An unmanned aerial vehicle maritime communication flight path planning method, device and system

Abstract

The application discloses a kind of unmanned aerial vehicle maritime communication flight path planning method, device and system, belong to path planning technical field, method includes: grouping to the user scattered on sea surface, and form Delaunay triangle by grouped user;Determine the hovering point of unmanned aerial vehicle in each described Delaunay triangle;According to each described hovering point, the flight path of unmanned aerial vehicle is planned, the order that unmanned aerial vehicle visits each described hovering point is determined, and the shortest path is acquired;Based on the shortest path, the energy consumption of unmanned aerial vehicle in acceleration, deceleration and uniform speed stage is optimized, and the optimal path of unmanned aerial vehicle maritime communication is acquired.The method can acquire the shortest path and optimize the energy consumption of unmanned aerial vehicle in acceleration, deceleration and uniform speed stage, acquire the optimal path of unmanned aerial vehicle maritime communication.

Description

Technical Field

[0001] This invention relates to a method, apparatus, and system for planning flight paths for unmanned aerial vehicles (UAVs) in maritime communication, belonging to the field of path planning technology. Background Technology

[0002] To address the challenges of dispersed users at sea and limited drone communication coverage, optimizing drone paths is crucial for enhancing communication between drones and users at sea.

[0003] Researchers proposed several optimization schemes, including joint optimization of flight paths, node wake-up scheduling, and association, to minimize the maximum task completion time for all UAVs. However, minimizing task completion time does not necessarily minimize energy consumption. To optimize power, path, and user service quality, researchers proposed a cyclic path algorithm, designing circular and figure-eight cyclic paths and comparing the impact of wind speed on energy consumption under both paths. Researchers used deep learning algorithms to dynamically adjust UAV paths in real time to adapt to the real-time communication requirements of UAVs flying at sea. However, these methods struggle to quickly acquire UAV paths, and existing studies typically assume UAVs fly at a constant speed, neglecting energy consumption during acceleration and deceleration phases.

[0004] Therefore, there is an urgent need for a high-efficiency flight path planning method for reliable communication of UAVs in maritime communications. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus and system for planning flight paths for UAV maritime communication, which can obtain the shortest path and optimize the energy consumption of UAV during acceleration, deceleration and constant speed phases to obtain the optimal path for UAV maritime communication.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for planning flight paths for unmanned aerial vehicle (UAV) maritime communication, comprising:

[0008] Users scattered across the sea surface are grouped, and the grouped users form a Delaunay triangle.

[0009] Determine the hovering point of the UAV in each of the aforementioned Delaunay triangles;

[0010] Based on each hovering point, the flight path of the UAV is planned, the order in which the UAV visits each hovering point is determined, and the shortest path is obtained.

[0011] Based on the shortest path, the energy consumption of the UAV during acceleration, deceleration, and constant speed phases is optimized to obtain the optimal path for UAV maritime communication.

[0012] Building upon the first aspect, further, the users scattered across the sea are grouped, forming a Delaunay triangle comprised of:

[0013] Clustering algorithms are used to group users scattered on the sea surface, and Delaunay triangles are formed with the positions of each user in each group as vertices.

[0014] In conjunction with the first aspect, further determining the hovering points of the UAV within each of the aforementioned Delaunay triangles includes:

[0015] Find the Fermat points of each of the aforementioned Delaunay triangles;

[0016] If the distance from the Fermat point of the Delaunay triangle to its vertex is within the service range of the drone, then the Fermat point of the Delaunay triangle is used as the hovering point of the drone.

[0017] If the distance from the Fermat point of the Delaunay triangle to its vertex exceeds the service range of the drone, then the second-best point of the Delaunay triangle is found as the hovering point of the drone.

[0018] In the Delaunay triangle formed by the positions of users i, j, and k, the formula for finding the secondary advantage is:

[0019]

[0020]

[0021] Where, q m Let f(x) be the horizontal position of the m-th hovering point (i.e., the second best point being sought). m ,y m x is the sum of the distances from the m-th hovering point (i.e., the second-best point being sought) to the i, j, and k-th users. m y m Let a be the coordinates of the m-th hovering point (i.e., the second-best point being sought). i b i Let a be the coordinates of the i-th user. j b j Let a be the coordinates of the j-th user. k b k Let be the coordinates of the k-th user.

[0022] In conjunction with the first aspect, further, based on the aforementioned shortest path, the energy consumption of the UAV during acceleration, deceleration, and constant speed phases is optimized to obtain the optimal path for UAV maritime communication, including:

[0023] Based on the shortest path, an objective function is constructed to optimize the energy consumption of the UAV during acceleration, deceleration, and constant speed phases.

[0024] By introducing slack variables to process the non-convex part of the objective function, the objective function is transformed from a non-convex function into a convex function.

[0025] Solve the objective function, which is transformed into a convex function, to obtain the optimal path for UAV maritime communication.

[0026] Building upon the first aspect, the objective function for optimizing the energy consumption of the UAV during acceleration, deceleration, and constant speed phases is further defined as follows:

[0027]

[0028]

[0029] Where E is the total energy consumption of the drone, E F For the flight energy consumption of drones, E H Let λ be the hovering energy consumption of the drone, Q be the set of horizontal positions of the hovering points, γ be the received signal-to-noise ratio of the drone, and λ be the [missing value]. v Let λ be the change in the speed of the UAV within each time slot. t T is the length of the time slot. U v is the time required for the drone to maintain a constant speed phase. n Let v be the velocity of the UAV in the nth time slot. N Let L be the velocity of the UAV in the Nth time slot. m V is the flight distance between the m-th hovering point and the (m-1)-th hovering point. max a is the maximum speed of the drone. max λ is the maximum acceleration of the drone. max γ0 is the maximum length of the time slot, γ0 is the signal-to-noise ratio threshold of the UAV, and N is the total number of time slots.

[0030] In conjunction with the first aspect, the slack variable is further defined as follows:

[0031]

[0032] in, Let v0 be the forward average rotor-induced velocity of the UAV, and v be the relaxation variable. n Let be the speed of the drone in the nth time slot.

[0033] Combining the first aspect, further, the objective function transformed into a convex function is:

[0034]

[0035]

[0036] Among them, v n Let v be the velocity of the UAV in the nth time slot. N Let P be the velocity of the UAV in the Nth time slot. Fappro (v n Human-machine interaction at speed v n The power P Fappro (v N For the drone at speed v N The power P HU D is the hovering power of the drone. m Let R be the amount of data received by the drone from the user at the m-th hovering point. m,i Let λ be the communication rate between the UAV at the m-th hovering point and the i-th user. v Let λ be the change in the speed of the UAV within each time slot. t λ is the length of the time slot. w Let T be the change in the velocity of the UAV within the time slot where slack variables are introduced. U L is the time required for the drone to maintain a constant speed phase. m V is the flight distance between the m-th hovering point and the (m-1)-th hovering point. max a is the maximum speed of the drone. max λ is the maximum acceleration of the drone. max γ is the maximum length of the time slot, γ is the received signal-to-noise ratio of the UAV, and γ0 is the received signal-to-noise ratio threshold of the UAV. As slack variables, Let N be the lower bound function, N be the total number of time slots, and M be the total number of hover points.

[0037] Secondly, the present invention provides a drone maritime communication flight path planning device, comprising:

[0038] Grouping module: Used to group users scattered on the sea surface, forming a Delaunay triangle from the grouped users;

[0039] Point finding module: used to find the Fermat points of each of the Delaunay triangles as the hovering points of the UAV;

[0040] Planning module: Used to plan the flight path of the UAV based on each hovering point, determine the order in which the UAV visits each hovering point, and obtain the shortest path;

[0041] Optimization module: Used to optimize the energy consumption of the UAV during acceleration, deceleration and constant speed phases based on the shortest path, and obtain the optimal path for UAV maritime communication.

[0042] Thirdly, the present invention provides a UAV maritime communication flight path planning system, including a processor and a storage medium;

[0043] The storage medium is used to store instructions;

[0044] The processor is configured to operate according to the instructions to perform the steps of the method according to any one of the first aspects.

[0045] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] The UAV maritime communication flight path planning method provided by this invention determines the hovering points of the UAV within a Delaunay triangle formed by groups of dispersed users on the sea surface, and then plans the UAV's flight path, determining the order in which the UAV visits each hovering point to obtain the shortest path. Based on the shortest path, the energy consumption of the UAV during acceleration, deceleration, and constant speed phases is optimized to obtain the optimal path for UAV maritime communication. Unlike traditional methods where the UAV flies directly above the user to communicate, the UAV can simultaneously serve surrounding users while hovering at the hovering point, shortening the UAV's flight distance, improving communication efficiency, and reducing energy consumption. When the distance from the Fermat point of the Delaunay triangle to its vertex exceeds the UAV's service range, the second-best point of the Delaunay triangle is found as the UAV's hovering point. This ensures both a short distance from the UAV to the user and that the UAV can simultaneously meet the communication needs of multiple users at the hovering point, effectively improving communication quality and user experience. Compared to traditional methods that do not consider energy consumption during acceleration and deceleration, this invention optimizes the energy consumption of UAVs during acceleration, deceleration, and constant speed phases. It introduces slack variables to handle the non-convex part of the objective function, transforming the objective function from a non-convex function to a convex function. Solving the transformed convex objective function yields the optimal path for UAV maritime communication, which can further improve the performance of UAVs. Attached Figure Description

[0048] Figure 1 This is a flowchart of the UAV maritime communication flight path planning method provided in an embodiment of the present invention. Detailed Implementation

[0049] The technical solution of this application will be further described in detail below with reference to specific embodiments.

[0050] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. Unless otherwise specified, the embodiments of this application and the technical features within them can be combined with each other.

[0051] Example 1:

[0052] Figure 1 This is a flowchart illustrating a UAV maritime communication flight path planning method provided in this embodiment. This flowchart only shows the logical sequence of the method in this embodiment; however, it can be implemented in different ways without conflict. Figure 1 Complete the steps shown or described in the order indicated.

[0053] The UAV maritime communication flight path planning method provided in this embodiment can be applied to a terminal and can be executed by a UAV maritime communication flight path planning device. This device can be implemented by software and / or hardware and can be integrated into the terminal, such as any tablet computer or computer device with communication function.

[0054] See Figure 1 The UAV maritime communication flight path planning method in this embodiment specifically includes the following steps:

[0055] Step 1: Divide the users scattered on the sea surface into groups, forming a Delaunay triangle from the grouped users;

[0056] In this embodiment, grouping users scattered on the sea surface and forming a Delaunay triangle from the grouped users specifically includes: using a clustering algorithm to group users scattered on the sea surface, and forming a Delaunay triangle with the position of each user in each group as the vertex.

[0057] Step 2: Determine the hovering point of the drone within each Delaunay triangle;

[0058] In this embodiment, determining the hovering point of the UAV in each Delaunay triangle specifically includes the following steps:

[0059] Step 1: Locate the Fermat point for each Delaunay triangle;

[0060] Step 2: If the distance from the Fermat point of the Delaunay triangle to its vertex is within the service range of the drone, then the Fermat point of the Delaunay triangle is used as the hovering point of the drone.

[0061] Step 3: If the distance from Fermat's point to the vertex of the Delaunay triangle exceeds the service range of the drone, then find the second-best point of the Delaunay triangle as the hovering point of the drone.

[0062] In this embodiment, the formula for finding the secondary advantage in the Delaunay triangle formed by the positions of the i, j, and k users is as follows:

[0063]

[0064]

[0065] Where, q m Let f(x) be the horizontal position of the m-th hovering point (i.e., the second best point being sought). m ,y m x is the sum of the distances from the m-th hovering point (i.e., the second-best point being sought) to the i, j, and k-th users. m y m Let a be the coordinates of the m-th hovering point (i.e., the second-best point being sought). i b i Let a be the coordinates of the i-th user. j b j Let a be the coordinates of the j-th user. k b k Let be the coordinates of the k-th user.

[0066] Step 3: Based on each hovering point, plan the drone's flight path, determine the order in which the drone visits each hovering point, and obtain the shortest path;

[0067] In this embodiment, the shortest path problem is treated as the Traveling Salesman Problem (TSP) and a corresponding algorithm is used to obtain the shortest path by determining the order in which the drone visits each hovering point.

[0068] Step 4: Based on the shortest path, optimize the energy consumption of the UAV during acceleration, deceleration and constant speed phases to obtain the optimal path for UAV maritime communication.

[0069] Existing UAV flight studies typically assume that the UAV flies at a constant speed, without considering the energy consumed during acceleration and deceleration. Therefore, after determining the optimal hovering point, the aim is to optimize energy consumption during the three phases of UAV flight: acceleration, deceleration, and constant speed. However, this problem is non-convex. To address this, this embodiment employs a progressively convex approximation (SCA) technique to minimize the UAV's energy consumption, ensuring optimal energy utilization efficiency during flight and further improving the UAV's performance.

[0070] In this embodiment, the energy consumption of the UAV during acceleration, deceleration, and constant speed phases is optimized based on the shortest path to obtain the optimal path for UAV maritime communication. This specifically includes the following steps:

[0071] Step 1: Based on the shortest path, construct an objective function to optimize the energy consumption of the UAV during acceleration, deceleration, and constant speed phases;

[0072] In this embodiment, the objective function for optimizing the energy consumption of the UAV during acceleration, deceleration, and constant speed phases is:

[0073]

[0074]

[0075] Where E is the total energy consumption of the drone, E F For the flight energy consumption of drones, E H Let λ be the hovering energy consumption of the drone, Q be the set of horizontal positions of the hovering points, γ be the received signal-to-noise ratio of the drone, and λ be the [missing value]. v Let λ be the change in the speed of the UAV within each time slot. t T is the length of the time slot. U v is the time required for the drone to maintain a constant speed phase. n Let v be the velocity of the UAV in the nth time slot. N Let L be the velocity of the UAV in the Nth time slot. m V is the flight distance between the m-th hovering point and the (m-1)-th hovering point. max a is the maximum speed of the drone. max λ is the maximum acceleration of the drone. max γ0 is the maximum length of the time slot, γ0 is the signal-to-noise ratio threshold of the UAV, and N is the total number of time slots.

[0076] Step 2: Introduce slack variables to handle the non-convex part of the objective function, transforming the objective function from a non-convex function to a convex function;

[0077] In this embodiment, the slack variable is:

[0078]

[0079] in, Let v0 be the forward average rotor-induced velocity of the UAV, and v be the relaxation variable. n Let be the speed of the drone in the nth time slot.

[0080] The objective function for converting to a convex function is:

[0081]

[0082]

[0083] Among them, v n Let v be the velocity of the UAV in the nth time slot. N Let P be the velocity of the UAV in the Nth time slot. Fappro (v n Human-machine interaction at speed v n The power P Fappro (v N For the drone at speed v N The power P HU D is the hovering power of the drone. m Let R be the amount of data received by the drone from the user at the m-th hovering point. m,i Let λ be the communication rate between the UAV at the m-th hovering point and the i-th user. v Let λ be the change in the speed of the UAV within each time slot. t λ is the length of the time slot. w Let T be the change in the velocity of the UAV within the time slot where slack variables are introduced. U L is the time required for the drone to maintain a constant speed phase. m V is the flight distance between the m-th hovering point and the (m-1)-th hovering point. max a is the maximum speed of the drone. max λ is the maximum acceleration of the drone. max γ is the maximum length of the time slot, γ is the received signal-to-noise ratio of the UAV, and γ0 is the received signal-to-noise ratio threshold of the UAV. As slack variables, Let N be the lower bound function, N be the total number of time slots, and M be the total number of hover points.

[0084] Step 3: Solve the objective function, which has been converted into a convex function, to obtain the optimal path for UAV maritime communication.

[0085] Example 2:

[0086] In this embodiment, a communication system between a maritime drone and users on a ship is considered. The drone collects information from the users and transmits it back to a land-based base station. Users are randomly assigned at sea; assuming there are I users, Represents a set of users, defined as The horizontal position of the i-th user is u i =[a i ,b i Assume the drone only provides service to users while hovering, and there are M hovering points. The set representing hover points is defined as follows: The horizontal position of the m-th hovering point is q. m =[x m ,y mLet Q be the set of horizontal positions of the hovering points. Assume the drone flies at a constant altitude of H meters. The distance between the drone and the i-th user at the m-th hovering point is:

[0087]

[0088] The drone goes through three phases as it flies from one hovering point to another. In the first phase, the speed accelerates from 0 m / s to a suitable speed. In the second phase, it maintains a constant speed. In the third phase, the drone begins to decelerate, slowing down to 0 m / s upon reaching the next hovering point.

[0089] Since there are no obvious obstacles on the sea surface, and the drone flies at a relatively high altitude, it is assumed that the wireless channel between the drone and the user is primarily a line-of-sight (LoS) link. The channel power from the drone to the i-th user at the m-th hovering point follows a free-space path loss model, and the channel power from the drone to the i-th user at the m-th hovering point is:

[0090]

[0091] Among them, h m,i Let β be the channel power from the UAV to the i-th user at the m-th hovering point, and β0 be the channel power gain when the reference distance is 1 meter.

[0092] Each user sends D bits of data to the drone. The communication rate between the drone at the m-th hovering point and the i-th user is:

[0093] R m,i = B log2(1+γ)

[0094] Among them, R m,i Let be the communication rate between the UAV at the m-th hovering point and the i-th user, B be the channel bandwidth, and γ be the UAV's received signal-to-noise ratio. Among them, P US For each user, the transmit power is given, and N0 is the noise power spectral density.

[0095] To ensure Quality of Service (QoS), QoS thresholds are defined to guarantee system performance. Assuming the received signal-to-noise ratio threshold for the UAV is γ0, then:

[0096] γ≥γ0

[0097] In this embodiment, we consider a UAV communicating with one user in the same time slot, using Time Division Multiple Access (TDMA). A binary variable is defined to represent the association between the UAV at the m-th hovering point and the i-th user. When φ i,m When φ = 1, it indicates that the UAV communicates with the i-th user at the m-th hovering point.i,m When = 0, it means that the drone did not communicate with the i-th user at the m-th hovering point. Therefore, we get:

[0098]

[0099] Therefore, the communication rate between the drone and the user can be rewritten as:

[0100]

[0101] The propulsion energy model for the UAV is as follows:

[0102]

[0103] Where v is the speed of the UAV, v0 is the forward average rotor induced velocity of the UAV, and P UAV (v) represents the propulsion energy of the UAV at velocity v, where P0 and P1 are constants. The blade shape power and induced power of the drone while it is hovering. denoted as ρ, where d is the rotor blade tip velocity of the UAV, d0 is the fuselage drag ratio of the UAV, A is the rotor disk area of ​​the UAV, ρ is the air density, and s is the rotor robustness of the UAV.

[0104] Based on the UAV's propulsion energy model, setting v = 0, the UAV's hovering power is obtained as follows:

[0105] P HU =P0+P1

[0106] Among them, P HU This refers to the hovering power of the drone.

[0107] The total hovering energy consumption for the drone to communicate with surrounding users at the hovering point is:

[0108]

[0109] Among them, E H Let T be the total hovering energy consumption of the UAV communicating with surrounding users at the m-th hovering point. m The time it takes for the drone to communicate with surrounding users at its m-th hovering point can be expressed as:

[0110]

[0111] Among them, D m Let be the amount of data received by the drone from the user at the m-th hovering point.

[0112] Since the drone's flight involves moving from one hovering point to another, its flight path is divided into M segments based on the number of hovering points. Within the m-th segment, the drone's energy consumption is divided into two parts: energy consumption during acceleration and deceleration, and energy consumption during constant speed. The energy consumption for one complete flight cycle (i.e., one full flight path) is:

[0113]

[0114] Among them, E F The energy consumption for a drone to fly one lap. Let m be the flight energy consumption of the UAV during the acceleration and deceleration phases in the m-th segment of its flight path. Let be the flight energy consumption of the UAV during the constant speed phase in the m-th segment of its flight path.

[0115] In this embodiment, T is used. A T D T U This represents the time required for the drone's acceleration, deceleration, and constant speed phases. When the drone's acceleration is 'a', the time required for the acceleration and deceleration phases is equal. To simplify the description, the time is first discretized into N equal time slots, i.e., T. A =Nλ t , λ t Let v represent the length of the time slot, and let v represent the speed of the UAV in the nth time slot. n =αλ t n, n = 1, 2, ..., N. Using λ v =aλ t This represents the change in the drone's velocity within each time slot, yielding v. n =λ v n, n = 1, 2, ..., N. Then, based on the UAV's propulsion energy model, the flight energy consumption of the UAV during the acceleration and deceleration phases between the two hovering points is obtained as follows:

[0116]

[0117] Among them, E FA P represents the flight energy consumption of the drone during the acceleration and deceleration phases when it hovers between two hovering points. Fappro (v n Human-machine interaction at speed v n The power below.

[0118] After the drone accelerates through N time slots, it enters a constant speed phase. The flight energy consumption of the drone during the constant speed phase between two hovering points is:

[0119] E FU =P Fappro (vN )T U

[0120] Among them, E FU v represents the flight energy consumption of the drone during the constant velocity phase when hovering between two hovering points. N Let P be the velocity of the UAV in the Vth time slot. FapprO (v N For the drone at speed v N The power below.

[0121] Based on the propulsion energy model of the UAV, and by considering the set of horizontal positions of the hovering point Q, the UAV's received signal-to-noise ratio γ, and the change in the UAV's velocity λ in each time slot... v The length of the time slot λ t And the time T required for the drone to maintain a constant speed phase. U To perform joint optimization to minimize the energy consumption of drone services to users, the optimization problem becomes:

[0122]

[0123]

[0124] Where E is the total energy consumption of the drone, E F For the flight energy consumption of drones, E H Let λ be the hovering energy consumption of the drone, Q be the set of horizontal positions of the hovering points, γ be the received signal-to-noise ratio of the drone, and λ be the [missing value]. v Let λ be the change in the speed of the UAV within each time slot. t T is the length of the time slot. U v is the time required for the drone to maintain a constant speed phase. n Let v be the velocity of the UAV in the nth time slot. N Let L be the velocity of the UAV in the Nth time slot. m Let m be the flight distance between the m-th hovering point and the (m-1)-th hovering point. Where, x m y m Let x be the coordinate of the m-th hovering point. m-1 y m-1 V represents the coordinates of the (m-1)th hovering point. max a is the maximum speed of the drone. max λ is the maximum acceleration of the drone. max γ0 is the maximum length of the time slot, γ0 is the signal-to-noise ratio threshold of the UAV, and N is the total number of time slots.

[0125] C1 constrains the straight-line distance between the two hovering points traversed by the UAV. C2 constrains the maximum speed of the UAV within the time slot when it accelerates to uniform flight. C3 constrains the speed change of the UAV during acceleration and deceleration phases. C4 constrains the length of the time slot. C5 constrains the service quality of the UAV. Question 1 is a non-convex problem involving convex optimization and linear programming, which is difficult to solve directly. Therefore, Question 1 is decomposed into two sub-problems: UAV path planning and minimizing the energy consumption of the UAV.

[0126] (1) Regarding UAV path planning:

[0127] First, users are clustered and grouped to form a Delaunay triangle. Based on Fermat point theory, preliminary hovering points are determined. After determining the Fermat points, it is evaluated whether these points are within the effective communication range between the users and the UAV. Considering the predefined received signal-to-noise ratio (SNR) threshold for the UAV, power allocation is not studied; therefore, the transmit power of all users is constant. Furthermore, since there are relatively few scatterers at sea, the LOS is mainly affected by small-scale fading, which can be predicted using distance information. Therefore, small-scale fading is known. Thus, it is assumed that the SNR is mainly affected by large-scale fading, which is related to the distance between the UAV and the user. Small-scale fading is typically complex Gaussian fading, so the statistics of small-scale fading are known. Therefore, the SNR is actually only affected by large-scale fading. The distance threshold between the UAV and the user is defined as R. The formula for finding the suboptimal point in the Delaunay triangle formed with the positions of the i, j, and k users as vertices is:

[0128]

[0129]

[0130] Where, q m Let f(x) be the horizontal position of the m-th hovering point (i.e., the second best point being sought). m ,y m x is the sum of the distances from the m-th hovering point (i.e., the second-best point being sought) to the i, j, and k-th users. m y m Let a be the coordinates of the m-th hovering point (i.e., the second-best point being sought). i b i Let a be the coordinates of the i-th user. j b j Let a be the coordinates of the j-th user. k b k Let be the coordinates of the k-th user.

[0131] Existing convex optimization methods require the objective function to be convex. Therefore, it is necessary to transform the objective function into a convex function. Consider using the quasi-Newton method. First, the objective function is transformed, and its constraints are converted into Lagrange multipliers to obtain the Lagrange function. Then, the Hessian matrix is ​​solved. Using the quasi-Newton method, a series of quadratic programming subproblems are solved to gradually find the closest value.

[0132] (2) Regarding minimizing the energy consumption of drones:

[0133] After determining the hovering point of the drone, the variable to be optimized is the change in the drone's velocity λ within each time slot. v The length of the time slot λ t The time T required for the drone to maintain a constant speed phase U Therefore, the optimization problem is:

[0134]

[0135]

[0136] Question 2 remains a non-convex problem. For the non-convex problem Question 2, the first step is to handle the energy consumption expression of the drone during flight, which is non-convex. To address this, a slack variable is introduced to handle the difficult-to-process non-convex part of the energy consumption expression.

[0137] Introduce slack variables and substitute them into v n Subsequently, the propulsion energy model for the UAV is as follows:

[0138]

[0139] The slack variables are:

[0140]

[0141] in, Let v0 be the forward average rotor-induced velocity of the UAV, and v be the slack variable. n Let be the speed of the drone in the nth time slot.

[0142] Based on the slack variables, we obtain:

[0143]

[0144] Based on the Continuous Convex Approximation (SCA) algorithm, for any given local point in the τth iteration... Both can be approximated using a first-order Taylor series expansion because it is applicable to v. n and w n It is a joint convex structure, therefore we get:

[0145]

[0146] in, w is a lower bound function n,τ Let v be the slack variable in the τth iteration. n,τ Let λ be the velocity of the UAV in the nth time slot during the τth iteration. v Let λ be the change in the speed of the UAV within each time slot. w Let be the change in the drone's velocity within the time slot where slack variables are introduced. Then the optimization problem is:

[0147]

[0148]

[0149] The optimal solution to Q3 always makes the equality in C6 true. Therefore, Q2 is equivalent to Q3. Clearly, Q3 is convex and can be solved using standard convex optimization tools.

[0150] Example 3:

[0151] This embodiment provides a drone maritime communication flight path planning device, including:

[0152] Grouping module: Used to group users scattered on the sea surface, forming a Delaunay triangle from the grouped users;

[0153] Point finding module: used to find the Fermat points of each Delaunay triangle as the hovering points of the drone;

[0154] Planning module: Used to plan the flight path of the drone based on each hovering point, determine the order in which the drone visits each hovering point, and obtain the shortest path;

[0155] Optimization module: Used to optimize the energy consumption of UAVs during acceleration, deceleration and constant speed phases based on the shortest path, and obtain the optimal path for UAV maritime communication.

[0156] The UAV maritime communication flight path planning device provided in this application embodiment can execute the UAV maritime communication flight path planning method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0157] Example 4:

[0158] This embodiment provides a system, including a processor and a storage medium;

[0159] Storage media are used to store instructions;

[0160] The processor is used to perform operations according to instructions to execute the steps of the method in Embodiment 1.

[0161] Example 5:

[0162] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in Embodiment 1.

[0163] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0165] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0167] The above are merely preferred embodiments of this application. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for planning flight paths for unmanned aerial vehicle (UAV) maritime communication, characterized in that, include: Users scattered across the sea surface are grouped, and the grouped users form a Delaunay triangle. Determine the hovering point of the UAV in each of the aforementioned Delaunay triangles; Based on each hovering point, the flight path of the UAV is planned, the order in which the UAV visits each hovering point is determined, and the shortest path is obtained. Based on the shortest path, the energy consumption of the UAV during acceleration, deceleration and constant speed phases is optimized to obtain the optimal path for UAV maritime communication. Determining the hovering point of the UAV within each of the aforementioned Delaunay triangles includes: Find the Fermat points of each of the aforementioned Delaunay triangles; If the distance from the Fermat point of the Delaunay triangle to its vertex is within the service range of the drone, then the Fermat point of the Delaunay triangle is used as the hovering point of the drone. If the distance from the Fermat point of the Delaunay triangle to its vertex exceeds the service range of the drone, then the second-best point of the Delaunay triangle is found as the hovering point of the drone. Among them, in the first , , In the Delaunay triangle formed by the positions of the users as vertices, the formula for finding the second-best point is: ； in, For the first The hovering point, i.e., the horizontal position of the second-best option being sought. For the first The hovering point, i.e., the second-best point being sought, is the point from which the second-best point to the first-best point is found. , , The sum of distances between users, , For the first The hovering point, i.e., the coordinates of the second-best option being sought, , For the first The coordinates of each user , For the first The coordinates of each user , For the first The coordinates of each user.

2. The UAV maritime communication flight path planning method according to claim 1, characterized in that, Users scattered across the sea surface are grouped into groups, forming a Delaunay triangle that includes: Clustering algorithms are used to group users scattered on the sea surface, and Delaunay triangles are formed with the positions of each user in each group as vertices.

3. The UAV maritime communication flight path planning method according to claim 1, characterized in that, Based on the shortest path, the energy consumption of the UAV during acceleration, deceleration, and constant speed phases is optimized to obtain the optimal path for UAV maritime communication, including: Based on the shortest path, an objective function is constructed to optimize the energy consumption of the UAV during acceleration, deceleration, and constant speed phases. By introducing slack variables to process the non-convex part of the objective function, the objective function is transformed from a non-convex function into a convex function. Solve the objective function, which is converted into a convex function, to obtain the optimal path for UAV maritime communication.

4. The UAV maritime communication flight path planning method according to claim 3, characterized in that, The objective function for optimizing the energy consumption of the UAV during acceleration, deceleration, and constant speed phases is: ； in, The total energy consumption of the drone. For the flight energy consumption of drones, Energy consumption for drone hovering. This is the set of horizontal positions of the hovering points. The signal-to-noise ratio (SNR) of the drone's receiver. This represents the change in the drone's velocity within each time slot. The length of the time slot, This refers to the time required for the drone to maintain a constant speed. For drones in the The speed of each time slot For drones in the The speed of each time slot For the first The hovering point and the first The flight distance between hovering points The maximum speed of the drone, The maximum acceleration of the drone, The maximum length of the time slot. The receiving signal-to-noise ratio threshold for the drone. This represents the total number of time slots.

5. The UAV maritime communication flight path planning method according to claim 3, characterized in that, The slack variable is: ； in, As slack variables, The forward average rotor induced velocity of the UAV. For drones in the The speed of each time slot.

6. The UAV maritime communication flight path planning method according to claim 3, characterized in that, The objective function for converting to a convex function is: ； in, For drones in the The speed of each time slot For drones in the The speed of each time slot Human-machine speed The power below, For drones at speed The power below, The hovering power of the drone. For drones in the The amount of data received from the user at each hover point For drones in the The hovering point and the first Communication rate per user This represents the change in the drone's velocity within each time slot. The length of the time slot, Let be the change in the drone's velocity within the time slot where slack variables are introduced. This refers to the time required for the drone to maintain a constant speed. For the first The hovering point and the first The flight distance between hovering points The maximum speed of the drone, The maximum acceleration of the drone, The maximum length of the time slot. The signal-to-noise ratio (SNR) of the drone's receiver. The receiving signal-to-noise ratio threshold for the drone. As slack variables, It is a lower bound function. The total number of time slots, This represents the total number of hover points.

7. A drone maritime communication flight path planning device, characterized in that, include: Grouping module: Used to group users scattered on the sea surface, forming a Delaunay triangle from the grouped users; Point finding module: used to find the Fermat points of each of the Delaunay triangles as the hovering points of the UAV; Planning module: Used to plan the flight path of the UAV based on each hovering point, determine the order in which the UAV visits each hovering point, and obtain the shortest path; Optimization module: used to optimize the energy consumption of the UAV during acceleration, deceleration and constant speed phases based on the shortest path, and obtain the optimal path for UAV maritime communication; Determining the hovering point of the UAV within each of the aforementioned Delaunay triangles includes: Find the Fermat points of each of the aforementioned Delaunay triangles; If the distance from the Fermat point of the Delaunay triangle to its vertex is within the service range of the drone, then the Fermat point of the Delaunay triangle is used as the hovering point of the drone. If the distance from the Fermat point of the Delaunay triangle to its vertex exceeds the service range of the drone, then the second-best point of the Delaunay triangle is found as the hovering point of the drone. Among them, in the first , , In the Delaunay triangle formed by the positions of the users as vertices, the formula for finding the second-best point is: ； in, For the first The hovering point, i.e., the horizontal position of the second-best option being sought. For the first The hovering point, i.e., the second-best point being sought, is the point from which the second-best point to the first-best point is found. , , The sum of distances between users, , For the first The hovering point, i.e., the coordinates of the second-best option being sought, , For the first The coordinates of each user , For the first The coordinates of each user , For the first The user's seat.

8. A UAV maritime communication flight path planning system, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 6.

Images