CN110364031B - Path planning and wireless communication method for UAV swarms in ground sensor networks - Google Patents

Abstract

The invention provides a path planning and wireless communication method for an unmanned aerial vehicle cluster in a ground sensor network. The method comprises the steps that a plurality of ground sensor nodes are randomly distributed in a wide area, under the condition that each ground sensor node is guaranteed to successfully upload a certain data volume with limited energy, a path planning and wireless communication mechanism optimization model for unmanned aerial vehicle cluster information acquisition is established, the problem of minimization of total flight time of the unmanned aerial vehicle in the path planning and wireless communication mechanism optimization model for unmanned aerial vehicle cluster information acquisition is solved through an algorithm based on a dichotomy method, and the optimal flight time interval number N of the unmanned aerial vehicle is obtained. According to the invention, the flight trajectory of the unmanned aerial vehicle cluster, the scheduling strategy of the unmanned aerial vehicle and the ground sensor node and the corresponding ground sensor transmitting power and transmission time are jointly optimized, so that the flight time of the unmanned aerial vehicle is minimized, and the energy of the unmanned aerial vehicle is saved.

Description

Path planning and wireless communication method for UAV swarms in ground sensor networks

technical field

The present invention relates to the technical field of unmanned aerial vehicles, in particular to a path planning and wireless communication method of an unmanned aerial vehicle cluster in a ground sensor network.

Background technique

Unmanned Aerial Vehicle (UAV) is an unmanned aerial vehicle operated by radio remote control equipment or its own program control device, which uses aerodynamic force to navigate and perform desired functions. It has a wide range of applications, low cost and strong survivability. , Good maneuverability and easy to use. UAVs have been widely used in military, civil and other fields, greatly reducing the cost of casualties and improving the safety and adaptability of combat system platforms. In recent years, with the continuous reduction of production costs and the characteristics of miniaturization, high mobility, and flexible deployment, UAVs are increasingly used in civil and commercial fields, such as: flow control, cargo transportation, precision agriculture, aerial Inspection, environmental monitoring, emergency search and rescue and emergency communications, etc. Especially for some boring, dirty and dangerous tasks, drones have advantages over manned aircraft, so the demand for drones will increase.

With the breakthroughs in UAV "vision" technology, fixed-point hovering technology, tracking shooting technology, automatic obstacle avoidance technology, wireless communication technology and ultra-remote control technology, the load capacity, endurance time and flight altitude of UAV Therefore, UAVs can be applied to wireless communication systems to improve performance. The application of UAVs in wireless communication systems is mainly divided into two aspects: on the one hand, UAVs rely on wireless communication to control and guide operations. On the other hand, UAVs can serve as aerial base stations or relays, both of which require the use of wireless communication.

Compared with the traditional ground communication network, the main advantages of the micro and small UAV communication network are as follows:

1: Easy to deploy and flexible. The communication link can be quickly established by the flight of the drone equipped with communication equipment, eliminating the need for wired communication wiring; the lifting and lowering of the drone can be controlled at any time, so that the coverage and network capacity change with the mission area and demand. And change; tiny drones are small, light, and easy to carry. One can flexibly deploy or recycle drone base stations, addressing the tidal effects of business needs, reducing network costs as well as network energy consumption.

2: Not restricted by complex terrain. Due to the height of the base station, the traditional wireless communication method is easily affected by obstacles such as mountains and tall buildings, and the communication quality is seriously degraded. After the miniature UAV takes off, it can avoid obstacles and establish a reliable communication link by taking advantage of the air superiority of the UAV platform. Because in most scenarios, the UAV communication link is a short-distance LoS (Line-of-Sight) link, which will result in a greater performance improvement.

3: The communication equipment has strong applicability and high information transmission quality. The micro-unmanned aerial vehicle platform can easily realize the upgrading of communication equipment and improve the communication quality of communication.

In particular, UAVs can be applied to wireless ground sensor network information collection without ground infrastructure. In the traditional wireless ground sensor network, there is a fusion center, and the ground sensor nodes need to transmit the collected data to the fusion center through multiple hops, so that each ground sensor not only transmits its own data, but also transmits the data of other nodes as a relay. data, causing the ground sensor power consumption to be too fast and the network connection to fail. Using UAVs as mobile receivers for large-area information collection can avoid this problem. On the one hand, the high mobility of the UAV can ensure that it can fly to a suitable position to collect information from each ground sensor node; on the other hand, the ground-to-air uplink transmission link is usually a LoS (Line of sight, line of sight) chain road, which has a higher data rate than ground-to-ground transmission. However, most of the existing UAV applications only consider enhancing the energy efficiency or spectral efficiency of ground sensor nodes, ignoring that the limited energy of UAVs is the bottleneck of UAV-assisted wireless networks. A small number of literatures have studied the problem of data collection by a single UAV on the ground sensors arranged in a line. At this time, the ground sensors need to upload data in a given order. Therefore, for scenarios where the order of uploading data from the ground sensors is not given, how to design an unmanned aerial vehicle? The optimal trajectory and communication mechanism for the data collection of the ground sensor nodes widely distributed on the ground by the machine cluster is very necessary.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a path planning and wireless communication method for a UAV swarm in a ground sensor network to overcome the problems of the prior art.

In order to achieve the above objects, the present invention adopts the following technical solutions.

A path planning and wireless communication method for drone clusters in a ground sensor network, preferably, a plurality of ground sensor nodes are randomly distributed in a wide area, and a plurality of drones fly from their respective starting points to their ending points to perform operations on the ground sensors. Information collection, and the flying height of the UAV is fixed, the method specifically includes:

Under the condition of ensuring that each ground sensor node successfully uploads a certain amount of data with limited energy, a path planning and wireless communication mechanism optimization model for UAV swarm information collection is established, and the UAV swarm information is solved by an algorithm based on dichotomy. The problem of minimizing the total UAV flight time in the collected path planning and wireless communication mechanism optimization model, and obtaining the optimal UAV flight time interval number N;

For a given number N of UAV flight time intervals, the path planning and wireless communication mechanism optimization model of the UAV swarm information collection is used to ensure that the problem of the given flight time has a solution by introducing an auxiliary variable Δ, and according to The positive or negative of Δ judges the feasibility of a given N at this time; the optimal UAV flight trajectory, the scheduling strategy of UAV and ground sensors, and the corresponding ground sensor transmission time and transmit power.

和

分别表示无人机m的起点位置和终点位置坐标，其中

并假设第k个地面传感器的位置坐标为

且需向无人机上传B_k比特数据，第k个地面传感器可用能量为E_k焦耳，其中k∈{1，2，…，K}。中央处理器获取每个无人机起始位置和终点位置坐标以及每个地面传感器节点位置、需上传数据量大小及可用能量；Preferably, K ground sensor nodes are randomly distributed in a wide area, there are M unmanned aerial vehicles flying from their respective starting points to their ending points to collect information from the ground sensors, and the flying height of the unmanned aerial vehicles is fixed as H, to establish a Three-dimensional Cartesian coordinate system, let

and

Represent the coordinates of the starting point and the ending point of the UAV m, respectively, where

and assume that the position coordinates of the kth ground sensor are

And it is necessary to upload B _k bits of data to the UAV, and the available energy of the kth ground sensor is E _k joules, where k∈{1, 2,…,K}. The central processor obtains the coordinates of the starting and ending positions of each drone, the position of each ground sensor node, the amount of data to be uploaded, and the available energy;

表示第m个无人机在第n个时间区间的位置坐标，其中

设每个无人机的飞行速度不超过v_max米每秒，则每个无人机在一个时间区间内的飞行距离不超过d_max＝v_maxT_s米；The flight time of the UAV is discretized into N non-overlapping time intervals, and the length of each time interval is T _s seconds, so that the flight trajectory of the m-th UAV is {q _m1 , ..., q _mn , ... , q _mN }, where

Represents the position coordinates of the mth UAV in the nth time interval, where

Assuming that the flying speed of each drone does not exceed v _max meters per second, the flying distance of each drone in a time interval does not exceed d _max = v _max T _s meters;

The flight distance of any UAV in any time slot needs to meet the following constraints

where q _m0 = _um means that the UAV m takes off from its initial position, and q _mN = vm requires the UAV _m to reach its end point at the end of the mission.

Preferably, the method further includes:

赫兹，其中W表示系统总带宽，每个时间区间进一步被分成K个互不重叠的时隙，当某个无人机服务某个地面传感器时，对应的时隙长度非0，当某个无人机没有服务某个地面传感器时，对应的时隙长度为0，令τ_mnk和p_mnk分别表示在第n个时间区间，第k个地面传感器向第m个无人机传输数据时间占一个时间区间的比例以及对应的发送功率，则需要满足如下的约束条件：Let each drone be allocated the same size non-overlapping bandwidth

Hertz, where W represents the total bandwidth of the system, and each time interval is further divided into K non-overlapping time slots. When a drone serves a ground sensor, the corresponding time slot length is non-zero. When the man-machine does not serve a ground sensor, the corresponding time slot length is 0. Let τ _mnk and p _mnk respectively represent that in the nth time interval, the kth ground sensor transmits data to the mth UAV for one time. The ratio of the time interval and the corresponding transmit power need to meet the following constraints:

in

Constraint (4) means that each ground sensor can only communicate with one UAV at most in any time interval. Assuming that the channel between any UAV and any ground sensor is a LoS link, the mth UAV communicates with The channel power gain between the kth ground sensor is:

where ξ is the channel power gain when the relative distance is 1 m. The kth ground sensor needs to upload B _k bits of data in N time intervals, and the available energy of the kth ground sensor is E _k Joules, so the following constraints need to be met:

σ²表示高斯白噪声的功率谱密度，单位为瓦特每赫兹。in

σ ² represents the power spectral density of white Gaussian noise in watts per Hertz.

Preferably, the path planning and wireless communication mechanism optimization model for UAV swarm information collection includes:

且

in

and

Preferably, the problem of minimizing the total UAV flight time in the path planning of the UAV cluster information collection and the optimization model of the wireless communication mechanism is solved by an algorithm based on the bisection method, so as to obtain the optimal UAV flight time The number of time intervals N, including:

如果N可行，则令N_max＝N，否则N_min＝N，再重复过程

判断可行性，以此类推，最终获得最优的N。First determine the upper bound N _max and the lower bound N _min of the number of UAV flight time intervals, and then let

If N is feasible, let N _max =N, otherwise N _min =N, and repeat the process

Judge the feasibility, and so on, and finally obtain the optimal N.

Preferably, based on the optimal number of UAV flight time intervals N, that is, given N, the path planning and wireless communication mechanism optimization model collected by the UAV swarm information is based on continuous The algorithm of the convex approximation method solves the optimal UAV flight trajectory, the scheduling strategy of UAV and ground sensors, and the corresponding transmission time and transmission power of ground sensors, including:

Given a certain N, the original problem (8) can be rewritten as

s.t. (8b)-(8f), (9b)

The introduction of the auxiliary variable Δ ensures that the problem (9) must have a solution, and the feasibility of a given N can be judged by the positive or negative of Δ in the optimization result; where B _k is the total amount of data that the kth sensor needs to transmit,

近似为凸约束The non-convex constraint (8e) in the path planning and wireless communication mechanism optimization model of the UAV swarm information collection, namely

Approximate convex constraint

And add a penalty term to the objective function, so that the objective function becomes

是一个对角权重矩阵，对于任意n，k在第i次迭代时对角线元素为

α为惩罚项

的权重值，可根据实际设置或调整；in

is a diagonal weight matrix, and for any n, the diagonal elements of k at the ith iteration are

α is the penalty term

The weight value can be set or adjusted according to the actual situation;

将所述无人机集群信息采集的轨迹优化模型中的约束条件(8f)改写为：definition

Rewrite the constraint (8f) in the trajectory optimization model of the UAV swarm information collection as:

Rewrite the constraints (8g) in the trajectory optimization model of the UAV swarm information collection as:

是关于(e_mnk，d_mnk)的凸函数，能够被

处一阶泰勒展开式近似，则有(13a) is a non-convex constraint,

is a convex function with respect to (e _mnk , d _mnk ), which can be given by

Approximate the first-order Taylor expansion, then we have

则(13a)近似为：in

Then (13a) is approximated as:

is a convex constraint;

After approximation by convex constraints (10), (11), (12), (13b), and (15), the original problem becomes a convex problem, and the convex optimization tool is used to solve the convex problem. The results are used to update the parameters of the next iteration until the iterative calculation converges, and the optimal UAV flight trajectory {q _mn } under a given N, the scheduling strategy of UAV and ground sensors and the corresponding The ground sensor transmission time {τ _mnk }×T _s and transmission power {p _mnk }, the scheduling strategy of the ground sensor includes whether {τ _mnk } is 0 specified in the nth time interval, the mth unmanned Whether the machine communicates with the kth sensor.

It can be seen from the technical solutions provided by the above embodiments of the present invention that the present invention is that when a certain number of UAVs and their corresponding start and end positions, and a certain number of ground sensor nodes and their corresponding position coordinates are given, Ensure that each ground sensor node can successfully upload a certain amount of data under limited energy constraints, and jointly optimize the flight trajectory of the UAV cluster, the scheduling strategy of the UAV and ground sensor nodes, and the corresponding ground sensor transmission power and transmission time. , so as to minimize the flight time of the UAV and save the energy of the UAV.

Additional aspects and advantages of the present invention will be set forth in part in the following description, which will be apparent from the following description, or may be learned by practice of the present invention.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

FIG. 1 is a system scene diagram on which a drone swarm collects data from ground sensor nodes according to an embodiment of the present invention.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but not to be construed as a limitation of the present invention.

It will be understood by those skilled in the art that the singular forms "a", "an", "the" and "the" as used herein can include the plural forms as well, unless expressly stated otherwise. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of stated features, integers, steps, operations, elements and/or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components and/or groups thereof. It will be understood that when we refer to an element as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Furthermore, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as herein, are not to be taken in an idealized or overly formal sense. explain.

In order to facilitate the understanding of the embodiments of the present invention, the following will take several specific embodiments as examples for further explanation and description in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.

Embodiments of the present invention provide a method for path planning and wireless communication mechanism optimization for UAV swarm information collection in a ground sensor network. This method firstly specifies the number of UAVs and the corresponding starting position, the coordinates of the end position, the number of ground sensors and the corresponding coordinates, energy constraints, the amount of data to be uploaded, etc., and then considers the mobility constraints of the UAVs, etc. , model the problem to get the original problem. However, since the problem is a non-convex problem, the present invention proposes an algorithm based on the bisection method and the continuous convex approximation method to solve the problem. First, the number of flight time intervals N of the UAV is given, and then a continuous convex approximation is used to approximate the problem of the given N. Judging the feasibility under this condition, the optimal N and the corresponding UAV flight trajectory, scheduling strategy and resource allocation can be finally obtained by using the dichotomy method.

和

分别表示无人机m的起点位置和终点位置坐标，其中

并假设第k个地面传感器的位置坐标为

且它需向无人机上传B_k比特数据，由于地面传感器节点能量有限，第k个地面传感器可用能量为E_k焦耳，其中k∈{1，2，…，K}。并且我们假设存在一个中央处理器，可知每个无人机起始位置和终点位置坐标以及每个地面传感器节点位置、需上传数据量大小及可用能量。FIG. 1 is a system scenario diagram based on which a drone swarm performs data collection on ground sensor nodes according to an embodiment of the present invention. As shown in FIG. 1 , the present invention considers a system scenario: K ground sensor nodes are randomly distributed in In a wide area, there are M UAVs flying from their respective starting points to the end points to collect information from the ground sensors, and the flying height of the UAVs is fixed as H. The starting and ending positions of each drone are determined according to the actual situation, and the flying height of the drone must meet relevant laws and regulations. In order to facilitate the analysis and solution, a three-dimensional Cartesian coordinate system is established.

and

Represent the coordinates of the starting point and the ending point of the UAV m, respectively, where

and assume that the position coordinates of the kth ground sensor are

And it needs to upload B _k bits of data to the UAV. Due to the limited energy of the ground sensor nodes, the available energy of the kth ground sensor is E _k joules, where k∈{1, 2,…,K}. And we assume that there is a central processing unit that knows the coordinates of the starting and ending positions of each drone, the location of each ground sensor node, the amount of data to be uploaded, and the available energy.

表示第m个无人机在第n个时间区间的位置坐标，其中

假设每个无人机的飞行速度不超过v_max米每秒，则每个无人机在一个时隙内的飞行距离不超过d_max＝v_maxT_s米。In order to facilitate the subsequent solution, the flight time of the UAV is discretized into N non-overlapping time intervals, and the length of each time interval is T _s seconds, so that the flight trajectory of the mth UAV can be approximated as {q _m1 , ..., _qmn , ..., _qmN }, where

Represents the position coordinates of the mth UAV in the nth time interval, where

Assuming that the flying speed of each UAV does not exceed v _max meters per second, the flying distance of each UAV in one time slot does not exceed d _max =v _max T _s meters.

Therefore, the flight distance of any UAV in any time slot needs to meet the following constraints

where q _m0 = _um means that the UAV m takes off from its initial position, and q _mN = vm requires the UAV _m to reach its end point at the end of the mission.

赫兹，其中W表示系统总带宽，单位为赫兹。每个时间区间进一步被分成K个互不重叠的时隙，即每个无人机以TDMA(Time-division multiple access,时分多址接入)方式与其服务的地面传感器节点进行通信，可知当某个无人机服务某个地面传感器时，对应的时隙长度非0，否则为0。令τ_mnk和p_mnk分别表示在第n个时间区间，第k个地面传感器向第m个无人机传输数据时间占一个时间区间的比例以及对应的发送功率。其需要满足：Assume that each drone is allocated the same size non-overlapping bandwidth

Hertz, where W is the total system bandwidth in Hertz. Each time interval is further divided into K non-overlapping time slots, that is, each UAV communicates with the ground sensor nodes it serves by means of TDMA (Time-division multiple access, time division multiple access). When each UAV serves a ground sensor, the corresponding time slot length is not 0, otherwise it is 0. Let τ _mnk and p _mnk denote the proportion of the time that the kth ground sensor transmits data to the mth UAV in a time interval and the corresponding transmit power in the nth time interval, respectively. It needs to meet:

in

Constraint (4) means that each ground sensor can only communicate with at most one UAV in any time interval. Note that if τ _mnk =0, then p _mnk =0. It is assumed that the channel between any drone and any ground sensor is a LoS (Line of sight) link, and that the Doppler shift due to the mobility of the drone is well compensated. Therefore, the channel power gain between the mth UAV and the kth ground sensor is:

where ξ is the channel power gain when the relative distance is 1 m. As mentioned above, the kth ground sensor needs to upload B _k bits of data in N time intervals, the available energy is E _k Joules, and the constraints that need to be satisfied are:

σ²表示高斯白噪声的功率谱密度，单位为瓦特每赫兹。in

σ ² represents the power spectral density of white Gaussian noise in watts per Hertz.

The goal of the present invention is that each ground sensor can successfully upload a certain amount of data under its energy constraints, and by optimizing the flight trajectory of the UAV, the time for the UAV to collect data is minimized, thereby ensuring that no one is in a certain sense. The total flight energy of the aircraft is the smallest. According to formulas (1)-(7), the following trajectory optimization model for UAV swarm information collection can be established:

且

可以看出问题(8)在数学上不易求解，主要是由于两点。首先它是一个关于N的离散整数优化问题，其次，即便给定某个N，(8e)-(8g)也是非凸约束导致问题难以求解。in

and

It can be seen that the problem (8) is not easy to solve mathematically, mainly due to two points. First, it is a discrete integer optimization problem about N, and second, even given a certain N, (8e)-(8g) is a non-convex constraint that makes the problem difficult to solve.

For the path planning and wireless communication mechanism optimization model of the above-mentioned UAV swarm information collection, the algorithm for solving the optimal N is shown in the following algorithm 1:

如果N可行，则令N_max＝N，否则N_min＝N，再重复过程

判断可行性，以此类推，最终获得最优的N。The specific processing process includes: the embodiment of the present invention considers the use of the bisection method to solve, firstly determine the upper bound N _max and the lower bound N _min of the number of UAV flight time intervals, and then set

If N is feasible, let N _max =N, otherwise N _min =N, and repeat the process

Judge the feasibility, and so on, and finally obtain the optimal N.

The continuous convex approximation method (Successive Convex Approximation) is used to solve the non-convex subproblem under a given N in the path planning and wireless communication mechanism optimization model of the above-mentioned UAV swarm information collection.

Specifically, for a given N, problem (8) can be rewritten as

s.t. (8b)-(8f), (9b)

此处log为自然对数。如果Δ≤0，则此时的N对问题(8)可行，否则N太小了。where Δ is an auxiliary variable ensuring the feasibility of problem (9), and

Here log is the natural logarithm. If Δ≤0, then N at this time is feasible for problem (8), otherwise N is too small.

The following describes how to solve problem (9). First, the non-convex constraint (8e) can be approximated as:

And add a penalty term to the objective function, so that the objective function becomes

是一个对角权重矩阵，对于任意n，k在第i次迭代时对角线元素为

α为惩罚项

的权重值，可根据实际设置或调整。本发明实施例定义

则约束(8f)和(9c)可分别改写为：where α is the weight corresponding to the penalty term,

is a diagonal weight matrix, and for any n, the diagonal elements of k at the ith iteration are

α is the penalty term

The weight value can be set or adjusted according to the actual situation. Definitions of Embodiments of the Present Invention

Then constraints (8f) and (9c) can be rewritten as:

是关于(e_mnk，d_mnk)的凸函数，它可以被

点处一阶泰勒展开式近似。因此有Note that (12b) is still a nonconvex constraint, but

is a convex function with respect to (e _mnk , d _mnk ), which can be given by

A first-order Taylor expansion approximation at the point. Therefore there is

则(12b)可以被近似为in

Then (12b) can be approximated as

It is a convex constraint. Through (10), (11), (12a), (12c), (14), the embodiment of the present invention can obtain the following feasibility test problem for N:

注意到在取得最优解时，约束(15h)的等号成立。在给定N时，基于连续凸近似的求解算法如算法2所示，其中ζ是一个很小的数字，为了保证迭代的稳定性。in

Note that the equality sign of constraint (15h) holds when the optimal solution is obtained. When N is given, the solution algorithm based on continuous convex approximation is shown in Algorithm 2, where ζ is a small number, in order to ensure the stability of the iteration.

By approximation, the non-convex problem (9) becomes a convex problem (15), which can be solved with off-the-shelf convex optimization tools (such as cvx), and the results obtained in each iteration are used to update the parameters of the next iteration until convergence , and finally get the optimal UAV flight trajectory {q _mn }, UAV and ground sensor scheduling strategy under a given N (specified in the nth time interval by whether {τ _mnk } is 0, Whether the m-th UAV communicates with the k-th sensor) and the corresponding ground sensor transmission time {τ _mnk }×T _s and transmit power {p _mnk }.

Based on the given total number of UAV flight time intervals N, the solution algorithm based on the continuous convex approximation method for this problem is as follows: Algorithm 2:

To sum up, the present invention ensures that each ground sensor node can operate within a limited range when a certain number of UAVs and their corresponding starting and ending positions and a certain number of ground sensor nodes and their corresponding position coordinates are given. A certain amount of data is successfully uploaded under the energy constraints, and the flight time of the UAV is minimized by jointly optimizing the flight trajectory of the UAV cluster, the scheduling strategy of the UAV and ground sensor nodes, and the corresponding ground sensor transmission power and transmission time. , saving the energy of the drone.

The present invention considers a more general scenario: the ground sensor nodes are randomly distributed in a wide area, and the drone cluster is used for information collection, and the order in which the drones access the ground sensor nodes is not given, and the solution based on this scenario is more efficient practicality.

Those of ordinary skill in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the modules or processes in the accompanying drawing are not necessarily necessary to implement the present invention.

From the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art. The computer software products can be stored in storage media, such as ROM/RAM, magnetic disks, etc. , CD, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

The various embodiments in this specification are described in a progressive manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the apparatus or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for related parts. The device and system embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, It can be located in one place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited to this. Substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (3)

1. the path planning and wireless communication method of unmanned aerial vehicle swarm in a ground sensor network, it is characterized in that, multiple ground sensor nodes are randomly distributed in a wide area, and multiple unmanned aerial vehicles fly from respective starting point to terminal point to The ground sensor collects information, and the flying height of the UAV is fixed. The method specifically includes: Under the condition of ensuring that each ground sensor node successfully uploads a certain amount of data with limited energy, a path planning and wireless communication mechanism optimization model for UAV swarm information collection is established, and the UAV swarm information is solved by an algorithm based on dichotomy. The problem of minimizing the total UAV flight time in the collected path planning and wireless communication mechanism optimization model, and obtaining the optimal UAV flight time interval number N; For a given UAV flight time interval number N, use the path planning and wireless communication mechanism optimization model of the UAV swarm information collection to solve the optimal UAV flight trajectory through an algorithm based on the continuous convex approximation method , UAV and ground sensor scheduling strategy and corresponding ground sensor transmission time and transmission power; Suppose K ground sensor nodes are randomly distributed in a wide area, there are M UAVs flying from their respective starting points to the end points to collect information from the ground sensors, and the flying height of the UAVs is fixed as H, to establish a three-dimensional Cartesian coordinate system, set

and

Represent the coordinates of the starting point and the ending point of the UAV m, respectively, where

and assume that the position coordinates of the kth ground sensor are

And it is necessary to upload B _k bits of data to the UAV, the available energy of the kth ground sensor is E _k joules, where k ∈ {1, 2, ..., K}, the central processor obtains the starting position and The coordinates of the end point and the location of each ground sensor node, the amount of data to be uploaded and the available energy; The flight time of the UAV is discretized into N non-overlapping time intervals, and the length of each time interval is T _s seconds, so that the flight trajectory of the m-th UAV is {q _m1 , ..., q _mn , ... , q _mN }, where

Represents the position coordinates of the mth UAV in the nth time interval, where

Assuming that the flying speed of each UAV does not exceed v _max meters per second, the flying distance of each UAV in a time interval does not exceed d _max =v _max T _s meters; The flight distance of any UAV in any time slot needs to meet the following constraints

where q _m0 = _um means that the UAV m takes off from its initial position, and q _mN = v _m requires the UAV m to reach its end point at the end of the mission; The method also includes: Let each drone be allocated the same size non-overlapping bandwidth

Hertz, where W represents the total bandwidth of the system, and each time interval is further divided into K non-overlapping time slots. When a drone serves a ground sensor, the corresponding time slot length is non-zero. When the man-machine does not serve a ground sensor, the corresponding time slot length is 0. Let τ _mnk and p _mnk respectively represent that in the nth time interval, the kth ground sensor transmits data to the mth UAV for one time. The ratio of the time interval and the corresponding transmit power need to meet the following constraints:

in

Constraint (4) means that each ground sensor can only communicate with one UAV at most in any time interval. Assuming that the channel between any UAV and any ground sensor is a LoS link, the mth UAV communicates with The channel power gain between the kth ground sensor is:

where ξ is the channel power gain when the relative distance is 1 meter, the kth ground sensor needs to upload B _k bits of data in N time intervals, and the available energy of the kth ground sensor is E _k joules, which needs to satisfy the following Restrictions:

in

σ ² represents the power spectral density of white Gaussian noise in watts per Hertz; The path planning and wireless communication mechanism optimization model for UAV swarm information collection includes:

in

and

2. method according to claim 1, is characterized in that, described by the algorithm based on dichotomy to solve the path planning of described UAV swarm information collection and the total UAV flight time in the wireless communication mechanism optimization model Minimize the problem to get the optimal number of UAV flight time intervals N, including: First determine the upper bound N _max and the lower bound N _min of the number of UAV flight time intervals, and then let

If N is feasible, let N _max =N, otherwise N _min =N, and repeat the process

Judging the feasibility, and so on, the optimal N is finally obtained, and the feasibility of a given N is determined by the positive or negative of the introduced auxiliary variable Δ, that is, if Δ≤0, the N at this time is feasible, otherwise N is not feasible. 3. The method according to claim 1 or 2, characterized in that, when a certain N is given, the feasibility of N is judged by introducing an auxiliary variable Δ, and at the same time, the path of the UAV swarm information collection is used. The planning and wireless communication mechanism optimization model solves the optimal UAV flight trajectory, the scheduling strategy of UAV and ground sensors, and the corresponding ground sensor transmission time and transmission power through an algorithm based on the continuous convex approximation method, including: Given a certain N, the original problem (8) can be rewritten as

s.t. (8b)-(8f), (9b)

The introduction of the auxiliary variable Δ ensures that the problem (9) must have a solution, and the feasibility of a given N can be judged by the positive or negative of Δ in the optimization result; where log is the natural logarithm, and B _k is the kth sensor that needs to be transmitted the total amount of data,

The non-convex constraint (8e) in the path planning and wireless communication mechanism optimization model of the UAV swarm information collection, namely

Approximate convex constraint

And add a penalty term to the objective function, so that the objective function becomes

in

is a diagonal weight matrix, and for any n, the diagonal elements of k at the ith iteration are

α is the penalty term

weight value; definition

Rewrite the constraint (8f) in the trajectory optimization model of the UAV swarm information collection as:

Correspondingly, the constraint condition (9c) in the trajectory optimization model for the collection of UAV swarm information is rewritten as:

(13a) is a non-convex constraint,

is a convex function with respect to (e _mnk , d _mnk ), which can be given by

Approximate the first-order Taylor expansion, then we have

in

Then (13a) is approximated as:

is a convex constraint; After approximation by the convex constraints (10), (11), (12), (13b), and (15), the original problem becomes a convex problem, and the convex optimization tool is used to solve the convex problem. The result is used to update the parameters of the next iteration until the iterative calculation converges, and the optimal UAV flight trajectory {q _mn }, the UAV and the ground sensor scheduling strategy and the corresponding under a given N are obtained. The ground sensor transmission time {τ _mnk }×T _s and transmission power {p _mnk }, the scheduling strategy of the ground sensor includes whether {τ _mnk } is 0 specified in the nth time interval, the mth unmanned Whether the machine communicates with the kth sensor.

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