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

Abstract

The invention belongs to the technical field of smart grid mobile energy storage, and particularly relates to a trajectory design method and system for a wireless energy transmission network of an unmanned aerial vehicle cluster, and the method comprises the steps: firstly calculating the power of a radio frequency signal transmitted to equipment through a direct-view link based on the distance between an unmanned aerial vehicle and the equipment and air-ground channel parameters; according to the method, the direct current power collected by equipment passing through a direct-view link is calculated in combination with the characteristics of a rectifying circuit, and then under the mobility constraint and the anti-collision constraint, the unmanned aerial vehicle cluster trajectory optimization problem is constructed by taking the direct current power collected by the equipment with the minimum collected direct current power as the target. And finally, iteratively solving an unmanned aerial vehicle cluster trajectory optimization problem based on a convex optimization algorithm to obtain an optimized unmanned aerial vehicle cluster trajectory. The wireless energy transmission efficiency in the intelligent power grid can be remarkably improved, the method is suitable for scenes with high requirements for the energy transmission efficiency in the intelligent power grid, the characteristics of the rectifying circuit are considered when the computing equipment collects the direct-current power, and the method is more suitable for practical application.

Description

Technical Field

[0001] The present invention belongs to the field of smart grid mobile energy storage technology, and specifically relates to a trajectory design method and system for a UAV cluster wireless energy transmission network. Background Art

[0002] Smart grids play a key role in modern power systems. 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 power systems. Wireless energy transmission, as an emerging technology, is widely used in smart grid energy transmission due to its advantages of not requiring additional transmission lines, eliminating the cost of traditional cables, and improving the stability and cost-effectiveness of power transmission. However, since wireless energy transmission primarily transmits energy via radio frequency signals, radio frequency signals experience significant signal path attenuation on heavily obstructed surfaces, making effective long-distance energy transmission difficult.

[0003] Due to the high mobility and low-attenuation air-to-ground transmission links offered by drones, wireless charging of ground-based devices using drone-carried RF energy transmitters has become an effective solution to these problems. Furthermore, since drones' high mobility provides additional degrees of freedom to the network, optimizing drone trajectories can significantly improve the performance of drone-based wireless energy transfer networks. A series of studies have been conducted in the field on trajectory design in drone-based energy transfer systems, which have shown promise in improving system performance. For example, optimizing drone trajectories to minimize mission time and energy consumption has been shown to improve these systems. However, these studies only consider single-drone energy transfer systems, and the efficiency of wireless energy transfer for drone swarms remains low. Furthermore, in wireless energy transfer systems, RF signals must pass through a series of rectifier circuits to be converted into a usable DC signal. This conversion relationship is a complex nonlinear function, while these studies only consider ideal linear conversion or simple nonlinear conversion, making them of limited practical significance. Therefore, a trajectory optimization method for drone swarm wireless energy transfer networks is urgently needed to improve the efficiency of wireless energy transfer in smart grids. Summary of the Invention

[0004] The purpose of the present invention is to address the above-mentioned problems existing in the prior art and to provide a trajectory design method and system for a UAV cluster wireless energy transmission network that can improve the efficiency of wireless energy transmission.

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

[0006] In a first aspect, the present invention provides a trajectory design method for a UAV cluster wireless energy transmission network, the trajectory design method comprising:

[0007] S1. Calculate the RF signal power transmitted to the device through the direct line of sight link based on the distance between the drone and the device and the air-to-ground channel parameters. Combined with the characteristics of the rectifier circuit, calculate the device-collected DC power through the direct line of sight link.

[0008] S2. Under the constraints of mobility and anti-collision, the trajectory optimization problem of the UAV cluster is constructed with the goal of maximizing the DC power collected by the device with the minimum DC power;

[0009] S3. Iteratively solve the UAV cluster trajectory optimization problem based on the convex optimization algorithm to obtain the optimized UAV cluster trajectory.

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

[0011]

[0012] In the above formula, d m,k [n] represents the distance between the mth drone and the kth device in the nth time slot; q m [n] represents the position of the mth UAV in the nth time slot; w k represents the location of the kth device; H represents the flight altitude of the drone; n∈{1,...,N}, N represents the total number of time slots; m∈{1,...,M}, M represents the total number of drones; k∈{1,...,K}, K represents the total number of devices;

[0013] Calculate the RF signal power transmitted to the device through a line-of-sight link using the following formula:

[0014]

[0015]

[0016] In the above formula, Q m,k [n] represents the RF signal power transmitted from the mth UAV to the kth device through the direct line of sight link in the nth time slot; represents the probability of the existence of a LoS link between the mth UAV and the kth device in the nth time slot; β0 represents the channel attenuation coefficient per unit distance; P represents the UAV RF power; α represents the direct 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 kth device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the kth device in the nth time slot; R L,k represents the load of the rectifier circuit of the kth device; W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv Represents the reverse bias circuit of the rectifier diode; μ k [n] is about Q m,k [n] function, the function expression is as follows:

[0021]

[0022] In the above formula, n0 represents the truncation order; γ i is a positive coefficient; Represents M non-negative integers Any sequence composed of , and the sum of the sequence is equal to i, that is Represents a constant form factor.

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

[0024]

[0025] In the above formula, q represents the trajectory of the drone cluster, q={q m [n]},q m [n] represents the position of the mth UAV in the nth time slot; δ t Represents the length of each time slot; n∈{1,...,N}, 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 mth UAV in the n+1th time slot and the nth time slot respectively; V m represents the maximum speed of the mth UAV; q m [0],q m [N] represents the position of the m-th UAV in the 0th time slot and the Nth time slot respectively; q m,0 ,q m,F Represent the starting point and end point of the mth UAV respectively; m∈{1,...,M}, M represents the total number of UAVs;

[0030] The anti-collision constraints include:

[0031]

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

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

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

[0035] S32. Using convex approximation method at iterative local points, the UAV cluster trajectory optimization problem is reconstructed into a convex problem;

[0036] S33, use the ellipsoid method to solve the convex problem obtained in S32, and obtain the solution of this iteration, that is, the unmanned cluster trajectory q * ;

[0037] S34, determine whether the improvement of the objective function of this iteration compared with the objective function of 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, that is, the unmanned cluster trajectory q obtained in this iteration * As the local point of the next iteration and the auxiliary variable θ obtained in this iteration * Return to S32 together to continue iteration.

[0038] The S32 specifically comprises: approximating the calculation formula of the DC power collected by the device to a lower-bound concave function at a given iterative local point, approximating the non-convex anti-collision constraint to a convex constraint, and then reconstructing the UAV cluster trajectory optimization problem based on the calculation formula of the DC power collected by the device and the anti-collision constraint;

[0039] The expression of 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, Represents the lower bound concave function of the device's DC power acquisition formula; θ k,m [n] represents auxiliary variables; All are positive approximate coefficients; d k,m [n] represents the distance between the mth UAV and the kth device in the nth time slot; α represents the direct line of sight link attenuation index;

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

[0048]

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

[0050] The convex problem reconstructed from the UAV cluster 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]}, θ represents a set of auxiliary variables.

[0057] In a second aspect, the present invention provides a trajectory design system for a UAV cluster wireless energy transmission network, the trajectory design system comprising a device acquisition DC power calculation module, an optimization problem construction module, and an optimization solution module;

[0058] The device-collected DC power calculation module is used to calculate the RF signal power transmitted to the device through the direct line of sight link based on the distance between the drone and the device and the air-to-ground channel parameters, and calculate the device-collected DC power through the direct line of sight link in combination with the characteristics of the rectifier circuit;

[0059] The optimization problem construction module is used to construct a UAV cluster trajectory optimization problem with the goal of maximizing the DC power collected by the device with the minimum DC power under mobility constraints and anti-collision constraints;

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

[0061] The device collects DC power calculation module and 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 mth drone and the kth device in the nth time slot; q m [n] represents the position of the mth UAV in the nth time slot; w k represents the location of the kth device; H represents the flight altitude of the drone; n∈{1,...,N}, N represents the total number of time slots; m∈{1,...,M}, M represents the total number of drones; k∈{1,...,K}, K represents the total number of devices;

[0064] The device collected DC power calculation module is further configured to calculate the radio frequency signal power transmitted to the device through the direct line of sight link according to the following formula:

[0065]

[0066]

[0067] In the above formula, Q m,k [n] represents the RF signal power transmitted from the mth UAV to the kth device through the direct line of sight link in the nth time slot; represents the probability of the existence of a LoS link between the mth UAV and the kth device in the nth time slot; β0 represents the channel attenuation coefficient per unit distance; P represents the UAV RF power; α represents the direct line of sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters;

[0068] The device collected DC power calculation module is further used to calculate the device collected 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 kth device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the kth device in the nth time slot; R L,k represents the load of the rectifier circuit of the kth device; W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv Represents the reverse bias circuit of the rectifier diode; μ k [n] is about Q m,k [n] function, the function expression is as follows:

[0072]

[0073] In the above formula, n0 represents the truncation order; γ i is a positive coefficient; Represents M non-negative integers Any sequence composed of , and the sum of the sequence is equal to i, that is Represents a constant form factor.

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

[0075]

[0076] In the above formula, q represents the trajectory of the drone cluster, q={q m [n]},q m [n] represents the position of the mth UAV in the nth time slot; δ t Represents the length of each time slot; n∈{1,...,N}, 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 mth UAV in the n+1th time slot and the nth time slot respectively; V m represents the maximum speed of the mth UAV; q m [0],q m [N] represents the position of the m-th UAV in the 0th time slot and the Nth time slot respectively; q m,0 ,q m,F Represent the starting point and end point of the mth UAV respectively; m∈{1,...,M}, M represents the total number of UAVs;

[0081] The anti-collision constraints include:

[0082]

[0083] In the above formula, Indicates the nth time slot The location of the drone, r safe 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 cluster (0) as the iterative local point; the convex approximation module is used to reconstruct the UAV cluster trajectory optimization problem into a convex problem using the convex approximation method at the iterative local point; the convex problem solving module is used to solve the convex problem using the ellipsoid method to obtain the solution of this iteration, that is, the UAV cluster trajectory q * The output module is used to determine whether the improvement of the objective function of this iteration compared with the objective function of the previous iteration is less than the iteration threshold τ. If so, the iteration is stopped and the solution obtained in this iteration is output as the optimal solution; otherwise, the solution obtained in this iteration, that is, the unmanned cluster trajectory q obtained in this iteration, is output as the optimal solution. * As the local point of 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 device's collected DC power at a given iterative local point as a lower-bound concave function, and approximate the non-convex anti-collision constraint as a convex constraint. Then, the UAV cluster trajectory optimization problem is reconstructed based on the calculation formula of the device's collected DC power and the anti-collision constraint. The expression of the lower-bound concave function of the device's collected DC power formula is:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] In the above formula, Represents the lower bound concave function of the device's DC power acquisition formula; θ k,m [n] represents auxiliary variables; All are positive approximate coefficients; d k,m [n] represents the distance between the mth UAV and the kth device in the nth time slot; α represents the direct line of sight link attenuation index;

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

[0094]

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

[0096] The convex problem reconstructed from the UAV cluster 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]}, θ represents a set of auxiliary variables.

[0103] Compared with the prior art, the present invention has the following beneficial effects:

[0104] The present invention describes a trajectory design method for a UAV cluster wireless energy transmission network. The method first calculates the radio frequency signal power transmitted to the device through a direct-line-of-sight link based on the distance between the UAV and the device and the air-to-ground channel parameters, and calculates the DC power collected by the device through the direct-line-of-sight link in combination with the rectifier circuit characteristics. Then, under mobility constraints and collision avoidance constraints, a UAV cluster trajectory optimization problem is constructed with the goal of maximizing the DC power collected by the device with the minimum DC power collected. Finally, the UAV cluster trajectory optimization problem is iteratively solved based on a convex optimization algorithm to obtain an optimized UAV cluster trajectory. The above method optimizes the UAV cluster trajectory by maximizing the energy collected by the device with the minimum energy collected under the UAV mobility constraints and collision avoidance constraints, thereby significantly improving the wireless energy transmission efficiency in the smart grid. The method is suitable for scenarios in the smart grid that have high requirements for energy transmission efficiency. In addition, the rectifier circuit characteristics are taken into account when calculating the DC power collected by the device through the direct-line-of-sight link, which is more suitable for practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 Schematic diagram of the process of trajectory design method of the present invention.

[0106] Figure 2 This is the wireless energy transmission network for drone clusters that the present invention is aimed at.

[0107] Figure 3 The optimal UAV cluster trajectory obtained by simulation calculation.

[0108] Figure 4 Schematic diagram of the structure of the trajectory design system of the present invention. DETAILED DESCRIPTION

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

[0110] Example 1:

[0111] A trajectory design method for wireless energy transmission network of UAV clusters. Figure 2 The wireless energy transmission network of the UAV cluster shown in the figure is carried out, see Figure 1, the trajectory design method specifically includes the following steps:

[0112] S1. Calculate the RF signal power transmitted to the device through the direct line of sight link based on the distance between the drone and the device and the air-to-ground channel parameters. Combined with the characteristics of the rectifier circuit, calculate the device-collected DC power through the direct line of sight link.

[0113] The calculation formula for the distance between the drone and the device is:

[0114]

[0115] In the above formula, d m,k [n] represents the distance between the mth drone and the kth device in the nth time slot; q m [n] represents the position of the mth UAV in the nth time slot; w k represents the location of the kth device; H represents the flight altitude of the drone; n∈{1,...,N}, N represents the total number of time slots; m∈{1,...,M}, M represents the total number of drones; k∈{1,...,K}, K represents the total number of devices;

[0116] The calculation formula for the RF signal power transmitted to the device through the direct line of sight link is:

[0117]

[0118]

[0119] In the above formula, Q m,k [n] represents the RF signal power transmitted from the mth UAV to the kth device through the direct line of sight link in the nth time slot; represents the probability of the existence of a LoS link between the mth UAV and the kth device in the nth time slot; β0 represents the channel attenuation coefficient per unit distance; P represents the UAV RF power; α represents the direct line of sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters;

[0120] The calculation formula for the DC power collected by the device is:

[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 kth device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the kth device in the nth time slot; RL,k represents the load of the rectifier circuit of the kth device; W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv Represents the reverse bias circuit of the rectifier diode; μ k [n] is about Q m,k [n] function, the function expression is as follows:

[0124]

[0125] In the above formula, n0 represents the truncation order; γ i is a positive coefficient; Represents M non-negative integers Any sequence composed of , and the sum of the sequence is equal to i, that is represents a constant waveform factor;

[0126] S2. Under the constraints of UAV mobility and collision avoidance, a UAV cluster trajectory optimization problem is constructed with the goal of maximizing the DC power collected by the device with the lowest DC power. The device with the lowest energy collection means the device with the lowest energy collection among all ground devices. By optimizing the UAV trajectory to improve the energy collected by this device, the energy collected by other ground devices can be simultaneously improved. Therefore, maximizing the energy collected by the device with the lowest energy collection means maximizing the collection of the UAV's RF energy and converting it into DC energy that can be used by the device, thereby improving energy transmission efficiency.

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

[0128]

[0129] In the above formula, q represents the trajectory of the drone cluster, q={q m [n]},q m [n] represents the position of the mth UAV in the nth time slot; δ t Represents the length of each time slot; n∈{1,...,N}, 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 mth UAV in the n+1th time slot and the nth time slot respectively; V m represents the maximum speed of the mth UAV; q m [0],q m [N] represents the position of the m-th UAV in the 0th time slot and the Nth time slot respectively; q m,0 ,q m,F Represent the starting point and end point of the mth UAV respectively; m∈{1,...,M}, M represents the total number of UAVs;

[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 cluster trajectory optimization problem based on the convex optimization algorithm to obtain the optimized UAV cluster trajectory. The specific steps include:

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

[0139] S32. Reconstruct the UAV cluster trajectory optimization problem into a convex problem using a convex approximation method at an iterative local point. The convex approximation method is specifically as follows: first, at a given iterative local point, the calculation formula for the device's collected DC power is approximated as a lower-bound concave function, and the non-convex anti-collision constraint is approximated as a convex constraint. Then, the UAV cluster trajectory optimization problem is reconstructed based on the calculation formula for the device's collected DC power and the anti-collision constraint. The process of approximating the calculation formula for the device's collected DC power as a lower-bound concave function at a given iterative local point is as follows:

[0140] Because Q m，k The reciprocal of [n] is monotonically decreasing and convex, then according to the properties of convex functions, at the iterative local point The following inequality holds:

[0141]

[0142]

[0143]

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

[0145] Due to d k,m [n] α It is monotonically decreasing convex, then according to the properties of convex function, at the iterative local point The following inequality holds:

[0146]

[0147]

[0148]

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

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

[0151] In the above formula, Represents the lower bound concave function of the device's DC power acquisition formula;

[0152] The auxiliary variable constraints are The expression after the non-convex anti-collision constraint is approximated as a convex constraint is:

[0153]

[0154] In the above formula, Represents the mth and The iterative local points of the UAVs are finally obtained by reconstructing the convex problem of the UAV cluster trajectory optimization problem:

[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]}, θ represents the set of auxiliary variables;

[0161] S33, use the ellipsoid method to solve the convex problem obtained in S32, and obtain the solution of this iteration, that is, the unmanned cluster trajectory q * ;

[0162] S34, determine whether the improvement of the objective function of this iteration compared to the objective function of the previous iteration is less than the iteration threshold τ, if so, stop the iteration, and convert the unmanned cluster trajectory q obtained in this iteration into * Output as the optimal unmanned cluster trajectory; otherwise, the solution obtained in this iteration is used as the local point of the next iteration, and the auxiliary variable θ obtained in this iteration is used as the local point of the next iteration. * Return to S32 together to continue iteration.

[0163] Performance Verification:

[0164] The trajectory design method described in the present invention is used to simulate and analyze the wireless energy transmission network of the UAV cluster. The UAV cluster wireless energy transmission network includes two UAVs and five randomly distributed ground devices, that is, M = 2, K = 5; the simulation results are as follows Figure 3 As shown, in order to improve the efficiency of wireless energy transmission, drone clusters tend to fly near ground equipment to reduce losses, while strictly maintaining a safe distance between drones.

[0165] Example 2:

[0166] See also Figure 4 A trajectory design system for a wireless energy transmission network for a swarm of unmanned aerial vehicles (UAVs) includes a device-collected DC power calculation module, an optimization problem construction module, and an optimization solution module. The device-collected DC power calculation module is used to calculate the power of radio frequency signals transmitted to the device through a direct-line-of-sight link based on the distance between the UAV and the device and air-to-ground channel parameters, and calculates the device-collected DC power through the direct-line-of-sight link in combination with the characteristics of a rectifier circuit. Specifically, the device-collected DC power calculates the distance between the UAV and the device according to the following formula:

[0167]

[0168] In the above formula, d m,k [n] represents the distance between the mth drone and the kth device in the nth time slot; q m [n] represents the position of the mth UAV in the nth time slot; w krepresents the location of the kth device; H represents the flight altitude of the drone; n∈{1,...,N}, N represents the total number of time slots; m∈{1,...,M}, M represents the total number of drones; k∈{1,...,K}, K represents the total number of devices;

[0169] The device collects DC power and calculates the RF signal power transmitted to the device through the line-of-sight link using the following formula:

[0170]

[0171]

[0172] In the above formula, Q m,k [n] represents the RF signal power transmitted from the mth UAV to the kth device through the direct line of sight link in the nth time slot; represents the probability of the existence of a LoS link between the mth UAV and the kth device in the nth time slot; β0 represents the channel attenuation coefficient per unit distance; P represents the UAV RF power; α represents the direct line of sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters;

[0173] The device collects DC power according to 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 kth device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the kth device in the nth time slot; R L,k represents the load of the rectifier circuit of the kth device; W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv Represents the reverse bias circuit of the rectifier diode; μ k [n] is about Q m,k [n] function, the function expression is as follows:

[0177]

[0178] In the above formula, n0 represents the truncation order; γ i is a positive coefficient; Represents M non-negative integers Any sequence composed of , and the sum of the sequence is equal to i, that is represents a constant waveform factor;

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

[0180]

[0181] In the above formula, q represents the trajectory of the drone cluster, q={q m [n]},q m [n] represents the position of the mth UAV in the nth time slot; δ t Represents the length of each time slot; n∈{1,...,N}, 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 mth UAV in the n+1th time slot and the nth time slot respectively; V m represents the maximum speed of the mth UAV; q m [0],q m [N] represents the position of the m-th UAV in the 0th time slot and the Nth time slot respectively; q m,0 ,q m,F Represent the starting point and end point of the mth UAV respectively; m∈{1,...,M}, M represents the total number of UAVs;

[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 cluster trajectory optimization problem based on the convex optimization algorithm to obtain the optimized UAV cluster 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 cluster (0) as an iterative local point; the convex approximation module is used to reconstruct the UAV cluster trajectory optimization problem into a convex problem using a convex approximation method at the iterative local point; specifically, the convex approximation module is used to approximate the calculation formula of the device's DC power collection as a lower-bound concave function at a given iterative local point, and approximate the non-convex anti-collision constraint as a convex constraint, and then reconstruct the UAV cluster trajectory optimization problem based on the calculation formula of the device's DC power collection and the anti-collision constraint; the expression of the lower-bound concave function of the device's DC power collection formula is:

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196] In the above formula, Represents the lower bound concave function of the device's DC power acquisition formula; θ k,m [n] represents auxiliary variables; All are positive approximate coefficients; d k,m [n] represents the distance between the mth UAV and the kth device in the nth time slot; α represents the direct line of sight link attenuation index;

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

[0198]

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

[0200] The convex problem reconstructed from the UAV cluster 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]}, 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 of this iteration, that is, the unmanned cluster trajectory q * The output module is used to determine whether the improvement of the objective function of this iteration compared with the objective function of the previous iteration is less than the iteration threshold τ. If so, the iteration is stopped and the unmanned cluster trajectory q obtained in this iteration is converted to * Output as the optimal unmanned cluster trajectory; otherwise, the solution obtained in this iteration, i.e., the unmanned cluster trajectory q * As the local point of the next iteration and the auxiliary variable θ * Send them together to the convex approximation module to continue iteration.

[0208] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0209] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0210] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0211] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function 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, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A trajectory design method for a UAV cluster wireless energy transmission network, characterized by: The trajectory design method comprises: S1. Calculate the RF signal power transmitted to the device through the direct line of sight link based on the distance between the drone and the device and the air-to-ground channel parameters. Combined with the characteristics of the rectifier circuit, calculate the device-collected DC power through the direct line of sight link. S2. Under the constraints of mobility and anti-collision, the trajectory optimization problem of the UAV cluster is constructed with the goal of maximizing the DC power collected by the device with the minimum DC power; S3. Based on the convex optimization algorithm, the UAV cluster trajectory optimization problem is iteratively solved to obtain the optimized UAV cluster trajectory.

2. The trajectory design method of a UAV cluster wireless energy transmission network according to claim 1 is characterized by: In S1, the distance between the drone and the device is calculated according to the following formula: In the above formula, d m,k [n] represents the distance between the mth drone and the kth device in the nth time slot; q m [n] represents the position of the mth UAV in the nth time slot; w k represents the location of the kth device; H represents the flight altitude of the drone; n∈{1,...,N}, N represents the total number of time slots; m∈{1,...,M}, M represents the total number of drones; k∈{1,...,K}, K represents the total number of devices; The RF signal power transmitted to the device through the direct line of sight link is calculated according to the following formula: In the above formula, Q m,k [n] represents the RF signal power transmitted from the mth drone to the kth device via the line-of-sight link in the nth time slot; represents the probability of the existence of a LoS link between the mth drone and the kth device in the nth time slot; β0 represents the channel attenuation coefficient per unit distance; P represents the drone RF power; α represents the direct line of sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters; Calculate the DC power collected by the device 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 collected DC power of the kth device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the kth device in the nth time slot; R L,k represents the load of the rectifier circuit of the kth device; W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv Represents the rectifier diode reverse bias circuit; μ k [n] is about Q m,k [n] function, the function expression is as follows: In the above formula, n0 represents the truncation order; γ i is a positive coefficient; Represents M non-negative integers Any sequence composed of, and the sum of the sequence is equal to i, that is Represents a constant waveform factor.

3. The trajectory design method of a UAV cluster wireless energy transmission network according to claim 2 is characterized by: In S2, the objective function of the UAV cluster trajectory optimization problem includes: In the above formula, q represents the trajectory of the drone cluster, q = {q m [n]}, q m [n] represents the position of the mth UAV in the nth time slot; δ t represents the length of each time slot; n∈{1,...,N}, 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 mth UAV in the n+1th time slot and the nth time slot respectively; V m represents the maximum speed of the mth drone; q m [0], q m [N] represents the position of the mth UAV in the 0th time slot and the Nth time slot respectively; q m,0 ,q m,F Respectively represent the starting point and end point of the mth UAV; m∈{1,...,M}, M represents the total number of UAVs; The anti-collision constraints include: In the above formula, Indicates the nth time slot The location of the drone, r safe Indicates the minimum safe distance.

4. The trajectory design method of a UAV cluster wireless energy transmission network according to claim 3 is characterized by: The iterative solution to the UAV cluster trajectory optimization problem based on the convex optimization algorithm specifically includes: S31. Initialize the initial trajectory q of the drone cluster (0) , initial auxiliary variable θ (0) And the iteration threshold τ, and the initial trajectory q of the drone cluster (0) As an iterative local point; S32, using the convex approximation method at the iterative local point to reconstruct the UAV cluster trajectory optimization problem into a convex problem; S33, use the ellipsoid method to solve the convex problem obtained in S32, and obtain the solution of this iteration, that is, the unmanned cluster trajectory q * ; S34, determine whether the improvement of the objective function of this iteration compared with the objective function of 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, that is, the unmanned cluster trajectory q obtained in this iteration * As the local point of the next iteration and the auxiliary variable θ obtained in this iteration * Return to S32 together to continue iteration.

5. The trajectory design method of a UAV cluster wireless energy transmission network according to claim 4 is characterized by: The S32 is specifically as follows: at a given iterative local point, the calculation formula of the device collecting DC power is approximated as a lower bound concave function, the non-convex anti-collision constraint is approximated as a convex constraint, and then the drone cluster trajectory optimization problem is reconstructed according to the calculation formula of the device collecting DC power and the anti-collision constraint; The expression of the lower bound concave function of the DC power collection formula of the device is: In the above formula, Represents the lower concave function of the DC power collection formula of the device; θ k,m [n] represents auxiliary variables; All are positive approximate coefficients; d k,m [n] represents the distance between the mth UAV and the kth device in the nth time slot; α represents the direct line of sight link attenuation index; The expression after the non-convex anti-collision constraint is approximated as a convex constraint is: In the above formula, Represents the mth and Iteration local points of the UAV, represents the iterative local point; The convex problem reconstructed from the UAV cluster trajectory optimization problem includes: In the above formula, θ={θ m,k [n]}, θ represents a set of auxiliary variables.

6. A trajectory design system for a UAV cluster wireless energy transmission network, characterized by: The trajectory design system includes a device acquisition DC power calculation module, an optimization problem construction module, and an optimization solution module; The device collected DC power calculation module is used to calculate the power of the RF signal transmitted to the device through the direct line of sight link based on the distance between the UAV and the device and the air-to-ground channel parameters, and calculate the device collected DC power through the direct line of sight link in combination with the characteristics of the rectifier circuit; The optimization problem construction module is used to construct the trajectory optimization problem of the drone cluster with the goal of maximizing the DC power collected by the device with the minimum DC power under mobility constraints and anti-collision constraints; The optimization solution module is used to iteratively solve the UAV cluster trajectory optimization problem based on a convex optimization algorithm to obtain an optimized UAV cluster trajectory.

7. The trajectory design system for a UAV cluster wireless energy transmission network according to claim 6, characterized in that: The device acquisition DC power calculation module 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 mth drone and the kth device in the nth time slot; q m [n] represents the position of the mth UAV in the nth time slot; w k represents the location of the kth device; H represents the flight altitude of the drone; n∈{1,...,N}, N represents the total number of time slots; m∈{1,...,M}, M represents the total number of drones; k∈{1,...,K}, K represents the total number of devices; The device collected DC power calculation module is also used to calculate the radio frequency signal power transmitted to the device through the direct line of sight link according to the following formula: In the above formula, Q m,k [n] represents the RF signal power transmitted from the mth drone to the kth device via the line-of-sight link in the nth time slot; represents the probability of the existence of a LoS link between the mth drone and the kth device in the nth time slot; β0 represents the channel attenuation coefficient per unit distance; P represents the drone RF power; α represents the direct line of sight link attenuation index; B1, B2, B3, and B4 all represent environmental parameters; The device collected DC power calculation module is also used to calculate the device collected 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 collected DC power of the kth device in the nth time slot; I out,k [n] represents the DC current output by the rectifier circuit of the kth device in the nth time slot; R L,k represents the load of the rectifier circuit of the kth device; W0(·) represents the main branch of the Lambert W function; c represents the circuit hardware parameters; I inv Represents the rectifier diode reverse bias circuit; μ k [n] is about Q m,k [n] function, the function expression is as follows: In the above formula, n0 represents the truncation order; γ i is a positive coefficient; Represents M non-negative integers Any sequence composed of, and the sum of the sequence is equal to i, that is Represents a constant waveform factor.

8. The trajectory design system for a UAV cluster wireless energy transmission network according to claim 7, characterized in that: The objective function of the UAV cluster trajectory optimization problem includes: In the above formula, q represents the trajectory of the drone cluster, q = {q m [n]}, q m [n] represents the position of the mth UAV in the nth time slot; δ t represents the length of each time slot; n∈{1,...,N}, 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 mth UAV in the n+1th time slot and the nth time slot respectively; V m represents the maximum speed of the mth drone; q m [0], q m [N] represents the position of the mth UAV in the 0th time slot and the Nth time slot respectively; q m,0 ,q m,F Respectively represent the starting point and end point of the mth UAV; m∈{1,...,M}, M represents the total number of UAVs; The anti-collision constraints include: In the above formula, Indicates the nth time slot The location of the drone, r safe Indicates the minimum safe distance.

9. The trajectory design system for a UAV cluster wireless energy transmission network according to claim 8, characterized in that: 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 drone cluster. (0) , initial auxiliary variable θ (0) And the iteration threshold τ, and the initial trajectory q of the drone cluster (0) as the iterative local point; the convex approximation module is used to reconstruct the UAV cluster trajectory optimization problem into a convex problem using the convex approximation method at the iterative local point; the convex problem solving module is used to solve the convex problem using the ellipsoid method to obtain the solution of this iteration, that is, the unmanned cluster trajectory q * ; The output module is used to determine whether the improvement of the objective function of this iteration compared with the objective function of the previous iteration is less than the iteration threshold τ. If so, the iteration is stopped and the solution obtained in this iteration is output as the optimal solution; otherwise, the solution obtained in this iteration, that is, the unmanned cluster trajectory q obtained in this iteration * As the local point of the next iteration and the auxiliary variable θ obtained in this iteration * Let’s continue iterating together.

10. The trajectory design system for a UAV cluster wireless energy transmission network according to claim 9, characterized in that: The convex approximation module is used to approximate the calculation formula of the device's DC power collection to a lower bound concave function at a given iterative local point, approximate the non-convex anti-collision constraint to a convex constraint, and then reconstruct the UAV cluster trajectory optimization problem based on the calculation formula of the device's DC power collection and the anti-collision constraint; the expression of the lower bound concave function of the device's DC power collection formula is: In the above formula, Represents the lower concave function of the DC power collection formula of the device; θ k,m [n] represents auxiliary variables; All are positive approximate coefficients; d k,m [n] represents the distance between the mth UAV and the kth device in the nth time slot; α represents the direct line of sight link attenuation index; The expression after the non-convex anti-collision constraint is approximated as a convex constraint is: In the above formula, Represents the mth and Iteration local points of the UAV, represents the iterative local point; The convex problem reconstructed from the UAV cluster trajectory optimization problem includes: In the above formula, θ={θ m,k [n]}, θ represents a set of auxiliary variables.

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