Multi-objective optimization unmanned aerial vehicle cluster dynamic trajectory planning and power control method

Abstract

The invention provides a multi-objective optimization unmanned aerial vehicle cluster dynamic trajectory planning and power control method. The method comprises the following steps: establishing a deployment and trajectory planning model of an unmanned aerial vehicle cluster; a ground-to-air channel model and an air-to-air channel model are provided; defining user data transmission time delay to evaluate the data collection performance of the unmanned aerial vehicle cluster; proposing an energy consumption model of an unmanned aerial vehicle cluster and a ground user in a data collection process; modeling an unmanned aerial vehicle cluster data collection problem into a multi-objective optimization problem; establishing a user-cluster tail unmanned aerial vehicle connection relationship through a Fermat point construction method; an improved non-dominated sorting whale optimization algorithm is adopted to solve a simplified multi-objective optimization problem, so that a Pareto frontier solution set is obtained by optimizing the access sequence of the unmanned aerial vehicles and the transmitting power of the unmanned aerial vehicles and users under the constraint conditions of user data collection and unmanned aerial vehicle cluster movement range limitation, and a multi-objective multi-objective optimization solution set is obtained by optimizing the access sequence of the unmanned aerial vehicles and the transmitting power of the unmanned aerial vehicles and the users. And efficient data collection of the unmanned aerial vehicle cluster is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of UAV trajectory planning, and specifically relates to a multi-objective optimization method for dynamic trajectory planning and power control of UAV swarms. Background Art

[0002] With the rapid development and increasing maturity of emerging frontier technologies such as the Internet of Things, artificial intelligence, and the Sixth Generation of communication system (6G), the 6G Aerial Access Networks (AAN) are required to have higher data transmission rates, lower latency, and higher reliability. The low-altitude aircraft technology mainly based on UAVs can achieve high-quality, high-rate, and full-domain communication with the characteristics of rapid deployment, flexible adjustment, and strong reconfigurability to cope with changing environments and requirements and ensure continuous and stable communication. The UAV swarm network is an infrastructure for meeting the low-altitude development needs and providing new information services. It is a new infrastructure configuration element for low-altitude development, which can support the realization of low-altitude networking, digitization, and intelligence. The UAV swarm does not rely on existing basic communication facilities and can dynamically and quickly construct a distributed and centerless network technology with the advantages of self-organization, self-recovery, and high anti-destruction. The rate can be adaptive, the bandwidth can be allocated on demand, UAVs can temporarily join or leave, multi-hop relaying can be performed, the nodes have high mobility and flexibility, the network topology can change dynamically, and services such as three-dimensional coverage, flexible access, self-organized transmission, and task offloading can be supported, with a wide range of application scenarios.

[0003] However, for the problem of collecting data in large-scale complex three-dimensional terrain areas, especially considering time-sensitive users, transmission delay is an important indicator. In addition, the deployment and flight trajectory planning of UAV swarms become increasingly difficult. In this case, it is crucial to comprehensively consider the energy efficiency of UAVs, the energy consumption of ground users, and the transmission delay of ground users. Therefore, how to flexibly deploy UAV swarms, dynamically adjust UAV flight trajectories, and perform reasonable power control to meet the energy consumption and transmission delay requirements of ground users in remote areas has become a key issue. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for dynamic trajectory planning and power control of UAV swarms based on multi-objective optimization to address the defects or problems of the prior art. A hierarchical UAV swarm architecture is constructed to provide data collection services for time-sensitive ground users in remote areas. By jointly optimizing the dynamic deployment, trajectory planning, and power control of UAV swarms, a multi-objective optimization method is designed to simultaneously minimize the total energy consumption of UAV swarms, the average energy consumption of users, and the average transmission delay of users, so as to achieve efficient data collection for a large number of remote users.

[0005] To achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0006] A multi-objective optimization method for dynamic trajectory planning and power control of an unmanned aerial vehicle (UAV) cluster, comprising the following steps:

[0007] Step 1: Propose a data collection scenario for the 6G air interface network, design a hierarchical UAV cluster architecture model, and establish a deployment and trajectory planning model for the UAV cluster;

[0008] Step 2: Based on the UAV cluster in Step 1, considering providing data collection services for randomly distributed ground users, establish a data collection model for users - cluster - tail UAVs - cluster - head UAVs, and design ground - to - air and air - to - air channel models respectively;

[0009] Step 3: Based on the data collection model obtained in Step 2, define the user transmission delay to evaluate the data collection performance of the UAV cluster, and considering the energy consumption of the UAV cluster and ground users, propose energy consumption models for the UAV cluster and ground users respectively;

[0010] Step 4: Based on the user transmission delay, the energy consumption of the UAV cluster and ground users in Step 3, model the UAV cluster data collection problem as a multi - objective optimization problem;

[0011] Step 5: Based on the multi - objective optimization problem proposed in Step 4, use the K - means - based clustering method to cluster users, divide regions by constructing a Voronoi diagram, and determine the scale and deployment location of the UAV cluster;

[0012] Step 6: Based on the UAV cluster deployment in Step 5, establish the connection relationship between ground users and cluster - tail UAVs by constructing the Fermat point method, and simplify the initial multi - objective optimization problem;

[0013] Step 7: Propose an improved non - dominated sorting whale optimization algorithm to solve the simplified multi - objective optimization problem in Step 6. By optimizing the access order of UAVs and the transmission power between UAVs and users, obtain the Pareto - front solution set to achieve efficient data collection of the UAV cluster.

[0014] To optimize the above technical solution, the specific measures also include:

[0015] Step 1 includes:

[0016] Step 1 - 1: Propose a data collection scenario for the 6G air interface network, including defining the set of randomly distributed ground users as and the hierarchical UAV cluster as where the UAV cluster The cluster - head UAV acting as an air - based station and Ms A cluster - tail UAV responsible for data collection and relay It is composed of, where U is the number of ground users, S is the number of UAV cluster layers, and h S is the number of cluster - head UAVs, is the number of cluster - tail UAVs in the s - th layer,

[0017] Step 1 - 2: Model the data - collection scenario according to the three - dimensional Cartesian coordinate system as:

[0018]

[0019] where Θ represents the spatial range of the data - collection scenario, (x, y, z) represents the three - dimensional coordinates of any point in space, X min 、Y min and Z min respectively specify the lower limits of the ranges of this point on the three coordinate axes, and X max 、Y max and Z max specify the upper limits of the ranges. Therefore, the three - dimensional coordinates of the ground user u, the cluster - head UAV h s and the cluster - tail UAV are represented as q u =(x u ,y u ,z u ), q s =(x s ,y s ,z s ) and Therefore, the distance d between the user u and the cluster - tail UAV u,m is expressed as:

[0020]

[0021] Similarly, the distance d between the cluster - tail UAV s and the cluster - head UAV h m,s is expressed as:

[0022]

[0023] Step 1 - 3: For the UAV cluster Among them, the cluster - head UAV h s hovers at the deployment point q s and is responsible for receiving the relay data from the cluster - tail UAVs. The cluster - tail UAVs need to fly to different hovering points to provide data - collection services for the ground users. Therefore, the trajectory of the cluster - tail UAV is modeled as the access order of different hovering points:

[0024]

[0025] Among them, represents the initial position of the cluster-tail UAV and represents the total number of hovering points of the cluster-tail UAV.

[0026] Step 2 includes:

[0027] Step 2-1: Considering providing data collection services for randomly distributed ground users, the ground-to-air channel model established between the ground user u and the cluster-tail UAV is:

[0028]

[0029] where h u,m is the channel gain of the communication link, is the small-scale fading channel gain, is the free-space fading channel gain; is the transmission rate between the user u and the cluster-tail UAV , B u,m is the channel bandwidth between the user u and the cluster-tail UAV , p u is the transmission power of the user u, σ 2 is the average noise power spectral density,

[0030] Step 2-2: After the cluster-tail UAV completes the data collection task of the ground user, it forwards the data to the cluster-head UAV, and the air-to-air channel model is modeled as:

[0031]

[0032] Among them, is the transmission rate between the cluster-tail UAV and the cluster-head UAV h s , B m,s is the channel bandwidth between the cluster-tail UAV and the cluster-head UAV h s , is the transmission power of the cluster-tail UAV, η LoS is the average path loss of the line-of-sight link.

[0033] Step 3 includes:

[0034] Step 3-1: The user transmission delay T of the user u u is defined as:

[0035]

[0036] Among them, is a binary variable used to represent the connection relationship between user u and the cluster head UAV , T u,m and T m,s respectively represent the transmission delays of the ground-to-air channel and the air-to-air channel, represents the hovering point of the nth cluster head UAV;

[0037] Step 3-2: The energy consumption of the UAV cluster includes two parts, the communication energy consumption and the movement energy consumption of the UAV. The energy consumption E m of the cluster head UAV is expressed as:

[0038]

[0039] Among them is the transmission energy consumption of the cluster head UAV, while and respectively represent the hovering energy consumption and the flight energy consumption of the cluster head UAV. The energy consumption E s of the cluster head UAV is expressed as:

[0040]

[0041] Among them, P hov represents the hovering power of the cluster head UAV, and T s represents the hovering time of the cluster head UAV,

[0042] Step 3-3: According to the ground-to-air channel model in Step 2, the transmission energy consumption of the ground user is expressed as

[0043]

[0044] Step 4 includes:

[0045] Step 4-1: According to the user transmission delay in Step 3 and the transmission energy consumption model of the ground user, the three optimization objectives to be jointly optimized are expressed as:

[0046]

[0047] Among them, A = {q s , M s , Φ, P, γ} is the set of optimization variables, is the deployment location of the UAV cluster, is the flight trajectory of the cluster head UAV, is the power control variable, is the transmission power of the cluster head UAV, is a connection relationship variable between ground users and cluster - head UAVs. In addition, the objective f1(A) is the total energy consumption TEU of the UAV cluster, f2(A) is the average energy consumption AEG of ground users, and f3(A) is the average transmission delay ADG of ground users.

[0048] Step 4 - 2: Jointly optimize the dynamic deployment, trajectory planning, and power of the UAV cluster. The overall objective of USDC - MOP is to simultaneously minimize the three objectives TEU, AEG, and ADG, expressed as:

[0049]

[0050]

[0051] Among them, constraint C1 specifies the maximum number M of cluster - head UAVs allocated to the UAV cluster max , constraint C2 stipulates the total number M of cluster - head UAVs, and constraints C3 and C4 respectively specify the minimum ground - to - air and air - to - air channel transmission rates and Constraint C5 stipulates that each ground user can only make one connection with one cluster - head UAV at one hovering point. Constraint C6 limits the maximum number of ground users U that a cluster - head UAV can simultaneously establish connection relationships with at one hovering point. Constraint C7 stipulates the maximum transmission delay of users max Constraint C8 specifies the value range of Constraint C9 specifies the deployment range of the UAV cluster, and constraint C10 specifies the set of all access sequences of cluster - head UAVs Constraint C11 and C12 respectively stipulate the transmission power ranges of users and cluster - head UAVs. Among them, is the minimum transmission power of user u, is the maximum transmission power of user u, is the minimum transmission power of the cluster - head UAV, is the maximum transmission power of the cluster - head UAV. The maximum transmission power of the cluster - head UAV.

[0052] Step 5 includes:

[0053] Step 5 - 1: Use the K - means algorithm to cluster ground users. Divide U ground users into M classes, and obtain M cluster centers expressed as:

[0054] Step 5 - 2: Construct a Voronoi diagram with the above - mentioned cluster centers as growth points Divide the entire area to obtain each sub - area expressed as: Simultaneously obtain the intersection set of the Voronoi diagram: Based on meeting the deployment requirements, obtain the optimal UAV cluster deployment plan, that is, select the deployment location set q of the UAV cluster from Ω s and the number M of cluster head UAVs allocated in the cluster s .

[0055] Step 6 includes:

[0056] Step 6-1: Construct the Fermat point based on the user distribution in each sub-region and establish the user-cluster head UAV connection relationship γ

[0057] Step 6-2: Based on the above algorithm, the initial multi-objective problem P0 can be simplified to:

[0058]

[0059] where:

[0060]

[0061] where B = {Φ, P} is the set of optimization variables, and the objective functions f'1(B), f'2(B), and f'3(B) are all simplified optimization objectives; since the user-cluster head UAV connection relationship has been determined, therefore and are parameters determined by user u is the ground-to-air channel transmission delay after the parameters determined by user u is the air-to-air channel transmission delay after the parameters determined by user u

[0062] Step 7 includes:

[0063] Step 7-1: Initialize relevant parameters, including the whale population size J max , the maximum number of iterations I' max and the number of objectives K

[0064] Step 7-2: Calculate the fitness of the whale individuals in the population. For each individual in the current population, calculate its fitness value according to the objective function

[0065] Step 7-3: Compare all individuals in the population, find all non-dominated individuals in the current population, and determine the non-dominated levels; for individuals within the same non-dominated level, calculate their crowding distances, and perform selection operations based on the non-dominated levels and crowding distances. Give priority to selecting individuals with lower non-dominated levels; if multiple individuals are at the same non-dominated level, select the individual with a larger crowding distance. Through such selection, select relatively excellent individuals to form a new population for subsequent iterative operations

[0066] Step 7-4: Update the positions of the whales. First, randomly select a whale individual from the new population and determine its update method, which is as follows:

[0067] Update based on the globally optimal individual: If the randomly generated probability is less than the set value, select the globally optimal individual in the current population. According to the mathematical model of hunting prey in the whale optimization algorithm, update the position of the currently selected whale individual to make it approach the globally optimal individual in the hope of finding a better solution.

[0068] Update based on a random individual: If the randomly generated probability is greater than or equal to the set value, randomly select another individual in the population. According to the mathematical model of searching for prey, update the position of the current whale individual to increase the search range and exploration ability of the population.

[0069] After updating the positions of the whale individuals, check whether their positions exceed the pre-set search space range. If they do, perform boundary processing, set the values exceeding the boundary to the boundary values, or make them return to the reasonable search space through mapping or other means.

[0070] Step 7-5: Check whether the current iteration number has reached the pre-set maximum iteration number. If it has, the algorithm ends and outputs the non-dominated solutions obtained in the current population. If it has not, return to Step 7-2 to continue the next round of iteration. Then, output the Pareto solution set and the optimization variable B = {Φ, P}.

[0071] The present invention has the following beneficial effects:

[0072] The present invention provides a multi-objective optimization method for the dynamic trajectory planning and power control of an unmanned aerial vehicle (UAV) swarm, which simultaneously minimizes the total energy consumption of the UAV swarm, the average energy consumption of users, and the average transmission delay of users under the constraints of communication and user services. The method simplifies the initial multi-objective optimization problem by using a region division method based on K-means clustering and Voronoi diagrams and a user-UAV association method based on Fermat points, and develops an improved non-dominated sorting whale optimization algorithm to simultaneously optimize the flight trajectories and transmission powers of the cluster-tail UAVs and the transmission powers of users. The present invention can obtain the Pareto front solutions of the multi-objective optimization problem while ensuring the requirements of communication and user services. Brief Description of the Drawings

[0073] Figure 1 It is a schematic diagram of the data collection scenario based on the UAV swarm involved in the present invention;

[0074] Figure 2 It is a schematic diagram of the time series of data collection by the UAV swarm designed in the present invention;

[0075] Figure 3 It is a result diagram of the region division based on the Voronoi diagram of the present invention;

[0076] Figure 4 This is the three - dimensional trajectory planning result diagram of the UAV cluster for the 60 - user scenario under the compromise solution of the present invention;

[0077] Figure 5 This is the comparison diagram of the energy consumption simulation results of the UAV cluster under different numbers of users and different optimization methods of the present invention;

[0078] Figure 6 This is the comparison diagram of the average energy consumption simulation results of ground users under different numbers of users and different optimization methods of the present invention;

[0079] Figure 7 This is the comparison diagram of the average transmission delay simulation results under different numbers of users and different optimization methods of the present invention;

[0080] Figure 8 This is the comparison diagram of the algorithm time complexity under different numbers of users and different optimization methods of the present invention;

[0081] Figure 9 This is the simulation result diagram of the user transmit power distribution under different target solutions of the present invention. Detailed implementation manners

[0082] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0083] Although the steps in the present invention are arranged with reference numerals, they are not used to limit the order of the steps. Unless the order of the steps is clearly stated or the execution of a certain step requires other steps as a basis, the relative order of the steps can be adjusted. It can be understood that the term "and / or" used herein relates to and encompasses any and all possible combinations of one or more of the associated listed items.

[0084] The following further describes the embodiments of the present invention in detail with reference to the accompanying drawings.

[0085] A multi - objective optimization UAV cluster dynamic trajectory planning and power control method provided by an embodiment of the present invention includes the following specific steps:

[0086] Step 1: A 6G air interface network data collection scenario is proposed, a hierarchical UAV cluster architecture model is designed, and a deployment and trajectory planning model of the UAV cluster is established;

[0087] Step 1 specifically includes:

[0088] Step 1-1: A 6G air interface network data collection scenario is proposed, mainly including a set of randomly distributed ground users defined as and a hierarchical UAV cluster defined as where the UAV cluster the cluster head UAV acting as an air base station and M s cluster tail UAVs responsible for data collection and relaying consist of.

[0089] Step 1-2: According to the three-dimensional Cartesian coordinate system, the data collection scenario is modeled as:

[0090]

[0091] where Θ represents the spatial scope of the data collection scenario, (x, y, z) represents the three-dimensional coordinates of any point in space, X min , Y min and Z min respectively specify the lower limits of the ranges of this point on the three coordinate axes, and X max , Y max and Z max specify the upper limits of the ranges. Therefore, the three-dimensional coordinates of the ground user u, the cluster head UAV h s and the cluster tail UAV are represented as q u =(x u , y u , z u ), q s =(x s , y s , z s ) and Therefore, the distance d between the user u and the cluster tail UAV u,m is expressed as:

[0092]

[0093] Similarly, the distance d between the cluster tail UAV s and the cluster head UAV h m,s is expressed as:

[0094]

[0095] Step 1-3: For the UAV cluster where the cluster head UAV h s at the deployment point q sThe hovering drone responsible for receiving the relay data from the cluster head drone, while the cluster head drone needs to fly to different hovering points to provide data collection services for ground users. Therefore, the trajectory of the cluster head drone

[0096]

[0097] is modeled as the access order of different hovering points: where represents the initial position of the cluster head drone and

[0098] denotes the total number of hovering points of the cluster head drone.

[0099] Step 2: Based on the drone swarm in Step 1, considering providing data collection services for randomly distributed ground users, a data collection model of user-cluster head drone-cluster drone is established, and ground-to-air and air-to-air channel models are designed respectively;

[0100] Step 2-1: Considering providing data collection services for randomly distributed ground users, the ground-to-air channel model established between the ground user u and the cluster head drone is:

[0101]

[0102] where h u,m is the channel gain of the communication link, is the small-scale fading channel gain, is the free-space fading channel gain; and represent the probabilities of line-of-sight communication and non-line-of-sight communication respectively, where η LoS and η NLoS represent the average path losses of line-of-sight communication and non-line-of-sight communication respectively; α and β are S-curve parameters determined by the environment; θ u,m is the elevation angle from the ground user to the cluster head drone; c is the speed of light, f is the carrier frequency, and d u,m is the Euclidean distance;

[0103] The transmission rate between the user u and the cluster head drone is expressed as:

[0104]

[0105] where B u,m is the channel bandwidth between the user u and the cluster head drone and p uis the transmission power of user u, and σ 2 is the average noise power spectral density.

[0106] Step 2-2: After the cluster-head UAV completes the data collection task of the ground user, it needs to forward the data to the cluster-head UAV. The air-to-air channel model is modeled as:

[0107]

[0108] where is the cluster-tail UAV and the cluster-head UAV h s the transmission rate between them, and B m,s is the cluster-tail UAV and the cluster-head UAV h s the channel bandwidth between them, is the transmission power of the cluster-tail UAV, and η LoS is the average path loss of the line-of-sight link.

[0109] Step 3: Based on the data collection model obtained in Step 2, define the user transmission delay to evaluate the data collection performance of the UAV cluster, consider the energy consumption of the UAV cluster and the ground users, and respectively propose the energy consumption models of the UAV cluster and the ground users;

[0110] Step 3 specifically includes:

[0111] Step 3-1: Figure 2 is the data collection time series diagram, where the user transmission delay T of user u u is defined as:

[0112]

[0113] where is a binary variable used to represent the connection relationship between user u and the cluster-tail UAV . T u,m and T m,s respectively represent the transmission delays of the ground-to-air channel and the air-to-air channel, expressed as

[0114]

[0115] where Q u is the amount of data transmitted by the ground user u.

[0116] Step 3-2: The energy consumption of the UAV cluster mainly includes two parts, the communication energy consumption and the mobile energy consumption of the UAVs. The energy consumption E of the cluster-tail UAV m is expressed as:

[0117]

[0118] where is the transmission energy consumption of the cluster-head UAV, while and represent the hovering energy consumption and the flight energy consumption of the cluster-head UAV respectively:

[0119]

[0120] where is the hovering time of the cluster-head UAV at the hovering point . To meet the data collection requirements of all users at this deployment location, the hovering time is expressed as the maximum user transmission delay:

[0121]

[0122] while P hov , P fly (||θ x,y ||), P ver (||θ z ||) are the hovering power, the horizontal flight power and the vertical flight power of the UAV respectively:

[0123] P hov = P0 + P1,

[0124]

[0125] P ver (||θ z ||) = Wg||θ z ||,

[0126] where P0 and P1 represent the blade profile power and the induced power in the hovering state respectively, U tips represents the tip speed of the rotor blade, v0 represents the average rotor induced speed, d0 is the fuselage drag ratio, ρ0 is the air density, s0 is the rotor solidity, A0 is the rotor disk area; θ x,y and θ z represent the horizontal speed and the vertical speed of the UAV respectively. It is assumed that all UAVs fly at the same speed. W is the mass of the UAV, and g is the acceleration due to gravity. is the flight time of the cluster-head UAV between two adjacent hovering points, expressed as:

[0127]

[0128] where θ represents the flight speed of the UAV.

[0129] In addition, the energy consumption E s of the cluster-head UAV is expressed as:

[0130]

[0131] Among which, P hov represents the hovering power of the cluster head UAV, and T s represents the hovering time of the cluster head UAV.

[0132] Step 3-3: According to the ground-to-air channel model in Step 2, the transmission energy consumption of the ground users is expressed as

[0133]

[0134] Step 4: Based on the user transmission delay and the energy consumption of the UAV cluster and users in Step 3, model the UAV cluster data collection problem as a multi-objective optimization problem;

[0135] Step 4 specifically includes:

[0136] Step 4-1: According to the delay and energy consumption models in Step 3, the three optimization objectives to be jointly optimized are expressed as:

[0137]

[0138] Among which A = {q s , M s , Φ, P, γ} is the set of optimization variables, is the deployment location of the UAV cluster, is the number of cluster tail UAVs assigned to each UAV cluster, is the flight trajectory of the cluster tail UAVs, is the power control variable, including the transmission powers of the ground users and the cluster tail UAVs, is the connection relationship variable between the ground users and the cluster tail UAVs. In addition, the objective f1(A) is the total energy consumption (TEU) of the UAV cluster, f2(A) is the average energy consumption (AEG) of the ground users, and f3(A) is the average transmission delay (ADG) of the ground users.

[0139] Step 4-2: Jointly optimize the dynamic deployment, trajectory planning and power of the UAV cluster. The overall objective of USDC-MOP is to simultaneously minimize the three objectives TEU, AEG and ADG, which is expressed as:

[0140]

[0141] Among which, the constraint C1 specifies the maximum number M max of cluster tail UAVs assigned to the UAV cluster, and the constraint C2 stipulates the total number M of cluster tail UAVs. The constraints C3 and C4 respectively specify the minimum channel transmission rates of ground-to-air and air-to-air and Constraint C5 stipulates that each ground user can only make one connection with a cluster - head UAV at one hovering point, while C6 restricts the cluster - head UAV to establish connection relationships with at most U max ground users simultaneously at one hovering point. Constraint C7 stipulates the maximum transmission delay of users Constraint C8 standardizes the value range of the binary connection variable Constraint C9 standardizes the deployment range of the UAV cluster. Constraint C10 standardizes the set of all access sequences of the cluster - head UAV Constraints C11 and C12 respectively stipulate the transmission power ranges of users and cluster - head UAVs.

[0142] Step 5: Based on the multi - objective optimization problem proposed in Step 4, use the K - means - based clustering method to cluster users, and use the method of constructing a Voronoi diagram to divide the region, and determine the scale and deployment location of the UAV cluster;

[0143] Step 5 specifically includes:

[0144] Step 5 - 1: Use the K - means algorithm to cluster ground users, divide U ground users into M classes, and obtain M cluster centers denoted as:

[0145] Step 5 - 2: Use the above - mentioned cluster centers as growth points to construct a Voronoi diagram Divide the entire region to obtain each sub - region denoted as: Meanwhile, obtain the intersection set of the Voronoi diagram: Ω = {ω1, ω2, …, ω ζ}. On the basis of meeting the deployment requirements, obtain the optimal UAV cluster deployment plan, that is, select the deployment location set q of the UAV cluster from Ω s and the number M of cluster - head UAVs allocated in the cluster s .

[0146] Step 6: Based on the UAV cluster deployment situation in Step 5, establish the connection relationship between ground users and cluster - head UAVs by constructing Fermat points, and simplify the initial multi - objective optimization problem;

[0147] Step 6 specifically includes:

[0148] Step 6 - 1: Construct Fermat points based on the user distribution in each sub - region and establish the user - cluster - head UAV connection relationship γ.

[0149] Step 6 - 2: Based on the above algorithm, the initial multi - objective problem P0 can be simplified to:

[0150]

[0151] Where:

[0152]

[0153] Among them, B = {Φ, P} is the set of optimization variables, and the objective functions f'1(B), f'2(B), and f'3(B) are all simplified optimization objectives; since the connection relationship between the user and the cluster head UAV has been determined, therefore and are the parameters determined by user u.

[0154] Step 7 proposes an improved non-dominated sorting whale optimization algorithm to solve the simplified multi-objective optimization problem in Step 6. By optimizing the access order of UAVs and the transmission power between UAVs and users, a Pareto front solution set is obtained to achieve efficient data collection of the UAV cluster.

[0155] Step 7 specifically includes:

[0156] Step 7-1: Initialize relevant parameters, including the whale population size J max , the maximum number of iterations I′ max , the number of objectives K, and relevant control parameters.

[0157] Step 7-2: Calculate the fitness of each whale individual in the population. For each individual in the current population, calculate its fitness value according to the objective function.

[0158] Step 7-3: Compare all individuals in the population, find all non-dominated individuals in the current population, and determine the non-dominated levels; for individuals in the same non-dominated level, calculate their crowding distances. Perform a selection operation based on the results of non-dominated sorting (non-dominated levels and crowding distances), giving priority to individuals with a lower non-dominated level; if multiple individuals are in the same non-dominated level, select the individual with a larger crowding distance. Through such selection, a certain number (usually determined according to factors such as the population size) of relatively excellent individuals are selected to form a new population for subsequent iterative operations.

[0159] Step 7-4: Update the whale positions. First, randomly select a whale individual from the new population, and determine its update method according to a certain probability, which is usually related to factors such as the current iteration number. Here are two main update methods:

[0160] Update based on the global best individual (encircling prey stage): If the randomly generated probability is less than a certain set value (such as 0.5), select the global best individual in the current population (determined comprehensively according to fitness and non-dominated sorting), and update the position of the currently selected whale individual according to the mathematical model of encircling prey in the whale optimization algorithm, making it approach the global best individual in order to expect to find a better solution.

[0161] Update based on a random individual (prey search phase): If the randomly generated probability is greater than or equal to the set value, randomly select another individual in the population and update the position of the current whale individual according to the mathematical model of prey search, increasing the search range and exploration ability of the population.

[0162] After updating the position of the whale individual, check whether its position exceeds the pre-set search space range. If it does, perform boundary processing. Common boundary processing methods include setting the value exceeding the boundary to the boundary value or making it return to the reasonable search space through mapping or other means.

[0163] Step 7 - 5: Check whether the current iteration number has reached the pre-set maximum iteration number. If it has, the algorithm ends and outputs the non-dominated solutions obtained in the current population (i.e., a set of solutions that are relatively optimal and balance each other on multiple objectives). If it has not, return to the step of "calculating the fitness of the population individuals" and continue the next round of iteration. Then, output the Pareto solution set and the optimization variable B = {Φ, P}.

[0164] Figure 4 The schematic diagram of the deployment and trajectory planning of the UAV swarm under the compromise solution in 60 user scenarios is given. The compromise solution refers to one solution selected from the Pareto solution set after normalizing 3 optimization objective functions. As can be seen from the figure, 60 ground users are respectively distributed in 8 sub-regions after division, and 3 UAV swarms are deployed at the junctions of different sub-regions. The cluster-tail UAVs of each UAV swarm start from the cluster deployment positions and go to the sub-regions they serve respectively to provide data collection services for ground users, that is, 8 cluster-tail UAVs serve 8 sub-regions respectively.

[0165] Figure 5 The relationship diagram between the total energy consumption of the UAV swarm and the number of ground users U under the minimum TEU solution is given under different algorithms, namely the improved non-dominated sorting whale optimization algorithm (INS-WOA), multi-objective artificial bee colony algorithm (MOAHA), multi-objective grey wolf optimization algorithm (MOGWO), and non-dominated sorting genetic algorithm (NSGA-II). First, the total energy consumption of the UAV swarm increases with the increase in the number of ground users. This is because, as shown in the line graph, with the increase in the number of ground users, the number of hovering points of the cluster-tail UAVs increases, which makes the entire flight path and task completion time increase, resulting in an increase in energy consumption. Secondly, by comparison, it is found that in scenarios with different numbers of ground users, the INS-WOA algorithm proposed in this paper can achieve the minimum total energy consumption of the UAV swarm.

[0166] Figure 6It is a graph showing the relationship between the average user energy consumption and the number of ground users U under the minimized AEG solution for four different algorithms. As the number of ground users increases, the average user energy consumption in the proposed INS-WOA stabilizes at around 0.02 J, which is 30% lower than that of the MOGWO and NSGA-II methods, demonstrating the obvious advantage of INS-WOA in user power control and ensuring lower user energy consumption.

[0167] Figure 7 It presents a graph showing the relationship between the average user transmission delay and the number of ground users U under the minimized ADG solution for four different algorithms. It can be found that the INS-WOA algorithm has an obvious advantage in terms of average transmission delay, ensuring the timely collection of time-sensitive user data.

[0168] Figure 8 It gives a comparison graph of the algorithm time complexity for different optimization methods with different numbers of ground users. As can be seen from the graph, the INS-WOA algorithm reduces the time complexity by 50% compared to the MOAHA, MOGWO, and NSGA-II algorithms. In addition, as the number of ground users U increases, the algorithm time complexity almost shows a linear growth, which provides the possibility for applications in large-scale scenarios.

[0169] Figure 9 It shows the transmission power distribution of 60 ground users under different objective solutions. For the sake of comparison, we standardized and normalized the Pareto solution set and selected a compromise solution among the three objectives for data analysis. For the compromise solution, the transmission power of most GUs is controlled within a relatively low range of [0 W, 0.5 W]. In addition, for the TEU solution, the distribution of transmission power is relatively dispersed. However, the power distributions under the AEG and ADG solutions show completely opposite trends because the user energy consumption increases with the increase of transmission power, while the greater the transmission power, the faster the data transmission speed and the smaller the average user delay.

[0170] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

[0171] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A multi-objective optimization method for dynamic trajectory planning and power control of an unmanned aerial vehicle cluster, characterized in that, It includes the following steps: Step 1: Propose the 6G air access network data collection scenario, design a hierarchical UAV cluster architecture model, and establish a deployment and trajectory planning model for the UAV cluster; Step 2: Based on the UAV cluster in Step 1, considering providing data collection services for randomly distributed ground users, establish a data collection model for user-cluster-tail UAV-cluster-head UAV, and design ground-to-air and air-to-air channel models respectively; Step 3: Based on the data collection model obtained in Step 2, define the user transmission delay to evaluate the data collection performance of the UAV cluster, and considering the energy consumption of the UAV cluster and ground users, propose energy consumption models for the UAV cluster and ground users respectively; Step 4: Based on the user transmission delay, energy consumption of the UAV cluster and ground users in Step 3, model the UAV cluster data collection problem as a multi-objective optimization problem; Step 5: Based on the multi-objective optimization problem proposed in Step 4, use the K-means based clustering method to cluster users, divide the area by constructing a Voronoi diagram, and determine the scale and deployment location of the UAV cluster; Step 6: Based on the UAV cluster deployment in Step 5, establish the connection relationship between the ground user and the cluster-tail UAV by constructing the Fermat point method, and simplify the initial multi-objective optimization problem; Step 7: Propose an improved non-dominated sorting whale optimization algorithm to solve the simplified multi-objective optimization problem in Step 6. By optimizing the access order of UAVs and the transmission power between UAVs and users, obtain the Pareto front solution set to achieve efficient data collection of the UAV cluster.

2. A multi-objective optimization UAV swarm dynamic trajectory planning and power control method according to claim 1, characterized in that, The said Step 1 includes: Step 1-1: Propose a 6G air interface network data collection scenario, including defining a set of randomly distributed ground users as and a hierarchical UAV cluster defined as where the UAV cluster acts as the cluster head UAV of the aerial base station and M s cluster tail UAVs responsible for data collection and relay are composed, where U is the number of ground users, S is the number of UAV cluster layers, h S is the number of cluster head UAVs, is the number of cluster tail UAVs in the s-th layer Step 1-2: Model the data collection scenario according to the three-dimensional Cartesian coordinate system as: where Θ represents the spatial range of the data collection scenario, (x, y, z) represents the three-dimensional coordinates of any point in space, X min , Y min and Z min respectively specify the lower limits of the ranges of this point on the three coordinate axes, X max , Y max and Z max then specify the upper limits. Therefore, the three-dimensional coordinates of the ground user u, the cluster head UAV h s and the cluster tail UAV are respectively represented as q u =(x u , y u , z u ), q s =(x s , y s , z s ) and Therefore, the distance d between the user u and the cluster tail UAV u,m is expressed as: Similarly, the distance between the cluster-tail UAV and the cluster-head UAV h s is denoted as the distance d m,s which is expressed as: Step 1-3: For the UAV cluster where the cluster head UAV h s at the deployment point q s maintains a hovering state to be responsible for receiving the relay data of the cluster tail UAVs, while the cluster tail UAVs need to fly to different hovering points to provide data collection services for ground users. Therefore, the trajectory of the cluster tail UAVs is modeled as the access order of different hovering points: Among them, represents the initial position of the cluster-tail UAV , and represents the total number of hovering points of the cluster-tail UAV.

3. A multi-objective optimization method for dynamic trajectory planning and power control of unmanned aerial vehicle clusters according to claim 2, characterized in that, The said Step 2 includes: Step 2-1: Consider providing data collection services for randomly distributed ground users. The ground-to-air channel model established between the ground user u and the cluster head UAV is as follows: as follows: where h u,m is the channel gain of the communication link, is the small-scale fading channel gain, is the free-space fading channel gain; is the transmission rate between user u and the cluster head UAV and B u,m is the channel bandwidth between user u and the cluster head UAV and p u is the transmit power of user u, and σ 2 is the average noise power spectral density, Step 2-2: After the cluster-tail UAV completes the data collection task of the ground user, forward it to the cluster-head UAV. The air-to-air channel model is modeled as: Among them, is the transmission rate between the cluster-tail UAV and the cluster-head UAV h s B m,s is the channel bandwidth between the cluster-tail UAV and the cluster-head UAV h s and is the transmit power of the cluster-tail UAV, η LoS is the average path loss of the line-of-sight link.

4. A multi-objective optimization method for dynamic trajectory planning and power control of an unmanned aerial vehicle cluster according to claim 3, characterized in that The said Step 3 includes: Step 3-1: User transmission delay T of user u u is defined as: Among them, is a binary variable used to represent the connection relationship between user u and the cluster head UAV , T u,m and T m,s respectively represent the transmission delays of the ground-to-air channel and the air-to-air channel, represents the hovering point of the nth cluster head UAV; Step 3-2: The energy consumption of the UAV cluster consists of two parts, the communication energy consumption of the UAVs and the movement energy consumption. The energy consumption E of the cluster head UAV m is expressed as: Among them is the transmission energy consumption of the cluster head UAV, and and respectively represent the hovering energy consumption and flight energy consumption of the cluster head UAV. The energy consumption E s of the cluster head UAV is expressed as: Among which P hov represents the hovering power of the cluster head UAV, and T s represents the hovering time of the cluster head UAV. Step 3-3: According to the ground-to-air channel model in Step 2, the transmission energy consumption of the ground user is expressed as 5. A multi-objective optimization method for dynamic trajectory planning and power control of unmanned aerial vehicle clusters according to claim 4, characterized in that The said Step 4 includes: Step 4-1: According to the user transmission delay in Step 3 and the transmission energy consumption model of the ground user, the three optimization objectives that need to be jointly optimized are expressed as: where A = {q s , M s , Φ, P, γ} is the set of optimization variables, is the deployment location of the UAV cluster, is the flight trajectory of the cluster head UAV, is the power control variable, is the transmission power of the cluster head UAV, is the connection relationship variable between the ground user and the cluster head UAV. In addition, the objective f1(A) is the total energy consumption TEU of the UAV cluster, f2(A) is the average energy consumption AEG of the ground user, and f3(A) is the average transmission delay ADG of the ground user. Step 4-2: Jointly optimize the dynamic deployment, trajectory planning and power of the UAV cluster. The overall objective of USDC-MOP is to simultaneously minimize the three objectives TEU, AEG and ADG, which is expressed as: Among them, the constraint C1 standardizes the maximum number M of cluster head UAVs allocated to the UAV cluster max , the constraint C2 stipulates the total number M of cluster head UAVs, and the constraints C3 and C4 respectively standardize the minimum ground-to-air and air-to-air channel transmission rates and The constraint C5 stipulates that each ground user can only make one connection with one cluster head UAV at one hovering point. The constraint C6 restricts the maximum number of connections that a cluster head UAV can establish with U max ground users at one hovering point. The constraint C7 stipulates the maximum transmission delay of the user The constraint C8 standardizes the value range of, the constraint C9 standardizes the deployment range of the UAV cluster, and the constraint C10 standardizes all access sequence sets of the cluster head UAVs The constraints C11 and C12 respectively stipulate the transmission power ranges of the user and the cluster head UAV. Among them, is the minimum transmission power of user u, is the maximum transmission power of user u, is the minimum transmission power of the cluster head UAV, the maximum transmission power of the cluster head UAV.

6. A multi-objective optimization method for dynamic trajectory planning and power control of an unmanned aerial vehicle cluster according to claim 5, characterized in that The said Step 5 includes: Step 5-1: Use the K-means algorithm to cluster the ground users, divide the U ground users into M classes, and obtain M cluster centers, which are expressed as: Step 5-2: Construct a Voronoi diagram using the above clustering centers as growth points Divide the entire region to obtain each sub-region, which is expressed as: At the same time, obtain the intersection set of the Voronoi diagram: On the basis of meeting the deployment requirements, obtain the optimal UAV cluster deployment plan, that is, select the set q of the deployment locations of the UAV cluster from Ω s and the number M of cluster head UAVs allocated in the cluster s .

7. A multi-objective optimization UAV swarm dynamic trajectory planning and power control method according to claim 1, characterized in that, The said Step 6 includes: Step 6-1: Construct the Fermat point based on the user distribution within each sub-region and establish the user-cluster head UAV connection relationship γ Step 6-2: Based on the above algorithm, the initial multi-objective problem P0 can be simplified to: Where: Among them, B = {Φ, P} is the set of optimization variables, and the objective functions f'1(B), f'2(B), and f'3(B) are all simplified optimization objectives; since the connection relationship between the user and the cluster head UAV has been determined, therefore and are parameters determined by user U, is the ground-to-air channel transmission delay after the parameters determined by user U, is the air-to-air channel transmission delay after the parameters determined by user u.

8. A multi-objective optimization method for dynamic trajectory planning and power control of an unmanned aerial vehicle cluster according to claim 7, characterized in that The said Step 7 includes: Step 7-1: Initialize relevant parameters, including the whale population size J max , the maximum number of iterations I' max and the number of objectives K. Step 7-2: Calculate the fitness of the whale individuals in the population. For each individual in the current population, calculate its fitness value according to the objective function, Step 7-3: Compare all individuals in the population, find all non-dominated individuals in the current population, and determine the non-dominated levels; for individuals within the same non-dominated level, calculate their crowding distances, and perform selection operations based on the non-dominated levels and crowding distances. Give priority to selecting individuals with lower non-dominated levels; if multiple individuals are at the same non-dominated level, select the individual with a larger crowding distance. Through such selection, select relatively excellent individuals to form a new population for subsequent iterative operations. Step 7-4: Update the whale positions. First, randomly select a whale individual from the new population and determine its update method. The update methods are as follows: Update based on the globally optimal individual: If the randomly generated probability is less than the set value, select the globally optimal individual in the current population, and update the position of the currently selected whale individual according to the mathematical model of hunting prey in the whale optimization algorithm, making it approach the globally optimal individual in the hope of finding a better solution. Update based on a random individual: If the randomly generated probability is greater than or equal to the set value, then randomly select another individual in the population and update the position of the current whale individual according to the mathematical model of searching for prey, increasing the search range and exploration ability of the population. After updating the positions of the whale individuals, check whether their positions exceed the pre-set search space range. If they do, perform boundary processing, set the values exceeding the boundary to the boundary values or make them return to the reasonable search space through mapping or other means. Step 7-5: Check whether the current iteration number has reached the pre-set maximum iteration number. If it has, the algorithm ends and outputs the non-dominated solutions obtained in the current population; if it has not, return to Step 7-2 to continue the next round of iteration. Then, output the Pareto solution set and the optimization variable B = {Φ, P}.

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