

[0137] In formula (14), is the flight time of the nth drone, Indicates the time it takes for the first drone to reach the corresponding target point. Indicates the buffer time for the remaining drones after the first one completes its flight mission;

[0138] Step 1.5.2: Use equation (15) to construct the spatial coordination model of the nth UAV :

[0139] (15)

[0140] In formula (15), Indicates The kth path point of the UAV With The first drone Waypoints The distance is the penalty coefficient for drones violating the space safety distance, The safe distance between drones.

[0141] Step 1.6: Use equation (16) to construct the objective function of the multi-UAV collaborative path planning model :

[0142] (16)

[0143] In formula (16), , , , , There are 5 weighting factors.

[0144] Step 2: Use the adaptive multi-population hybrid algorithm based on species to solve the multi-unmanned collaborative path model. The adaptive multi-population hybrid algorithm includes the improved gray wolf optimization algorithm and the adaptive multi-population differential evolution algorithm. It improves the position update formula of gray wolf individuals and enhances development. In the early stage of the algorithm, it is divided into multiple populations based on the characteristics of the solution, adopts different global search strategies, and coordinates information exchange between populations through cooperative pairing strategies, which improves the diversity and exploration of the population. In the later stage of the algorithm, the population is divided based on the fitness value, allowing it to learn from the excellent solution and accelerate convergence. In order to better solve the multi-UAV collaborative path planning problem, the adaptive multi-population hybrid algorithm is carried out in the following steps:

[0145] Step 2.1: Initialize the starting and target locations of the mission, the number of drones N, the locations of radar obstacles, the locations of missiles, and the number of path points K;

[0146] Step 2.2: Initialize the population size to NP, the maximum number of iterations to itermax, the dimension of individuals in the population to D=N×K, define the current number of iterations to t, and initialize t=1;

[0147] Step 2.3: Move N drones from the starting position Fly to their respective target locations The path planning scheme is encoded as an individual, so that the i-th individual in the t-th generation population is obtained ,in, represents the i-th individual The position of the jth dimension of The first drone Waypoint locations; Indicates rounding up, and % indicates remainder.

[0148] Step 2.4: Calculate the i-th individual in the t-th generation population using formula (16): The fitness value of And find the top three wolves with the smallest fitness value from the t-th generation population and record them as the t-th generation population Wolf , Wolf and Wolf ;in, represents the population in the tth generation The j-th dimension position of the wolf individual, represents the population in the tth generation The j-th dimension position of the wolf individual, represents the population in the tth generation The j-th dimension position of the wolf individual.

[0149] Step 2.5: If the individuals in the tth generation population belong to the top three levels of wolves, use formula (17) to update , and , get the updated Wolf individual , updated Wolf individual , updated Wolf individual ;

[0150] (17)

[0151] In formula (17), represents the gray wolf parameter of the tth iteration, and , and represents the j-dimensional position of the r1th and r2th individuals randomly selected from the tth generation population, and ; Represents the first random number of the tth generation between 0 and 1.

[0152] If the i-th individual in the t-th generation population does not belong to the top three levels of wolves, then the updated i-th individual is calculated using formula (18): ; Thus we get the tth generation update population ;

[0153] (18)

[0154] In formula (18), , and They are respectively Wolf individual, Wolf wolf individuals and The j-th element of the i-th individual affected by the wolf individual, , and They are respectively Wolf individual, Wolf individuals and The weight of the individual wolf.

[0155] Step 2.6: Use formula (16) to calculate the updated i-th individual The fitness value of ,like < , then As the i-th individual in the intermediate population of the t-th generation Otherwise, As the i-th individual in the intermediate population of the t-th generation ; Thus we get the intermediate population of the tth generation.

[0156] Step 2.7: If t≤(2 / 3)×itermax, the t-th generation renewal population is divided according to the flight heights of the individuals in the t-th generation intermediate population, and the first Δ individuals with the highest flight height are divided into the t-th generation high-altitude sub-population, the last Δ individuals are divided into the t-th generation low-altitude sub-population, and the middle Δ individuals are divided into the t-th generation medium-altitude sub-population; otherwise, the t-th generation renewal population is divided according to the fitness values ​​of the individuals in the t-th generation intermediate population, and the first Δ individuals with the smallest fitness value are divided into the t-th generation better sub-population, the middle Δ individuals are divided into the t-th generation medium sub-population, and the last Δ individuals are divided into the t-th generation worse sub-population;

[0157] Step 2.8: When t≤(2 / 3)itermax, if the updated i-th individual belongs to the high-altitude subpopulation, then according to formula (19), the ith mutant individual of the tth generation is obtained ,in, The j-dimensional position of the i-th mutant individual in the t-generation population represented by;

[0158] (19)

[0159] In formula (19), and are randomly selected from the hollow subpopulation of the t-th generation update population. Individual and The j-th dimension position of each individual, and , is the first randomly selected from the high-altitude population of the t-th generation update population. The j-th dimension position of each individual; Represents the second random number of the tth generation between 0 and 1.

[0160] like Belongs to the low-altitude subpopulation, then according to formula (20) we get the ith mutant individual of the tth generation: ;

[0161] (20)

[0162] In formula (20), , ,and is the first randomly selected population from the empty subpopulation of the t-th generation update population. Individual, Individual, The j-th dimension position of an individual, ; represents the third random number of the tth generation between 0 and 1;

[0163] like Belongs to the hollow subpopulation, then according to formula (21) we get the ith mutant individual of the tth generation: :

[0164] (twenty one)

[0165] In formula (21), , ,and is the first randomly selected population from the empty subpopulation of the t-th generation update population. Individual, Individual, The j-th dimension position of an individual, and is the first randomly selected population from the low-space subpopulation of the t-th generation renewal population. Individual, The j-th dimension position of an individual, ; Represents the fourth random number of the tth generation between 0 and 1.

[0166] When t>(2 / 3)itermax, if the updated i-th individual belongs to a better subpopulation, then according to formula (22), the ith mutant individual of the tth generation is obtained ;

[0167] (twenty two)

[0168] In formula (22), is the j-th dimension position of the individual with the best fitness value in the t-th generation updated population, and are randomly selected from the better subpopulations of the t-th generation update population. Individual, The j-th dimension position of an individual, , F is the mutation operator;

[0169] like Belongs to the medium subpopulation, then according to formula (23) we get the ith mutant individual of the tth generation ;

[0170] (twenty three)

[0171] In formula (23), It represents the j-th dimension position of the individual with the best fitness value in the medium sub-population of the t-th generation updated population. and are randomly selected from the medium subpopulation of the t-th generation update population. Individual, The j-th dimension position of an individual, .

[0172] like Belonging to the inferior subpopulation, according to formula (24), the ith mutant individual of the tth generation is obtained :

[0173] (twenty four)

[0174] In formula (24), and are randomly selected from the inferior subpopulation of the t-th generation update population. Individual, The j-th dimension position of an individual, represents the first randomly selected from the tth generation update population The j-th dimension position of an individual, .

[0175] Step 2.9: Generate the i-th crossover individual of the t-th generation using formula (26) :

[0176] (25)

[0177] In formula (25), is the crossover operator, is a random integer between [1,D], represents the fifth random number of the tth generation between 0 and 1, yes The j-th dimension position in .

[0178] Step 2.10: Calculate the i-th crossover individual vector according to formula (16): The fitness value of , and thus use formula (26) to obtain the i-th individual in the t+1 generation population:

[0179] (26)

[0180] Step 2.11: Assign t+1 to t. If t>itermax, it means that the itermax-th generation population is obtained, and the individual with the smallest fitness value is selected from it as the multi-UAV collaborative path planning solution. Otherwise, return to step 2.4 and execute sequentially.

[0181] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0182] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

Claims

1. A multi-UAV collaborative path planning method based on an adaptive multi-population hybrid algorithm, characterized in that: It is applied to N drones from a known starting position. Fly to their respective target locations In a three-dimensional flight scene, obstacles with known positions are set in the three-dimensional flight scene, wherein: represents the starting position of the nth drone, represents the target point position of the nth UAV, and the multi-UAV collaborative path planning method is performed according to the following steps: Step 1: Construct the objective function of the multi-UAV collaborative path planning model; Step 1.1: Let the flight path of the nth drone be ,in, is the number of trajectory points, represents the kth trajectory point of the nth UAV, and The coordinates of , and use formula (1) to establish the flight length model of the nth UAV : (1) In formula (1), Indicates that the nth drone is from Track points To Track Points Flight distance; Step 1.2: Use equations (2) and (3) to construct the flight altitude model of the nth UAV : (2) (3) In formula (2) and formula (3), is the nth drone at k trajectory point The flight altitude model, is the minimum altitude at which the drone can fly. is the maximum altitude at which the drone can fly. Indicates that the nth drone is in Track points The flight altitude, Indicates that the nth drone is in -1 track point Flight altitude; Step 1.3: Construct the maneuvering physical constraint model of the nth UAV, including: the deflection angle constraint model of the nth UAV And the climbing angle constraint model of the nth UAV ; Step 1.4: Construct the obstacle threat model of the nth UAV, including: the radar threat model of the nth UAV and the missile threat model of the nth drone ; Step 1.5: Construct the coordination model of the nth UAV, including: time coordination model and spatial collaboration models ; Step 1.6: Use equation (16) to construct the objective function of the multi-UAV collaborative path planning model : (16) In formula (16), , , , , are 5 weight factors; Step 2: Use the adaptive multi-population hybrid algorithm to solve the multi-UAV collaborative path model and obtain the multi-UAV path planning solution with the lowest path planning cost.

2. The multi-UAV collaborative path planning method based on the adaptive multi-population hybrid algorithm according to claim 1 is characterized in that: The step 1.3 comprises: Step 1.3.1: Use equations (4) and (5) to construct the deflection angle model and climb angle model of the nth UAV respectively: (4) (5) In formula (4) and formula (5), Indicates that the nth drone is at the kth trajectory point The deflection angle, Indicates that the nth drone is at the kth trajectory point The climbing angle, Represents the k+1th trajectory point of the nth drone The coordinates of Step 1.3.2: Use equations (6) and (7) to construct the deflection angle constraint model of the nth UAV ; (6) (7) In formula (6) and formula (7), is the nth drone at the kth trajectory point The deflection angle constraint, is the deflection angle of the nth UAV at the kth trajectory point, is the maximum deflection angle of the UAV; Step 1.3.3: Use equations (8) and (9) to construct the climb angle constraint model of the nth UAV. : (8) (9) In formula (8) and formula (9), is the nth drone at the kth trajectory point The climb angle constraint, is the climbing angle of the nth UAV at the kth trajectory point, is the maximum climb angle of the UAV.

3. The multi-UAV collaborative path planning method based on the adaptive multi-population hybrid algorithm according to claim 2 is characterized in that: The step 1.4 comprises: Step 1.4.1: Use equation (10) to construct the radar model: (10) In formula (10), Represents any three-dimensional coordinate in a three-dimensional flight scene, It refers to the area that the radar can cover. is the location of the center of the radar area, is the radius of the radar; Step 1.4.2: Use equation (11) to construct the radar threat model of the nth drone : (11) In formula (11), Indicates the nth drone Track points and Track points The distance between Indicates the nth drone Track points With Track points The trajectory segment Two intersection points with the radar area and The distance between Indicates the nth drone Track points Second intersection with the radar area The distance between Indicates the nth drone Track points First intersection with the radar area The distance between Represents a trajectory segment The distance from the midpoint to the center of the radar area; Step 1.4.3: Use equation (12) to construct the missile model : (12) In formula (12), It is the area that the missile can cover. is the coordinate of the center of the missile's bottom surface, represents the radius of the missile's bottom surface, Indicates the missile's altitude; Step 1.4.4: Use equation (13) to construct the missile threat model: (13) In formula (13), Indicates the nth drone Track points and Track points The distance between Indicates the nth drone Track points and Track points The trajectory segment Two intersection points with the missile area and The distance between Indicates the nth drone Track points Second intersection with the missile area The distance between Indicates the nth drone Track points First intersection with missile area The distance between express The distance from the midpoint of the missile to the center of the missile bottom circle.

4. The multi-UAV collaborative path planning method based on adaptive multi-population hybrid algorithm according to claim 3 is characterized in that: The step 1.5 comprises: Step 1.5.1: Use equation (14) to construct the time coordination model of the nth UAV : (14) In formula (14), is the flight time of the nth drone, Indicates the time it takes for the first drone to reach the corresponding target point. Indicates the buffer time for the remaining drones after the first one completes its flight mission; Step 1.5.2: Use equation (15) to construct the spatial coordination model of the nth UAV : (15) In formula (15), Indicates The kth path point of the UAV With The first drone Waypoints The distance is the penalty coefficient for drones violating the space safety distance, The safe distance between drones.

5. The multi-UAV collaborative path planning method based on adaptive multi-population hybrid algorithm according to claim 4 is characterized in that: The step 2 comprises: Step 2.1: Initialize the starting and target locations of the mission, the number of drones N, the locations of radar obstacles, the locations of missiles, and the number of path points K; Step 2.2: Initialize the population size to NP, the maximum number of iterations to itermax, the dimension of individuals in the population to D=N×K, define the current number of iterations to t, and initialize t=1; Step 2.3: Move N drones from the starting position Fly to their respective target locations The path planning scheme is encoded as an individual, so that the i-th individual in the t-th generation population is obtained ,in, represents the i-th individual The position of the jth dimension of The first drone Waypoint locations; Indicates rounding up, and % indicates remainder; Step 2.4: Calculate the i-th individual in the t-th generation population using formula (16): The fitness value of And find the top three wolves with the smallest fitness value from the t-th generation population and record them as the t-th generation population Wolf , Wolf and Wolf ;in, represents the population in the tth generation The j-th dimension position of the wolf individual, represents the population in the tth generation The j-th dimension position of the wolf individual, represents the population in the tth generation The j-th dimension position of the wolf individual; Step 2.5: If the individuals in the tth generation population belong to the top three levels of wolves, use formula (17) to update , and , get the updated Wolf individual , updated Wolf individual , updated Wolf individual ; (17) In formula (17), represents the gray wolf parameter of the tth iteration, and , and represents the j-dimensional position of the r1th and r2th individuals randomly selected from the tth generation population, and ; represents the first random number of the tth generation between 0 and 1; If the i-th individual in the t-th generation population does not belong to the top three levels of wolves, then the updated i-th individual is calculated using formula (18): ; Thus we get the tth generation update population ; (18) In formula (18), , and They are respectively Wolf individual, Wolf wolf individuals and The j-th element of the i-th individual affected by the wolf individual, , and They are respectively Wolf individual, Wolf individuals and The weight of the individual wolf; Step 2.6: Use formula (16) to calculate the updated i-th individual The fitness value of ,like < , then As the i-th individual in the intermediate population of the t-th generation Otherwise, As the i-th individual in the t-th generation intermediate population ; Thus we get the intermediate population of the tth generation; Step 2.7: If t≤(2 / 3)×itermax, the t-th generation renewal population is divided according to the flight heights of the individuals in the t-th generation intermediate population, and the first Δ individuals with the highest flight height are divided into the t-th generation high-altitude sub-population, the last Δ individuals are divided into the t-th generation low-altitude sub-population, and the middle Δ individuals are divided into the t-th generation medium-altitude sub-population; otherwise, the t-th generation renewal population is divided according to the fitness values ​​of the individuals in the t-th generation intermediate population, and the first Δ individuals with the smallest fitness value are divided into the t-th generation better sub-population, the middle Δ individuals are divided into the t-th generation medium sub-population, and the last Δ individuals are divided into the t-th generation worse sub-population; Step 2.8: When t≤(2 / 3)itermax, if the updated i-th individual belongs to the high-altitude subpopulation, then according to formula (19), the ith mutant individual of the tth generation is obtained ,in, The j-dimensional position of the i-th mutant individual in the t-generation population represented by; (19) In formula (19), and are randomly selected from the hollow subpopulation of the t-th generation update population. Individual and The j-th dimension position of each individual, and , is the first randomly selected from the high-altitude population of the t-th generation update population. The j-th dimension position of each individual; represents the second random number of the tth generation between 0 and 1; like Belongs to the low-altitude subpopulation, then according to formula (20) we get the ith mutant individual of the tth generation: ; (20) In formula (20), , ,and is the first randomly selected population from the empty subpopulation of the t-th generation update population. Individual, Individual, The j-th dimension position of an individual, ; represents the third random number of the tth generation between 0 and 1; like Belongs to the hollow subpopulation, then according to formula (21) we get the ith mutant individual of the tth generation: : (21) In formula (21), , ,and is the first randomly selected population from the empty subpopulation of the t-th generation update population. Individual, Individual, The j-th dimension position of an individual, and is the first randomly selected population from the low-space subpopulation of the t-th generation renewal population. Individual, The j-th dimension position of an individual, ; represents the fourth random number of the tth generation between 0 and 1; When t>(2 / 3)itermax, if the updated i-th individual belongs to a better subpopulation, then according to formula (22), the ith mutant individual of the tth generation is obtained ; (22) In formula (22), is the j-th dimension position of the individual with the best fitness value in the t-th generation updated population, and are randomly selected from the better subpopulations of the t-th generation update population. Individual, The j-th dimension position of an individual, , F is the mutation operator; like Belongs to the medium subpopulation, then according to formula (23) we get the ith mutant individual of the tth generation ; (23) In formula (23), It represents the j-th dimension position of the individual with the best fitness value in the medium sub-population of the t-th generation updated population. and are randomly selected from the medium subpopulation of the t-th generation update population. Individual, The j-th dimension position of an individual, ; like Belonging to the inferior subpopulation, according to formula (24), the ith mutant individual of the tth generation is obtained : (24) In formula (24), and are randomly selected from the inferior subpopulation of the t-th generation update population. Individual, The j-th dimension position of an individual, represents the first randomly selected from the tth generation update population The j-th dimension position of an individual, ; Step 2.9: Generate the i-th crossover individual of the t-th generation using formula (26) : (25) In formula (25), is the crossover operator, is a random integer between [1,D], represents the fifth random number of the tth generation between 0 and 1, yes The j-th dimension position in ; Step 2.10: Calculate the i-th crossover individual vector according to formula (16): The fitness value of , and thus use formula (26) to get the i-th individual in the t+1 generation population : (26) Step 2.11: Assign t+1 to t. If t>itermax, it means that the itermax-th generation population is obtained, and the individual with the smallest fitness value is selected as the multi-UAV collaborative path planning solution. Otherwise, return to step 2.4 and execute sequentially.

6. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the multi-UAV collaborative path planning method described in any one of claims 1-5, and the processor is configured to execute the program stored in the memory.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-UAV collaborative path planning method described in any one of claims 1 to 5 are executed.

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