Drone trajectory design method and device in multi-user wireless power transmission network

Abstract

A method and device for designing a trajectory of a UAV in a multi-user wireless power transmission network, the method comprising: constructing a nonlinear energy harvesting model, obtaining a received power at a user, and calculating an average received total energy of all users; constructing a joint optimization model of the trajectory of the UAV and a transmission power, with the goal of maximizing the average received total energy; maximizing the average received total energy under the condition that the UAV meets an energy budget and a power upper limit by using a Lagrange dual method, to obtain an optimal transmission power control scheme; for a given Lagrange multiplier, solving an optimal trajectory of the UAV under the corresponding Lagrange multiplier; designing an optimal Lagrange multiplier, and solving the joint optimization model to obtain an optimal trajectory of the UAV and a transmission power scheme. The present application maximizes the average total energy collected by all users by jointly optimizing the transmission power of the UAV and the trajectory design, and takes into account the nonlinear energy harvesting process and the limitation of the energy budget of the UAV.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a method and device for designing a trajectory of a UAV in a multi-user wireless power transmission network. BACKGROUND

[0002] As a strategic emerging industry, low-altitude economy is formally written into the national development strategy. Unmanned and intelligent have become the trend. Among them, the low-altitude production service mode supported by unmanned aerial vehicles (UAV) has been widely used in logistics distribution, agricultural plant protection, emergency rescue and other fields. Based on the advantages of high mobility and high controllability of UAV, by optimizing the continuous trajectory of UAV, compared with the ground communication network, additional performance increment space can be obtained. In addition, when the UAV is flying in the air, it will generally experience a time-varying communication environment, so it is very beneficial to jointly optimize the UAV trajectory and resource allocation for different channel states.

[0003] It is worth noting that integrating UAV into wireless power transmission network has become a popular trend. Wireless power transmission network is usually composed of a group of sensor nodes with limited battery capacity, which can effectively prolong the life of network nodes and maintain the stability of the network through wireless energy transmission technology (WPT). By integrating WPT technology on the UAV and deploying the UAV as a mobile power station, the performance of the UAV-assisted WPT network can be improved. However, for the multi-user WPT network assisted by UAV, there is still a lack of effective design of the optimal continuous trajectory and transmission power of the UAV, especially considering the use of a nonlinear energy collection model to simulate the real scene. SUMMARY

[0004] The purpose of the present application is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a method and device for designing a trajectory of a UAV in a multi-user wireless power transmission network, which maximizes the average total energy collected by all ground users by jointly optimizing the transmission power and trajectory design of the UAV, and takes into account the limitations of the nonlinear energy collection process and the energy budget of the UAV.

[0005] To achieve the above purpose, the technical solution of the present application is: a method for designing a trajectory of a UAV in a multi-user wireless power transmission network, comprising:

[0006] The UAV starts from a given starting point and simultaneously transmits energy to multiple users on the ground to obtain relevant channel parameters;

[0007] A nonlinear energy collection model is constructed to obtain the received power at the user and calculate the average total energy received by all users;

[0008] With the goal of maximizing the average total received energy, and with constraints such as maximum speed, start and end point, maximum power, and total available energy, a joint optimization model for UAV trajectory and transmit power is constructed.

[0009] By using the Lagrange duality method, the average total received energy is maximized while the UAV meets the energy budget and power limit, thus obtaining the optimal transmit power control scheme.

[0010] Given a Lagrange multiplier, find the optimal UAV trajectory under the corresponding Lagrange multiplier;

[0011] Design the optimal Lagrange multipliers and solve the joint optimization model to obtain the optimal UAV trajectory and launch power scheme.

[0012] The nonlinear energy harvesting model is as follows:

[0013] ;

[0014] ;

[0015] In the formula, for Time users The received power at that location; Regarding the power of the radio frequency received signal Nonlinear functions; This refers to the output current across the diode. for Time users The radio frequency received signal power at the location; For load resistance; , For drones At all times Horizontal position; For drones Transmission power at any given moment; This represents the channel gain at a reference distance of 1m. For users Horizontal position; This is the minimum flight altitude for drones.

[0016] The joint optimization model for the UAV trajectory and transmission power is as follows:

[0017] ;

[0018] In the formula, Total number of users; for Time users the total received energy at the user; the first derivative of the horizontal position of the UAV; the maximum speed of the UAV; and the horizontal positions of the start and end points of the UAV, respectively; the energy budget.

[0019] the instantaneous user distance between the UAV and the user;

[0020] ;

[0021] wherein, the mission cycle of the UAV.

[0022] the optimal transmit power control scheme is obtained by maximizing the average total received energy under the condition that the UAV satisfies the energy budget and the power upper limit through the Lagrange dual method, and the optimal transmit power control scheme comprises the following steps:

[0023] constructing a Lagrange function as follows:

[0024] ;

[0025] wherein, the Lagrange multiplier;

[0026] the Lagrange dual function is:

[0027] ;

[0028] the optimal transmit power is solved according to the following formula:

[0029] ;

[0030] defining the objective function as , and the first derivative is:

[0031] ;

[0032] wherein, the distance between the UAV and the user at the instant;

[0033] the point at which the first derivative is zero is set as , and satisfies:

[0034] ;

[0035] for a given Lagrange multiplier , the optimal transmit power is calculated as: ​

[0036] ;

[0037] wherein, is the optimal transmit power; is the maximum transmit power of the UAV.

[0038] Solving the optimal UAV trajectory under the corresponding Lagrange multiplier for a given Lagrange multiplier includes:

[0039] The Lagrange dual function is equivalent to:

[0040] ;

[0041] wherein, is the potential energy field; is the rope shape; is the rope density; is the minimum line density constraint; is the rope mass; is the total length of the rope;

[0042] ;

[0043] wherein, and are the equivalent corresponding received power and the optimal transmit power ; is the distance from the point to the horizontal position of the user ;

[0044] The force field in the potential energy field is described by the negative gradient of the scalar potential function:

[0045] ;

[0046] wherein, is the force field in the potential energy field;

[0047] The part of the optimal rope shape from the starting point of the UAV to , , the axial resultant force is 0, and the expressions are respectively:

[0048] ;

[0049] ;

[0050] wherein, is the optimal initial tension of the rope; is the optimal solution of the initial rope tension angle. is the component of the force field in the axis direction; is the component of the force field in the axis direction; is the optimal rope shape; is the sum of the projections of the attractive force and the initial rope tension in the axis direction; is the sum of the projections of the attractive force and the initial rope tension in the axis direction; is the internal tension of the rope;

[0051] optimal rope shape is:

[0052] ;

[0053] ;

[0054] where, is the starting position of the UAV flight;

[0055] According to , the optimal UAV trajectory is represented by the optimal rope shape as:

[0056] .

[0057] The design method of the optimal Lagrange multiplier is:

[0058] When , ;

[0059] When , starting from the feasible interval of , the optimal launch power is repeatedly solved with as the midpoint, and the interval is repeatedly shortened according to , until , the optimal Lagrange multiplier is obtained.

[0060] A UAV trajectory design device in a multi-user wireless power transmission network, the device is applied to the method described above, and the device comprises:

[0061] A parameter acquisition module for a UAV to transmit energy to multiple users on the ground from a given starting point, obtaining relevant channel parameters;

[0062] An average received total energy calculation module is configured to construct a nonlinear energy harvesting model, obtain received power at the user, and calculate average received total energy of all users;

[0063] A joint optimization model construction module is configured to construct a joint optimization model of the UAV trajectory and the transmission power with the maximum average received total energy as the target and the maximum speed constraint, the start and end point constraint, the maximum power constraint and the total available energy constraint as the constraint condition;

[0064] An optimal transmission power acquisition module is configured to maximize the average received total energy under the condition that the UAV meets the energy budget and the power upper limit by using the Lagrange dual method, and obtain an optimal transmission power control scheme;

[0065] An optimal UAV trajectory acquisition module is configured to solve the optimal UAV trajectory under a given Lagrange multiplier.

[0066] A joint optimization model solving module is configured to design an optimal Lagrange multiplier and solve the joint optimization model to obtain an optimal UAV trajectory and transmission power scheme.

[0067] A UAV trajectory design device in a multi-user wireless power transmission network, comprising a memory and a processor;

[0068] The memory is configured to store computer program codes and transmit the computer program codes to the processor;

[0069] The processor is configured to execute the above-mentioned method according to the instructions in the computer program codes.

[0070] A computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned method.

[0071] Compared with the prior art, the beneficial effects of the present application are:

[0072] In the UAV trajectory design method and device in a multi-user wireless power transmission network, the transmission power and trajectory design of the UAV are jointly optimized to maximize the average total energy of all ground users, and the nonlinear energy harvesting process and the energy budget limit of the UAV are considered. First, according to the nonlinear energy harvesting model, the expression of the received charging power at the user is derived, and the expression of the average received total energy of all users is further obtained. Second, by using the dual method, the characteristics of the optimal transmission power control scheme are constructed, and the dual problem is converted into a pure trajectory design problem. Finally, by using the mechanical equivalent method and the principle of physics, the UAV non-convex trajectory problem with a large number of continuous variables is solved, and the closed-form expression of the optimal trajectory solution is constructed. Attached Figure Description

[0073] Figure 1 This is a flowchart of a method for designing drone trajectories in a multi-user wireless power transmission network according to the present invention.

[0074] Figure 2 This is a model diagram of a drone system with a multi-user wireless power transmission network in an embodiment of the present invention.

[0075] Figure 3 This is a flowchart illustrating the optimal solution to the equivalent mechanical problem in an embodiment of the present invention.

[0076] Figure 4 This is a flowchart of a binary search to obtain the optimal initial rope tension in an embodiment of the present invention.

[0077] Figure 5 This is a structural block diagram of a drone trajectory design device in a multi-user wireless power transmission network according to the present invention.

[0078] Figure 6 This is a structural block diagram of a drone trajectory design device in a multi-user wireless power transmission network according to the present invention. Detailed Implementation

[0079] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0080] See Figure 1 A method for designing drone trajectories in a multi-user wireless power transmission network, comprising:

[0081] S1. The UAV starts from a given starting point and transmits energy to multiple users on the ground simultaneously, obtaining relevant channel parameters;

[0082] S2. Construct a nonlinear energy harvesting model to obtain the received power at the user and calculate the average total received energy for all users;

[0083] S3. With the goal of maximizing the average total received energy, and with constraints such as maximum speed, start and end point, maximum power, and total available energy, construct a joint optimization model for the UAV trajectory and transmission power.

[0084] S4. By using the Lagrange duality method, the average total received energy is maximized while the UAV meets the energy budget and power limit, thus obtaining the optimal transmit power control scheme.

[0085] S5. Given the Lagrange multipliers, solve for the optimal UAV trajectory under the corresponding Lagrange multipliers;

[0086] S6, design the optimal Lagrange multiplier, and solve the joint optimization model to obtain the optimal UAV trajectory and transmission power scheme.

[0087] The present application utilizes the maneuverability of the UAV to jointly design the trajectory and power of the UAV to maximize the average total energy collection of the UAV in a multi-user wireless power transmission network. The UAV flies at a fixed height and is responsible for providing energy supply for multiple user nodes on the ground. The starting point and the end point of the UAV are preset to charge at the designated landing position and prepare for subsequent energy supply tasks. The present application adopts a nonlinear energy collection model to maximize the average energy collection of all user nodes within a fixed time by jointly optimizing the trajectory and transmission power of the UAV. However, the nonlinear energy collection function and the infinite variable in the continuous trajectory bring great challenges to the non-convex problem of joint design. To solve this problem, the present application utilizes the convexity in the energy collection model to construct a tight convex approximation for the reconstructed problem. Due to the limited energy of wireless transmission, through dual analysis, the closed-form expression of the optimal transmission power of the UAV is obtained, and the dual joint optimization problem is transformed into a UAV trajectory design problem. By adopting a mechanical equivalent method, the variable dimension-reduced pure trajectory design problem is ingeniously equivalent to a variable density rope shape design problem, so as to construct a closed-form solution of the optimal continuous trajectory by means of mechanical principles. This method has low complexity and strong competitiveness in low latency scenarios.

[0088] The present application takes a multi-user wireless power transmission network as an example, as shown in Figure 2 . The UAV is a mobile transmitting end, starting from a known starting point, and the user nodes on the ground are receiving ends. The UAV flies at a fixed height from the starting point, simultaneously transmits energy to multiple users on the ground, and arrives at a given end point. The communication parameters obtained include the maximum speed limit of the UAV flight, the maximum transmission power limit , the minimum flight height of the UAV , the starting point and end point position information of the UAV are and , the total number of ground users is , respectively placed in , , all located on the ground. The communication duration between the UAV and the ground user is . Since the direct link between the UAV and the ground node is strong, the channel is modeled as a free space ideal model, and the channel power gain per unit distance is expressed as .

[0089] Further, the present application proposes to employ a small-signal model to characterize the nonlinearity of the rectifier (diode) during DC conversion at the user. During energy harvesting, the radio frequency signals emitted by the UAV are received by the ground node and converted to DC signals for battery supply. The relationship between the voltage and the output current across the diode can be modeled as:

[0090] ;

[0091] where is the reverse bias saturation current across the diode; is the ideal factor; is the thermal voltage of the diode.

[0092] By performing a Taylor expansion of the nonlinear current function of the voltage across the diode, the output current can be obtained as a nonlinear implicit function of the received radio frequency received signal power , denoted as:

[0093] ;

[0094] where is the load resistance; is the order of truncation, denoting the accuracy of the nonlinear current model approximation; all factors are intermediate constants, which can be obtained from the Taylor expansion.

[0095] For any given radio frequency signal power , the value of can be obtained accordingly. To further improve the computational efficiency, with the help of the Lambert W function properties, the explicit expression of can be obtained, denoted as:

[0096] ;

[0097] where is the principal branch of the Lambert W function, is the inverse function of . When is a strictly increasing convex function, which is widely used when dealing with problems involving exponentials and logarithms. In addition, the charging power at the user satisfies , which means that the charging power at the user is a nonlinear function of the radio frequency received signal power , denoted as . Therefore, at any moment the user The received power at the user may be expressed as

[0098] ;

[0099] ;

[0100] wherein is the received power at the user at time ; is a non-linear function of the received radio frequency signal power ; is the output current on the diode ; is the received radio frequency signal power at the user at time ; is the load resistance , is the horizontal position of the UAV at time ; is the transmission power of the UAV at time ; is the channel gain when the reference distance is 1m ; is the horizontal position of the user ;

[0101] is the minimum flight height of the UAV . Convexity proof of the non-linear model: Although the received power cannot be expressed as an elementary function, the corresponding design can be made by the convexity of the non-linear function . In particular, let and denote the horizontal distance from the current position of the UAV at time to the user , then

[0102] . If is satisfied, it can be proved that is a convex function with respect to . Since the first and second derivatives of are respectively expressed as follows:

[0103] ; ;

[0104] It can be further obtained that the following inequalities satisfy the conditions:

[0105] ;

[0106] can be obtained about It is a convex function. Note that the output current of the diode is definitely non-negative, that is... This holds true consistently. Simultaneously, the received power can be obtained. about It is a convex function, that is about It is a convex function. Furthermore, we can obtain... It is about A decreasing convex function.

[0107] Since the onboard battery capacity of drones is limited, this invention considers any Drone power at any time Satisfying maximum transmit power constraints , that is ,in, A pre-designed mission cycle for drones.

[0108] Similarly, the energy budget of the drone can also be pre-scheduled. Therefore, in wireless power transmission networks, this invention considers the limitation of the total available energy to be transmitted. That is, satisfying .

[0109] To fully utilize the energy collection capabilities of UAVs in wireless power transmission networks and enhance the energy collection intensity for multiple ground users, this invention assumes that all ground users can access energy throughout the entire UAV mission cycle. It receives infinitely collected energy. Therefore, in Time users The total energy received at the point is:

[0110] ;

[0111] Based on the above, it can be seen that... It is a function of the drone's trajectory and power. Time users The total energy received at the location is also related to the drone's trajectory. and transmission power The function. By jointly optimizing the continuous trajectory and transmit power of the UAV, the mission cycle is improved. The goal is to maximize the average total received energy for all users within the region, thereby further improving wireless power transmission performance while satisfying constraints on maximum speed, start and end points, maximum power, and total available energy. The joint optimization model for UAV trajectory and transmit power is as follows:

[0112] ;

[0113] In the formula, is the total number of users; is the total received energy at time ; is the first derivative of the horizontal position of UAV; is the maximum speed of UAV; and are the horizontal positions of the start point and the end point of UAV respectively; is the energy budget.

[0114] Further, by using the Lagrange dual method, the optimal transmit power control scheme is obtained by maximizing the average total received energy under the condition that the energy budget and the power upper limit of the UAV are satisfied: in order to improve the solving efficiency of the problem, the problem is decoupled by using the properties of constraints C4 and C5 in the joint optimization model (original problem (OP)) to obtain the closed-form expression of the optimal transmit power, and the original problem is converted into a pure trajectory design problem.

[0115] The original problem (OP) is characterized by using the dual method, and according to the constraint C4, a Lagrange multiplier is given, and part of the Lagrange function of the original problem (OP) is constructed as:

[0116] ;

[0117] In the formula, is the Lagrange multiplier;

[0118] The Lagrange dual function is:

[0119] ;

[0120] The dual problem (DP) corresponding to the original problem (OP) is constructed as:

[0121] ;

[0122] For any given and the UAV trajectory , the optimal transmit power can be obtained by the following formula:

[0123] ;

[0124] In the above non-linear energy harvesting model, is a decreasing convex function about , due to the symmetry of the convex function, that is is an increasing concave function about . In addition, is a decreasing convex function about is a linear increasing function, which is both convex and concave. To find the optimal transmit power, the first derivative condition is needed to solve the minimum value.

[0125] The objective function is defined as The first derivative is obtained as

[0126] ;

[0127] In the formula, is the distance between the UAV and the user at time t;

[0128] The point where the first derivative is zero is denoted as , which is related to the trajectory of the UAV (or the distance between the UAV and each user ) and the Lagrange multiplier , and satisfies:

[0129] ;

[0130] Considering the boundary condition , for a given Lagrange multiplier , the optimal transmit power is calculated as:

[0131] ;

[0132] In the formula, is the optimal transmit power; is the maximum transmit power of the UAV.

[0133] The present application characterizes the original problem by the dual method, and the corresponding Lagrange dual function can be obtained. According to the convexity of the user charging power and the derivative of the UAV transmit power in the non-linear energy collection module, the simplified optimization problem is solved, and the optimal power is expressed as a function of the Lagrange multiplier and the UAV trajectory , so as to obtain the optimal power control scheme.

[0134] Further, according to the optimal power control scheme, a new dual function can be obtained, which is a pure UAV trajectory design problem. The pure trajectory design problem can be successfully solved by mechanical equivalent method, and the solving process is shown in Figure 3 , wherein the optimal initial rope tension corresponding to the optimal solution of the rope shape can be solved by binary search, and the solving program flow is shown in Figure 4 .

[0135] For any given and UAV trajectory ​, combining the Lagrangian dual function and the optimal transmit power, the following new dual function is obtained:

[0136] ;

[0137] The new dual function becomes a pure drone trajectory design problem. In the dual method, for any given optimal solution to the pure trajectory design problem, the dual problem (DP) can be solved. Since the dual problem (DP) and the original problem (OP) have strong duality, when , the problem in the new dual function will produce the same optimal drone trajectory as in the original problem (OP) and formulate the optimal transmit power control scheme.

[0138] For any given , is always constant, so the term can be removed in the optimization process for the dual problem, and the dual problem is further equivalent to the mechanical problem (MP), which ingeniously converts the continuous trajectory design problem of the drone into the variable density rope shape design.

[0139] The Lagrangian dual function is equivalent to the mechanical equivalent method as follows:

[0140] ;

[0141] In the formula, is the potential energy field, which is used to represent the objective function of the dual problem; is the rope shape; is the rope density; is the minimum line density constraint; is the rope mass; is the total length of the rope;

[0142] ;

[0143] In the formula, and are the equivalent corresponding received power and optimal transmit power in the drone trajectory design; is the horizontal distance from the point to the user , which is equivalent to the horizontal distance from the drone to the user in the trajectory design; the equivalent and are obtained by replacing with . The potential energy field is a function of , and changes with The values of the scalar potential function differ.

[0144] The force field in the potential energy field is described by the negative gradient of the scalar potential function:

[0145] ;

[0146] where, is the force field in the potential energy field;

[0147] According to the principle of minimum total potential energy, the optimal string solution must remain in equilibrium, which greatly facilitates the construction of the optimal string solution Under the optimal string shape, the part from the starting point of the UAV to , the axial resultant force is 0, and the expressions are respectively:

[0148] ;

[0149] ;

[0150] where, is the initial tension of the optimal string; is the optimal solution of the initial string tension angle; is the component of the force field in the axis direction; is the component of the force field in the axis direction; is the optimal string shape; is the sum representing the projection of the attractive force and the initial string tension in the axis direction; is the sum representing the projection of the attractive force and the initial string tension in the axis direction; is the internal tension of the string;

[0151] Given and , combined with the initial value condition of , the equivalent problem of the optimal string shape under the condition of insufficient string mass is:

[0152] ;

[0153] ;

[0154] where, is the starting position of the UAV flight;

[0155] According to , the optimal UAV trajectory from the optimal rope shape is expressed as:

[0156] .

[0157] Therefore, for any given , the present application can successfully construct the optimal closed-form solution of the UAV trajectory, and further solve the corresponding optimal launch power of the UAV.

[0158] Further, for the optimal solution of the original problem (OP), it must satisfy . And according to the is a convex function about . It can be obtained that is monotonic about . Therefore, there exists such that holds. At this time, due to the strong duality of the original problem (OP) and the dual problem (DP), the same UAV trajectory as the original problem (OP) will be generated, and the final task will find the corresponding optimal . The design method of the optimal Lagrange multiplier is:

[0159] When , there may be multiple applicable , and at this time ;

[0160] When , start from the feasible interval of , repeatedly solve the optimal launch power with as the midpoint, and repeatedly shorten the interval according to , until to obtain the optimal Lagrange multiplier .

[0161] The UAV reaches the given end point, the energy transmission ends, and the charging of the UAV is performed.

[0162] Referring to Figure 5 , the present application also provides a UAV trajectory design device in a multi-user wireless power transmission network, which is applied to the above-mentioned UAV trajectory design method in a multi-user wireless power transmission network, and the device comprises:

[0163] A parameter acquisition module is configured to make the UAV start from a given starting point and simultaneously transmit energy to multiple users on the ground to obtain relevant channel parameters.

[0164] an average received total energy calculation module, configured to construct a nonlinear energy harvesting model, obtain the received power at the user, and calculate the average received total energy of all users;

[0165] a joint optimization model construction module, configured to construct a joint optimization model of the UAV trajectory and the transmission power with the maximum average received total energy as a target, and the maximum speed constraint, the start and end point constraint, the maximum power constraint and the total available energy constraint as constraint conditions;

[0166] an optimal transmission power acquisition module, configured to maximize the average received total energy under the condition that the UAV meets the energy budget and the power upper limit by using the Lagrange dual method, and obtain an optimal transmission power control scheme;

[0167] an optimal UAV trajectory acquisition module, configured to solve the optimal UAV trajectory under a given Lagrange multiplier;

[0168] a joint optimization model solution module, configured to design an optimal Lagrange multiplier and solve the joint optimization model to obtain an optimal UAV trajectory and transmission power scheme.

[0169] Referring to Figure 6 , the application further provides a UAV trajectory design device in a multi-user wireless power transmission network, comprising a memory and a processor.

[0170] The memory is used for storing computer program codes and transmitting the computer program codes to the processor.

[0171] The processor is used for executing the above-mentioned multi-user wireless power transmission network UAV trajectory design method according to the instructions in the computer program codes.

[0172] The application further provides a computer readable storage medium, wherein the computer readable storage medium stores computer programs, and the computer programs are executed by the processor to realize the above-mentioned multi-user wireless power transmission network UAV trajectory design method.

[0173] Generally, the computer instructions used to realize the method of the application can be carried by any combination of one or more computer readable storage media. The non-transitory computer readable storage medium can include any computer readable medium except the signal itself in the process of temporarily propagating.

[0174] The computer readable storage medium may, for example, be tangible or intransitory and may include an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, the computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0175] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages, and specifically Python language and platform frameworks based on TensorFlow, PyTorch, etc. suitable for neural network computing. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0176] The above apparatus and non-transitory computer readable storage medium can refer to the specific description of the method and advantages of the UAV trajectory design in a multi-user wireless power transmission network, which will not be described here.

[0177] Although the embodiments of the present application have been shown and described above, it should be understood by those skilled in the art that the above embodiments are exemplary and cannot be interpreted as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for designing a trajectory of a UAV in a multi-user wireless power transmission network, the method comprising: The method comprises the following steps: An unmanned aerial vehicle starts from a given starting point and simultaneously transmits energy to multiple users on the ground to obtain relevant channel parameters; A non-linear energy harvesting model is constructed to obtain the received power at the users and calculate the average total received energy of all users; A joint optimization model of the unmanned aerial vehicle trajectory and transmission power is constructed with the maximum average total received energy as the target and the maximum speed constraint, starting point and ending point constraint, maximum power constraint and total available energy constraint as the constraint conditions; The optimal transmission power control scheme is obtained by maximizing the average total received energy under the condition that the unmanned aerial vehicle meets the energy budget and power upper limit through the Lagrange dual method; For a given Lagrange multiplier, the optimal unmanned aerial vehicle trajectory under the corresponding Lagrange multiplier is solved; The optimal Lagrange multiplier is designed, and the optimal unmanned aerial vehicle trajectory and transmission power scheme are obtained by solving the joint optimization model; The non-linear energy harvesting model is as follows: ； ； wherein is the received power at the user at the moment; is a nonlinear function of the radio frequency received signal power ; is the output current on the diode; is the radio frequency received signal power at the user at the moment; is the load resistance; , is the horizontal position of the UAV at the moment height ; is the transmission power of the UAV at the moment ; is the channel gain when the reference distance is 1m; is the horizontal position of the user ; is the minimum flight height of the UAV. 2.The method of claim 1, wherein, The joint optimization model of the unmanned aerial vehicle trajectory and transmission power is as follows: ； wherein, is the total number of users; is is the total energy received at the time instant ; is the first derivative of the horizontal position of the UAV; is the maximum speed of the UAV; and are the horizontal positions of the start and end points of the UAV, respectively; is the energy budget; is the mission period of the UAV; is the maximum transmission power of the UAV. 3.The method of claim 2, wherein, The The user The total energy received at the user's location is: ； In the formula, is the UAV mission cycle. 4.The method of claim 3, wherein, The optimal transmission power control scheme is obtained by maximizing the average total received energy under the condition that the unmanned aerial vehicle meets the energy budget and power upper limit through the Lagrange dual method, which comprises the following steps: The following Lagrange function is constructed: ； wherein is a Lagrange multiplier; Lagrangian dual function is: ； The optimal transmission power is solved according to the following formula: ； The objective function is defined as The first derivative is obtained as ； In the formula, is the distance from the UAV to the user at the moment; the distance from the UAV to the user at the moment; The point where the first derivative is zero is denoted by , satisfying: ； For a given Lagrange multiplier the optimal transmit power is calculated as: ； In the formula, is the optimal transmission power; is the maximum transmission power of the UAV. 5.The method of claim 4, wherein, For a given Lagrange multiplier, the optimal unmanned aerial vehicle trajectory under the corresponding Lagrange multiplier is solved, which comprises the following steps: The Lagrange dual function is equivalent to the following formula through the mechanical equivalent method: ； wherein is a potential energy field; is a rope shape; is a rope density; is a minimum line density constraint; is a rope mass; is a total rope length; ； wherein and is the equivalent corresponding received power and the optimal transmit power ; is the point to the horizontal position of the user ; The force field in the potential energy field is described by the negative gradient of the scalar potential function: ； wherein is the force field in the potential energy field; At the optimal rope shape, the portion of the UAV from the origin to is , the axis force is 0, and the expressions are respectively: ； ； wherein is the optimal initial tension of the rope; is the optimal solution for the initial rope tension angle; is the component of the force field in the axis direction; is the component of the force field in the axis direction; is the optimal rope shape; is the sum characterizing the projection of the attractive force and the initial rope tension in the axis direction; is the sum characterizing the projection of the attractive force and the initial rope tension in the axis direction; is the internal tension of the rope; Optimal rope shape Is: ； ； In the formula, is the starting position of the UAV flight; According to optimal drone trajectory from the optimal rope shape is expressed as: 。 6.The method of claim 4, wherein, optimal lagrange multiplier The design method is: When time, ; When , the feasible interval is started from , the optimal transmit power is repeatedly solved with as the midpoint , and the interval is repeatedly shortened according to until , the optimal Lagrange multiplier is obtained .

7. An unmanned aerial vehicle trajectory design apparatus in a multi-user wireless power transmission network, characterized by, The device is applied to the method of any one of claims 1-6, and the device comprises: A parameter acquisition module is configured to cause an unmanned aerial vehicle to start from a given starting point and simultaneously transmit energy to multiple users on the ground to obtain relevant channel parameters; An average total received energy calculation module is configured to construct a non-linear energy harvesting model to obtain the received power at the users and calculate the average total received energy of all users; A joint optimization model construction module is configured to construct a joint optimization model of the unmanned aerial vehicle trajectory and transmission power with the maximum average total received energy as the target and the maximum speed constraint, starting point and ending point constraint, maximum power constraint and total available energy constraint as the constraint conditions; An optimal transmission power acquisition module is configured to obtain the optimal transmission power control scheme by maximizing the average total received energy under the condition that the unmanned aerial vehicle meets the energy budget and power upper limit through the Lagrange dual method; An optimal unmanned aerial vehicle trajectory acquisition module is configured to solve the optimal unmanned aerial vehicle trajectory under the corresponding Lagrange multiplier for a given Lagrange multiplier; A joint optimization model solving module is configured to design the optimal Lagrange multiplier and solve the joint optimization model to obtain the optimal unmanned aerial vehicle trajectory and transmission power scheme.

8. An unmanned aerial vehicle trajectory design device in a multi-user wireless power transmission network, comprising a memory and a processor; The memory is configured to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1-6 according to the instructions in the computer program code. ​ 9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1 to 6.

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