Trajectory design method and system of unmanned aerial vehicle cluster wireless energy transmission network

Abstract

The present application belongs to the technical field of smart grid mobile energy storage, and particularly relates to a trajectory design method and system for a UAV cluster wireless energy transmission network. The method first calculates the radio frequency signal power propagated to the device through the direct link based on the distance between the UAV and the device and the air-ground channel parameters, and then calculates the DC power collected by the device through the direct link in combination with the rectifier circuit characteristics. Then, under the mobility constraint and anti-collision constraint, the UAV cluster trajectory optimization problem is constructed with the objective of maximizing the DC power collected by the minimum device. Finally, the UAV cluster trajectory optimization problem is solved iteratively based on the convex optimization algorithm to obtain the optimized UAV cluster trajectory. The present application can significantly improve the wireless energy transmission efficiency in the smart grid, is suitable for scenarios with high requirements for energy transmission efficiency in the smart grid, and takes into account the rectifier circuit characteristics when calculating the DC power collected by the device, which is more suitable for practical applications.

Description

Technical Field

[0001] This invention belongs to the field of mobile energy storage technology for smart grids, and specifically relates to a trajectory design method and system for a wireless energy transmission network for unmanned aerial vehicle (UAV) swarms. Background Technology

[0002] In modern power systems, smart grids play a crucial role. By applying advanced communication and control technologies, they effectively monitor and dispatch power systems to improve the efficiency of power resource utilization and optimize the balance between power supply and demand. This helps ensure the stability and security of the power system. Wireless power transfer technology, as an emerging technology, is widely used in the energy transfer field of smart grids due to its advantage of not requiring additional transmission lines, eliminating the costs of traditional cables, and improving the stability and economy of power transmission. However, since wireless power transfer primarily transmits energy via radio frequency signals, these signals face severe signal attenuation problems in areas with significant ground obstructions, making effective long-distance energy transfer difficult.

[0003] Due to the advantages of high mobility and low-attenuation air-to-ground transmission links, drones carrying radio frequency (RF) power transmitters to wirelessly charge ground devices is an effective way to solve the aforementioned problems. Furthermore, the high mobility of drones brings additional degrees of freedom to the network; optimizing drone trajectories can significantly improve the performance of drone wireless power transfer networks. A series of studies on trajectory design in drone power transfer systems have been conducted, contributing to improved system performance, such as optimizing drone trajectories to minimize mission time and energy consumption. However, existing research only considers single-drone power transfer systems, and the efficiency of wireless power transfer for drone swarms remains low. Moreover, in wireless power transfer systems, RF signals need to pass through a series of rectifier circuits to be converted into usable DC signals. This conversion is a complex nonlinear function, while the aforementioned work only considers ideal linear or simple nonlinear conversions, which has limited practical significance. Therefore, there is an urgent need for a trajectory optimization method for drone swarm wireless power transfer networks to improve the efficiency of wireless power transfer in smart grids. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a trajectory design method and system for a drone swarm wireless power transmission network that can improve the efficiency of wireless power transmission.

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

[0006] In a first aspect, the present invention provides a trajectory design method for a wireless power transfer network for unmanned aerial vehicle (UAV) swarms, the trajectory design method comprising:

[0007] S1. Calculate the RF signal power propagating to the device through the line-of-sight link based on the distance between the UAV and the device and the air-to-ground channel parameters. Combine the characteristics of the rectifier circuit to calculate the DC power collected by the device through the line-of-sight link.

[0008] S2. Under mobility and collision avoidance constraints, construct a UAV swarm trajectory optimization problem with the goal of maximizing the DC power collected by the device with the minimum DC power.

[0009] S3. The convex optimization algorithm is used to iteratively solve the UAV swarm trajectory optimization problem and obtain the optimized UAV swarm trajectory.

[0010] In S1, the distance between the drone and the device is calculated using the following formula:

[0011]

[0012] In the above formula, d m,k [n] represents the distance between the m-th drone and the k-th device in the n-th time slot; q m [n] represents the position of the m-th drone in the n-th time slot; w k The location of the k-th device is indicated; H represents the drone's flight altitude; n∈{1,...,N}, where N represents the total number of time slots; m∈{1,...,M}, where M represents the total number of drones; k∈{1,...,K}, where K represents the total number of devices.

[0013] The power of the radio frequency signal propagating to the device through the line-of-sight link is calculated using the following formula:

[0014]

[0015]

[0016] In the above formula, Q m,k [n] represents the radio frequency signal power of the m-th drone in the n-th time slot propagating to the k-th device via the line-of-sight link; β0 represents the probability of a LoS link existing between the m-th UAV and the k-th device in the n-th time slot; P represents the UAV's radio frequency power; α represents the line-of-sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters.

[0017] Calculate the DC power collected by the device using the following formula:

[0018] P k [n] = I out,k [n]2 R L,k ;

[0019]

[0020] In the above formula, P k [n] represents the DC power collected by the k-th device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the k-th device in the nth time slot; R L,k The load of the rectifier circuit of the k-th device is represented by W0(·); W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv This indicates a reverse bias circuit for the rectifier diode; μ k [n] represents the information about Q. m,k The function [n] has the following expression:

[0021]

[0022] In the above formula, n0 represents the cutoff order; γ i It is a positive coefficient; Represents M non-negative integers Any sequence formed by these sequences, where the sum of the sequences equals i, i.e. Represents a constant waveform coefficient.

[0023] In S2, the objective function of the UAV swarm trajectory optimization problem includes:

[0024]

[0025] In the above formula, q represents the trajectory of the drone swarm, q = {q m [n]},q m [n] represents the position of the m-th drone in the n-th time slot; δ t This represents the length of each time slot; n∈{1,...,N}, where N represents the total number of time slots;

[0026] The mobility constraints include:

[0027] ||q m [n+1]-q m [n]||≤V m δ t ;

[0028] q m [0] = q m,0 ,q m [N] = q m,F ;

[0029] In the above formula, q m [n+1]、qm [n] represents the position of the (n+1)th time slot and the m-th drone in the nth time slot, respectively; V m q represents the maximum speed of the m-th drone; m [0]、q m [N] represents the position of the m-th UAV in the 0th and Nth time slots, respectively; q m,0 q m,F Let m and m represent the starting point and ending point of the m-th drone, respectively; m∈{1,...,M}, where M represents the total number of drones.

[0030] The anti-collision constraints include:

[0031]

[0032] In the above formula, Indicates the nth time slot. The location of the drone r safe This indicates the minimum safe distance.

[0033] The iterative solution of the UAV swarm trajectory optimization problem based on the convex optimization algorithm specifically includes:

[0034] S31. Initialize the initial trajectory q of the drone swarm. (0) Initial auxiliary variable θ (0) And the iterative threshold τ, and the initial trajectory q of the drone swarm (0) As a local point in the iteration;

[0035] S32. Reconstruct the UAV swarm trajectory optimization problem into a convex problem by using the convex approximation method at iterative local points;

[0036] S33. Solve the convex problem obtained in S32 using the ellipsoid method to obtain the solution for this iteration, namely the unmanned swarm trajectory q. * ;

[0037] S34. Determine whether the improvement of the objective function in this iteration compared to the objective function in the previous iteration is less than the iteration threshold τ. If so, stop the iteration and output the solution obtained in this iteration as the optimal solution; otherwise, output the solution obtained in this iteration, i.e., the unmanned swarm trajectory q obtained in this iteration. * As a local point for the next iteration and the auxiliary variable θ obtained in this iteration * Let's return to S32 and continue iterating.

[0038] Specifically, S32 involves approximating the calculation formula of the DC power collected by the device as a lower bound concave function at a given iterative local point, approximating the non-convex anti-collision constraint as a convex constraint, and then reconstructing the UAV swarm trajectory optimization problem based on the calculation formula of the DC power collected by the device and the anti-collision constraint.

[0039] The expression for the lower bound concave function of the DC power acquisition formula of the device is:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] In the above formula, The lower bound concave function representing the formula for DC power acquisition by the device; θ k,m [n] represents an auxiliary variable; All are positive approximation coefficients; d k,m [n] represents the distance between the m-th UAV and the k-th device in the n-th time slot; α represents the line-of-sight link attenuation index;

[0047] The expression for the non-convex anti-collision constraint after approximating it as a convex constraint is:

[0048]

[0049] In the above formula, Represents the mth and Iterative local points of a drone Represents the iterative local point;

[0050] The convex problem reconstructed from the UAV swarm trajectory optimization problem includes:

[0051]

[0052] ||q m [n+1]-q m [n]||≤V m δ t ;

[0053] q m [0] = q m,0 ,q m [N] = q m,F ;

[0054]

[0055]

[0056] In the above formula, θ={θ m,k [n]}, where θ represents the set of auxiliary variables.

[0057] Secondly, the present invention provides a trajectory design system for a wireless power transmission network of unmanned aerial vehicle (UAV) swarms, the trajectory design system comprising a DC power calculation module for device acquisition, an optimization problem construction module, and an optimization solution module;

[0058] The device's DC power calculation module is used to calculate the power of the radio frequency signal propagating to the device through the line-of-sight link based on the distance between the UAV and the device and the air-to-ground channel parameters, and to calculate the DC power of the device through the line-of-sight link by combining the characteristics of the rectifier circuit.

[0059] The optimization problem construction module is used to construct a drone swarm trajectory optimization problem with the objective of maximizing the DC power collected by the device with the minimum DC power under mobility constraints and collision avoidance constraints.

[0060] The optimization solution module is used to iteratively solve the UAV swarm trajectory optimization problem based on the convex optimization algorithm to obtain the optimized UAV swarm trajectory.

[0061] The DC power calculation module of the device is used to calculate the distance between the drone and the device according to the following formula:

[0062]

[0063] In the above formula, d m,k [n] represents the distance between the m-th drone and the k-th device in the n-th time slot; q m [n] represents the position of the m-th drone in the n-th time slot; w k The location of the k-th device is indicated; H represents the drone's flight altitude; n∈{1,...,N}, where N represents the total number of time slots; m∈{1,...,M}, where M represents the total number of drones; k∈{1,...,K}, where K represents the total number of devices.

[0064] The device's DC power calculation module is also used to calculate the power of the radio frequency signal propagating to the device through the line-of-sight link according to the following formula:

[0065]

[0066]

[0067] In the above formula, Q m,k [n] represents the radio frequency signal power of the m-th drone in the n-th time slot propagating to the k-th device via the line-of-sight link; β0 represents the probability of a LoS link existing between the m-th UAV and the k-th device in the n-th time slot; P represents the UAV's radio frequency power; α represents the line-of-sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters.

[0068] The device's DC power acquisition calculation module is also used to calculate the device's DC power according to the following formula:

[0069] P k [n] = I out,k [n] 2 R L,k ;

[0070]

[0071] In the above formula, P k [n] represents the DC power collected by the k-th device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the k-th device in the nth time slot; R L,k The load of the rectifier circuit of the k-th device is represented by W0(·); W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv This indicates a reverse bias circuit for the rectifier diode; μ k [n] represents the information about Q. m,k The function [n] has the following expression:

[0072]

[0073] In the above formula, n0 represents the cutoff order; γ i It is a positive coefficient; Represents M non-negative integers Any sequence formed by these sequences, where the sum of the sequences equals i, i.e. Represents a constant waveform coefficient.

[0074] The objective function of the UAV swarm trajectory optimization problem includes:

[0075]

[0076] In the above formula, q represents the trajectory of the drone swarm, q = {q m [n]},q m [n] represents the position of the m-th drone in the n-th time slot; δ t This represents the length of each time slot; n∈{1,...,N}, where N represents the total number of time slots;

[0077] The mobility constraints include:

[0078] ||qm [n+1]-q m [n]||≤V m δ t ;

[0079] q m [0] = q m,0 ,q m [N] = q m,F ;

[0080] In the above formula, q m [n+1]、q m [n] represents the position of the (n+1)th time slot and the m-th drone in the nth time slot, respectively; V m q represents the maximum speed of the m-th drone; m [0]、q m [N] represents the position of the m-th UAV in the 0th and Nth time slots, respectively; q m,0 q m,F Let m and m represent the starting point and ending point of the m-th drone, respectively; m∈{1,...,M}, where M represents the total number of drones.

[0081] The anti-collision constraints include:

[0082]

[0083] In the above formula, Indicates the nth time slot. The location of the drone r safe This indicates the minimum safe distance.

[0084] The optimization solution module includes an initialization module, a convex approximation module, a convex problem solving module, and an output module; the initialization module is used to initialize the initial trajectory q of the UAV cluster. (0) Initial auxiliary variable θ (0) And the iterative threshold τ, and the initial trajectory q of the drone swarm (0) As a local point in the iteration; the convex approximation module is used to reconstruct the UAV swarm trajectory optimization problem into a convex problem at the local point using the convex approximation method; the convex problem solving module is used to solve the convex problem using the ellipsoid method to obtain the solution for this iteration, i.e., the UAV swarm trajectory q. * The output module is used to determine whether the improvement of the objective function in this iteration compared to the objective function in the previous iteration is less than the iteration threshold τ. If so, the iteration stops, and the solution obtained in this iteration is output as the optimal solution; otherwise, the solution obtained in this iteration, i.e., the unmanned swarm trajectory q obtained in this iteration, is output. * As a local point for the next iteration and the auxiliary variable θ obtained in this iteration * Let's continue iterating together.

[0085] The convex approximation module is used to approximate the calculation formula of the DC power collected by the device as a lower bound concave function at a given iterative local point, and to approximate the non-convex anti-collision constraint as a convex constraint. Then, based on the calculation formula of the DC power collected by the device and the anti-collision constraint, the UAV swarm trajectory optimization problem is reconstructed. The expression for the lower bound concave function of the DC power collection formula is:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] In the above formula, The lower bound concave function representing the formula for DC power acquisition by the device; θ k,m [n] represents an auxiliary variable; All are positive approximation coefficients; d k,m [n] represents the distance between the m-th UAV and the k-th device in the n-th time slot; α represents the line-of-sight link attenuation index;

[0093] The expression for the non-convex anti-collision constraint approximating as a convex constraint is:

[0094]

[0095] In the above formula, Represents the mth and Iterative local points of a drone Represents the iterative local point;

[0096] The convex problem reconstructed from the UAV swarm trajectory optimization problem includes:

[0097]

[0098] ||q m [n+1]-q m [n]||≤V m δ t ;

[0099] q m [0] = q m,0 ,q m [N] = q m,F ;

[0100]

[0101]

[0102] In the above formula, θ={θ m,k [n]}, where θ represents the set of auxiliary variables.

[0103] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0104] The present invention discloses a trajectory design method for a wireless energy transmission network for a drone swarm. First, based on the distance between the drone and the device and air-to-ground channel parameters, the power of the radio frequency signal propagating to the device via a line-of-sight link is calculated. Then, combined with the characteristics of the rectifier circuit, the DC power collected by the device via the line-of-sight link is calculated. Next, under mobility and collision avoidance constraints, a drone swarm trajectory optimization problem is constructed with the objective of maximizing the DC power collected by the device with the minimum DC power. Finally, the drone swarm trajectory optimization problem is iteratively solved using a convex optimization algorithm to obtain the optimized drone swarm trajectory. This method significantly improves the efficiency of wireless energy transmission in smart grids by maximizing the energy collected by the device with the minimum energy collection under drone mobility and collision avoidance constraints. It is suitable for scenarios in smart grids with high energy transmission efficiency requirements, and the rectifier circuit characteristics are considered when calculating the DC power collected by the device via the line-of-sight link, making it more practical. Attached Figure Description

[0105] Figure 1 This is a flowchart illustrating the trajectory design method described in this invention.

[0106] Figure 2 This invention relates to a wireless power transmission network for drone swarms.

[0107] Figure 3 The optimal drone swarm trajectory is obtained from simulation calculations.

[0108] Figure 4 This is a schematic diagram of the trajectory design system described in this invention. Detailed Implementation

[0109] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0110] Example 1:

[0111] A trajectory design method for a wireless power transfer network for unmanned aerial vehicle (UAV) swarms, facing situations such as Figure 2 The wireless power transfer network for the drone swarm shown is implemented, see [link / reference]. Figure 1The trajectory design method specifically includes the following steps:

[0112] S1. Calculate the RF signal power propagating to the device through the line-of-sight link based on the distance between the UAV and the device and the air-to-ground channel parameters. Combine the characteristics of the rectifier circuit to calculate the DC power collected by the device through the line-of-sight link.

[0113] The formula for calculating the distance between the drone and the equipment is:

[0114]

[0115] In the above formula, d m,k [n] represents the distance between the m-th drone and the k-th device in the n-th time slot; q m [n] represents the position of the m-th drone in the n-th time slot; w k The location of the k-th device is indicated; H represents the drone's flight altitude; n∈{1,...,N}, where N represents the total number of time slots; m∈{1,...,M}, where M represents the total number of drones; k∈{1,...,K}, where K represents the total number of devices.

[0116] The formula for calculating the power of the radio frequency signal propagating to the device through the line-of-sight link is as follows:

[0117]

[0118]

[0119] In the above formula, Q m,k [n] represents the radio frequency signal power of the m-th drone in the n-th time slot propagating to the k-th device via the line-of-sight link; β0 represents the probability of a LoS link existing between the m-th UAV and the k-th device in the n-th time slot; P represents the UAV's radio frequency power; α represents the line-of-sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters.

[0120] The formula for calculating the DC power collected by the device is as follows:

[0121] P k [n] = I out,k [n] 2 R L,k ;

[0122]

[0123] In the above formula, P k [n] represents the DC power collected by the k-th device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the k-th device in the nth time slot; RL,k The load of the rectifier circuit of the k-th device is represented by W0(·); W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv This indicates a reverse bias circuit for the rectifier diode; μ k [n] represents the information about Q. m,k The function [n] has the following expression:

[0124]

[0125] In the above formula, n0 represents the cutoff order; γ i It is a positive coefficient; Represents M non-negative integers Any sequence formed by these sequences, where the sum of the sequences equals i, i.e. Represents a constant waveform coefficient;

[0126] S2. Under the constraints of UAV mobility and collision avoidance, a UAV swarm trajectory optimization problem is constructed with the goal of maximizing the DC power collected by the device with the minimum DC power. The device with the minimum energy collection means the device that collects the least energy among all ground devices. By optimizing the UAV trajectory to increase the energy collected by this device, the energy collected by other ground devices can be increased simultaneously. Therefore, maximizing the energy collected by the device with the minimum energy collection means maximizing the collection of radio frequency energy from the UAV and converting it into DC energy that the device can use, thereby improving energy transmission efficiency.

[0127] The objective function of the UAV swarm trajectory optimization problem includes:

[0128]

[0129] In the above formula, q represents the trajectory of the drone swarm, q = {q m [n]},q m [n] represents the position of the m-th drone in the n-th time slot; δ t This represents the length of each time slot; n∈{1,...,N}, where N represents the total number of time slots;

[0130] The mobility constraints include:

[0131] ||q m [n+1]-q m [n]||≤V m δ t ;

[0132] q m [0] = q m,0 ,q m [N] = q m,F ;

[0133] In the above formula, q m [n+1]、q m [n] represents the position of the (n+1)th time slot and the m-th drone in the nth time slot, respectively; V m q represents the maximum speed of the m-th drone; m [0]、q m [N] represents the position of the m-th UAV in the 0th and Nth time slots, respectively; q m,0 q m,F Let m and m represent the starting point and ending point of the m-th drone, respectively; m∈{1,...,M}, where M represents the total number of drones.

[0134] The anti-collision constraints include:

[0135]

[0136] In the above formula, Indicates the nth time slot. The location of the drone r safe Indicates the minimum safe distance;

[0137] S3. Iteratively solve the UAV swarm trajectory optimization problem using a convex optimization algorithm to obtain the optimized UAV swarm trajectory; specific steps include:

[0138] S31. Initialize the initial trajectory q of the drone swarm. (0) Initial auxiliary variable θ (0) And the iterative threshold τ, and the initial trajectory q of the drone swarm (0) As a local point in the iteration;

[0139] S32. At iterative local points, the UAV swarm trajectory optimization problem is reconstructed into a convex problem using a convex approximation method. Specifically, the convex approximation method involves: first, approximating the calculation formula for the DC power collected by the device as a lower-bound concave function at a given iterative local point, and approximating the non-convex anti-collision constraints as convex constraints; then, reconstructing the UAV swarm trajectory optimization problem based on the calculation formula for the DC power collected by the device and the anti-collision constraints. The process of approximating the calculation formula for the DC power collected by the device as a lower-bound concave function at a given iterative local point is as follows:

[0140] Due to Q m，k If the reciprocal of [n] is monotonically decreasing and convex, then according to the properties of convex functions, at the iterative local point... The following inequalities hold:

[0141]

[0142]

[0143]

[0144] In the above formula, All are positive approximation coefficients;

[0145] Because of d k,m [n] α If the function is monotonically decreasing and convex, then according to the properties of convex functions, at the iterative local point... The following inequalities hold:

[0146]

[0147]

[0148]

[0149] In the above formula, All are positive approximation coefficients;

[0150] Introducing auxiliary variable θ k,m With constraints on [n] and auxiliary variables, the lower bound concave function of the DC power acquisition formula of the device is obtained as follows:

[0151] In the above formula, The lower bound concave function represents the formula for the DC power collected by the device.

[0152] The auxiliary variable constraint is as follows: The expression for the non-convex anti-collision constraint after approximating it as a convex constraint is:

[0153]

[0154] In the above formula, Represents the mth and The iterative local points of the drone swarm; finally, the convex problem reconstructed from the drone swarm trajectory optimization problem is:

[0155]

[0156] ||q m [n+1]-q m [n]||≤V m δ t ;

[0157] q m [0] = q m,0 ,q m [N] = q m,F ;

[0158]

[0159]

[0160] In the above formula, θ={θ m,k [n]}, where θ represents the set of auxiliary variables;

[0161] S33. Solve the convex problem obtained in S32 using the ellipsoid method to obtain the solution for this iteration, namely the unmanned swarm trajectory q. * ;

[0162] S34. Determine whether the improvement of the objective function in this iteration compared to the objective function in the previous iteration is less than the iteration threshold τ. If so, stop the iteration and obtain the unmanned cluster trajectory q obtained in this iteration. * The optimal unmanned swarm trajectory is output; otherwise, the solution obtained in this iteration is used as a local point for the next iteration, along with the auxiliary variable θ obtained in this iteration. * Let's return to S32 and continue iterating.

[0163] Performance verification:

[0164] The trajectory design method described in this invention was used to simulate and analyze a wireless power transfer network for a UAV swarm. The UAV swarm wireless power transfer network consists of two UAVs and five randomly distributed ground devices, i.e., M=2, K=5. The simulation results are as follows: Figure 3 As shown, in order to improve the efficiency of wireless power transmission, drone swarms tend to fly closer to ground equipment to reduce losses, while maintaining a strict safe distance between drones.

[0165] Example 2:

[0166] See Figure 4 A trajectory design system for a drone swarm wireless power transmission network includes a device-acquired DC power calculation module, an optimization problem construction module, and an optimization solution module. The device-acquired DC power calculation module calculates the radio frequency signal power propagating to the device via a line-of-sight link based on the distance between the drone and the device and air-to-ground channel parameters. It then calculates the device-acquired DC power via the line-of-sight link by combining the characteristics of the rectifier circuit. Specifically, the distance between the drone and the device is calculated using the following formula:

[0167]

[0168] In the above formula, d m,k [n] represents the distance between the m-th drone and the k-th device in the n-th time slot; q m [n] represents the position of the m-th drone in the n-th time slot; w kThe location of the k-th device is indicated; H represents the drone's flight altitude; n∈{1,...,N}, where N represents the total number of time slots; m∈{1,...,M}, where M represents the total number of drones; k∈{1,...,K}, where K represents the total number of devices.

[0169] The DC power collected by the device is calculated using the following formula to determine the RF signal power propagating to the device through the line-of-sight link:

[0170]

[0171]

[0172] In the above formula, Q m,k [n] represents the radio frequency signal power of the m-th drone in the n-th time slot propagating to the k-th device via the line-of-sight link; β0 represents the probability of a LoS link existing between the m-th UAV and the k-th device in the n-th time slot; P represents the UAV's radio frequency power; α represents the line-of-sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters.

[0173] The DC power collected by the device is calculated using the following formula:

[0174] P k [n] = I out,k [n] 2 R L,k ;

[0175]

[0176] In the above formula, P k [n] represents the DC power collected by the k-th device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the k-th device in the nth time slot; R L,k The load of the rectifier circuit of the k-th device is represented by W0(·); W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv This indicates a reverse bias circuit for the rectifier diode; μ k [n] represents the information about Q. m,k The function [n] has the following expression:

[0177]

[0178] In the above formula, n0 represents the cutoff order; γ i It is a positive coefficient; Represents M non-negative integers Any sequence formed by these sequences, where the sum of the sequences equals i, i.e. Represents a constant waveform coefficient;

[0179] The optimization problem construction module is used to construct a UAV swarm trajectory optimization problem under mobility constraints and collision avoidance constraints, with the objective of maximizing the DC power collected by the device with the minimum DC power collection. The objective function of the UAV swarm trajectory optimization problem includes:

[0180]

[0181] In the above formula, q represents the trajectory of the drone swarm, q = {q m [n]},q m [n] represents the position of the m-th drone in the n-th time slot; δ t This represents the length of each time slot; n∈{1,...,N}, where N represents the total number of time slots;

[0182] The mobility constraints include:

[0183] ||q m [n+1]-q m [n]||≤V m δ t ;

[0184] q m [0] = q m,0 ,q m [N] = q m,F ;

[0185] In the above formula, q m [n+1]、q m [n] represents the position of the (n+1)th time slot and the m-th drone in the nth time slot, respectively; V m q represents the maximum speed of the m-th drone; m [0]、q m [N] represents the position of the m-th UAV in the 0th and Nth time slots, respectively; q m,0 q m,F Let m and m represent the starting point and ending point of the m-th drone, respectively; m∈{1,...,M}, where M represents the total number of drones.

[0186] The anti-collision constraints include:

[0187]

[0188] In the above formula, Indicates the nth time slot. The location of the drone r safe Indicates the minimum safe distance;

[0189] The optimization solution module is used to iteratively solve the UAV swarm trajectory optimization problem based on a convex optimization algorithm to obtain the optimized UAV swarm trajectory. The optimization solution module includes an initialization module, a convex approximation module, a convex problem solving module, and an output module. The initialization module is used to initialize the initial trajectory q of the UAV swarm. (0) Initial auxiliary variable θ (0) And the iterative threshold τ, and the initial trajectory q of the drone swarm (0) As iterative local points; the convex approximation module is used to reconstruct the UAV swarm trajectory optimization problem into a convex problem at the iterative local points using a convex approximation method; specifically, the convex approximation module is used to approximate the calculation formula of the DC power collected by the device as a lower bound concave function at a given iterative local point, approximate the non-convex anti-collision constraint as a convex constraint, and then reconstruct the UAV swarm trajectory optimization problem according to the calculation formula of the DC power collected by the device and the anti-collision constraint; the expression of the lower bound concave function of the DC power collected by the device is:

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196] In the above formula, The lower bound concave function representing the formula for DC power acquisition by the device; θ k,m [n] represents an auxiliary variable; All are positive approximation coefficients; d k,m [n] represents the distance between the m-th UAV and the k-th device in the n-th time slot; α represents the line-of-sight link attenuation index;

[0197] The expression for the non-convex anti-collision constraint after approximating it as a convex constraint is:

[0198]

[0199] In the above formula, Represents the mth and Iterative local points of a drone express;

[0200] The convex problem reconstructed from the UAV swarm trajectory optimization problem includes:

[0201]

[0202] ||q m [n+1]-q m [n]||≤V m δ t ;

[0203] q m [0] = q m,0 ,q m [N] = q m,F ;

[0204]

[0205]

[0206] In the above formula, θ={θ m,k [n]}, where θ represents the set of auxiliary variables;

[0207] The convex problem solving module is used to solve the above convex problem using the ellipsoid method to obtain the solution for this iteration, namely the unmanned swarm trajectory q. * The output module is used to determine whether the improvement of the objective function in this iteration compared to the objective function in the previous iteration is less than the iteration threshold τ. If so, the iteration is stopped, and the unmanned swarm trajectory q obtained in this iteration is output. * Output the optimal unmanned swarm trajectory; otherwise, output the solution obtained in this iteration, i.e., the unmanned swarm trajectory q. * As a local point and auxiliary variable θ in the next iteration * They are sent together to the convex approximation module for further iteration.

[0208] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. 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 goods 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.

[0209] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods 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 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0210] 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.

[0211] 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.

[0212] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A trajectory design method for a wireless power transfer network for unmanned aerial vehicle (UAV) swarms, characterized in that: The trajectory design method includes: S1. Calculate the RF signal power propagating to the device through the line-of-sight link based on the distance between the UAV and the device and the air-to-ground channel parameters. Combine the characteristics of the rectifier circuit to calculate the DC power collected by the device through the line-of-sight link. S2. Under mobility and collision avoidance constraints, construct a UAV swarm trajectory optimization problem with the goal of maximizing the DC power collected by the device with the minimum DC power. S3. Solve the UAV swarm trajectory optimization problem iteratively based on the convex optimization algorithm to obtain the optimized UAV swarm trajectory; The iterative solution of the UAV swarm trajectory optimization problem based on the convex optimization algorithm specifically includes: S31. Initialize the initial trajectory q of the drone swarm. (0) Initial auxiliary variable θ (0) And the iterative threshold τ, and the initial trajectory q of the drone swarm (0) As a local point in the iteration; S32. Reconstruct the UAV swarm trajectory optimization problem into a convex problem by using the convex approximation method at iterative local points; S33. Solve the convex problem obtained in S32 using the ellipsoid method to obtain the solution for this iteration, namely the unmanned swarm trajectory q. * ; S34. Determine whether the improvement of the objective function in this iteration compared to the objective function in the previous iteration is less than the iteration threshold τ. If so, stop the iteration and output the solution obtained in this iteration as the optimal solution; otherwise, output the solution obtained in this iteration, i.e., the unmanned swarm trajectory q obtained in this iteration. * As a local point for the next iteration and the auxiliary variable θ obtained in this iteration * Let's return to S32 and continue iterating; Specifically, S32 involves approximating the calculation formula for the DC power collected by the device as a lower bound concave function at a given iterative local point, approximating the non-convex anti-collision constraint as a convex constraint, and then reconstructing the UAV swarm trajectory optimization problem based on the calculation formula for the DC power collected by the device and the anti-collision constraint.

2. The trajectory design method for a wireless power transfer network for unmanned aerial vehicle (UAV) swarms according to claim 1, characterized in that: In S1, the distance between the drone and the device is calculated using the following formula: In the above formula, d m,k [n] represents the distance between the m-th drone and the k-th device in the n-th time slot; q m [n] represents the position of the m-th drone in the n-th time slot; w k The location of the k-th device is indicated; H represents the drone's flight altitude; n∈{1,...,N}, where N represents the total number of time slots; m∈{1,...,M}, where M represents the total number of drones; k∈{1,...,K}, where K represents the total number of devices. The power of the radio frequency signal propagating to the device through the line-of-sight link is calculated using the following formula: In the above formula, Q m,k [n] represents the radio frequency signal power of the m-th drone in the n-th time slot propagating to the k-th device via the line-of-sight link; β0 represents the probability of a LoS link existing between the m-th UAV and the k-th device in the n-th time slot; P represents the UAV's radio frequency power; and α represents the line-of-sight link attenuation exponent. B1, B2, B3, and B4 all represent environmental parameters; Calculate the DC power collected by the device using the following formula: P k [n]=I out,k [n] 2 R L,k ; In the above formula, P k [n] represents the DC power collected by the k-th device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the k-th device in the nth time slot; R L,k The load of the rectifier circuit of the k-th device is represented by W0(·); W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv This indicates a reverse bias circuit for the rectifier diode; μ k [n] represents the information about Q. m,k The function [n] has the following expression: In the above formula, n0 represents the cutoff order; γ i It is a positive coefficient; Represents M non-negative integers Any sequence formed by these sequences, where the sum of the sequences equals i, i.e. Represents a constant waveform coefficient.

3. The trajectory design method for a wireless power transfer network for a drone swarm according to claim 2, characterized in that: In S2, the objective function of the UAV swarm trajectory optimization problem includes: In the above formula, q represents the trajectory of the drone swarm, q = {q m [n]},q m [n] represents the position of the m-th drone in the n-th time slot; δ t This represents the length of each time slot; n∈{1,...,N}, where N represents the total number of time slots; The mobility constraints include: ||q m [n+1]-q m [n]||≤V m δ t ; q m [0]=q m,0 ,q m [N]=q m,F ; In the above formula, q m [n+1]、q m [n] represents the position of the (n+1)th time slot and the m-th drone in the nth time slot, respectively; V m q represents the maximum speed of the m-th drone; m [0]、q m [N] represents the position of the m-th UAV in the 0th and Nth time slots, respectively; q m,0 q m,F Let m and m represent the starting point and ending point of the m-th drone, respectively; m∈{1,...,M}, where M represents the total number of drones. The anti-collision constraints include: In the above formula, Indicates the nth time slot. The location of the drone r safe This indicates the minimum safe distance.

4. The trajectory design method for a wireless power transfer network for unmanned aerial vehicle (UAV) swarms according to claim 1, characterized in that: The expression for the lower bound concave function of the DC power acquisition formula of the device is: In the above formula, The lower bound concave function representing the formula for DC power acquisition by the device; θ k,m [n] represents an auxiliary variable; All are positive approximation coefficients; d k,m [n] represents the distance between the m-th UAV and the k-th device in the n-th time slot; α represents the line-of-sight link attenuation index; The expression for the non-convex anti-collision constraint after approximating it as a convex constraint is: In the above formula, Represents the mth and Iterative local points of a drone Represents the iterative local point; The convex problem reconstructed from the UAV swarm trajectory optimization problem includes: ||q m [n+1]-q m [n]||≤V m δ t ; q m [0]=q m,0 ,q m [N]=q m,F ; In the above formula, θ={θ m,k [n]}, where θ represents the set of auxiliary variables.

5. A trajectory design system for a wireless power transfer network for unmanned aerial vehicle (UAV) swarms, characterized in that: The trajectory design system includes a DC power calculation module for equipment acquisition, an optimization problem construction module, and an optimization solution module; The device's DC power calculation module is used to calculate the power of the radio frequency signal propagating to the device through the line-of-sight link based on the distance between the UAV and the device and the air-to-ground channel parameters, and to calculate the DC power of the device through the line-of-sight link by combining the characteristics of the rectifier circuit. The optimization problem construction module is used to construct a drone swarm trajectory optimization problem with the objective of maximizing the DC power collected by the device with the minimum DC power under mobility constraints and collision avoidance constraints. The optimization solution module is used to iteratively solve the UAV swarm trajectory optimization problem based on the convex optimization algorithm to obtain the optimized UAV swarm trajectory. The optimization solution module includes an initialization module, a convex approximation module, a convex problem solving module, and an output module; the initialization module is used to initialize the initial trajectory q of the UAV cluster. (0) Initial auxiliary variable θ (0) And the iterative threshold τ, and the initial trajectory q of the drone swarm (0) As a local point in the iteration; the convex approximation module is used to reconstruct the UAV swarm trajectory optimization problem into a convex problem at the local point using the convex approximation method; the convex problem solving module is used to solve the convex problem using the ellipsoid method to obtain the solution for this iteration, i.e., the UAV swarm trajectory q. * The output module is used to determine whether the improvement of the objective function in this iteration compared to the objective function in the previous iteration is less than the iteration threshold τ. If so, the iteration stops, and the solution obtained in this iteration is output as the optimal solution; otherwise, the solution obtained in this iteration, i.e., the unmanned swarm trajectory q obtained in this iteration, is output. * As a local point for the next iteration and the auxiliary variable θ obtained in this iteration * Let's continue iterating together; The convex approximation module is used to approximate the calculation formula of the DC power collected by the device as a lower bound concave function at a given iterative local point, approximate the non-convex anti-collision constraint as a convex constraint, and then reconstruct the UAV swarm trajectory optimization problem according to the calculation formula of the DC power collected by the device and the anti-collision constraint.

6. The trajectory design system for a wireless power transfer network for a drone swarm according to claim 5, characterized in that: The DC power calculation module of the device is used to calculate the distance between the drone and the device according to the following formula: In the above formula, d m,k [n] represents the distance between the m-th drone and the k-th device in the n-th time slot; q m [n] represents the position of the m-th drone in the n-th time slot; w k The location of the k-th device is indicated; H represents the drone's flight altitude; n∈{1,...,N}, where N represents the total number of time slots; m∈{1,...,M}, where M represents the total number of drones; k∈{1,...,K}, where K represents the total number of devices. The device's DC power calculation module is also used to calculate the power of the radio frequency signal propagating to the device through the line-of-sight link according to the following formula: In the above formula, Q m,k [n] represents the radio frequency signal power of the m-th drone in the n-th time slot propagating to the k-th device via the line-of-sight link; β0 represents the probability of a LoS link existing between the m-th UAV and the k-th device in the n-th time slot; P represents the UAV's radio frequency power; and α represents the line-of-sight link attenuation exponent. B1, B2, B3, and B4 all represent environmental parameters; The device's DC power acquisition calculation module is also used to calculate the device's DC power according to the following formula: P k [n]=I out,k [n] 2 R L,k ; In the above formula, P k [n] represents the DC power collected by the k-th device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the k-th device in the nth time slot; R L,k The load of the rectifier circuit of the k-th device is represented by W0(·); W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv This indicates a reverse bias circuit for the rectifier diode; μ k [n] represents the information about Q. m,k The function [n] has the following expression: In the above formula, n0 represents the cutoff order; γ i It is a positive coefficient; Represents M non-negative integers Any sequence formed by these sequences, where the sum of the sequences equals i, i.e. Represents a constant waveform coefficient.

7. The trajectory design system for a wireless power transfer network for a drone swarm according to claim 6, characterized in that: The objective function of the UAV swarm trajectory optimization problem includes: In the above formula, q represents the trajectory of the drone swarm, q = {q m [n]},q m [n] represents the position of the m-th drone in the n-th time slot; δ t This represents the length of each time slot; n∈{1,...,N}, where N represents the total number of time slots; The mobility constraints include: ||q m [n+1]-q m [n]||≤V m δ t ; q m [0]=q m,0 ,q m [N]=q m,F ; In the above formula, q m [n+1]、q m [n] represents the position of the (n+1)th time slot and the m-th drone in the nth time slot, respectively; V m q represents the maximum speed of the m-th drone; m [0]、q m [N] represents the position of the m-th UAV in the 0th and Nth time slots, respectively; q m,0 q m,F Let m and m represent the starting point and ending point of the m-th drone, respectively; m∈{1,...,M}, where M represents the total number of drones. The anti-collision constraints include: In the above formula, Indicates the nth time slot. The location of the drone r safe This indicates the minimum safe distance.

8. The trajectory design system for a wireless power transfer network for unmanned aerial vehicle (UAV) swarms according to claim 5, characterized in that: The expression for the lower bound concave function of the DC power acquisition formula of the device is: In the above formula, The lower bound concave function representing the formula for DC power acquisition by the device; θ k,m [n] represents an auxiliary variable; All are positive approximation coefficients; d k,m [n] represents the distance between the m-th UAV and the k-th device in the n-th time slot; α represents the line-of-sight link attenuation index; The expression for the non-convex anti-collision constraint after approximating it as a convex constraint is: In the above formula, Represents the mth and Iterative local points of a drone Represents the iterative local point; The convex problem reconstructed from the UAV swarm trajectory optimization problem includes: ||q m [n+1]-q m [n]||≤V m δ t ; q m [0]=q m,0 ,q m [N]=q m,F ; In the above formula, θ={θ m,k [n]}, where θ represents the set of auxiliary variables.

Images