CN119781493A - Method and system for optimizing real-time data collection and transmission trajectory of multiple UAVs - Google Patents
Method and system for optimizing real-time data collection and transmission trajectory of multiple UAVs Download PDFInfo
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Abstract
本发明公开一种多无人机协作实时数据采集与传输轨迹优化方法及系统，方法包括：步骤1：针对大规模无线传感器网络数据采集和传输场景，建立多无人机协作的实时数据采集和传输的系统模型，构建完成时间最小化问题；步骤2：对传感器节点进行聚类，确定聚类中心为数据采集点的位置，然后根据无人机与基站、无人机与无人机之间的通信半径对任务区域进行划分，位于相同区域的采集点由同一架无人机负责；步骤3：通过轨迹离散化将步骤1中所构建的优化问题转化为关于多无人机悬停点、悬停时间、飞行速度和采集顺序的新优化问题；步骤4：对于步骤3中转化后的新优化问题，通过基于点匹配的轨迹规划算法进行求解，得到多无人机的飞行轨迹。
The invention discloses a method and system for optimizing the trajectory of multi-UAV collaborative real-time data collection and transmission. The method comprises: step 1: for large-scale wireless sensor network data collection and transmission scenarios, a system model of multi-UAV collaborative real-time data collection and transmission is established to construct a completion time minimization problem; step 2: clustering sensor nodes, determining the location of the cluster center as a data collection point, and then dividing the task area according to the communication radius between the UAV and the base station and between the UAVs, and the collection points located in the same area are under the responsibility of the same UAV; step 3: converting the optimization problem constructed in step 1 into a new optimization problem about the hovering points, hovering time, flight speed and collection sequence of multiple UAVs through trajectory discretization; step 4: solving the new optimization problem converted in step 3 through a trajectory planning algorithm based on point matching to obtain the flight trajectory of the multiple UAVs.
Description
Technical Field
The invention belongs to the fields of sensor network data acquisition and unmanned aerial vehicle track planning, and particularly relates to a multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission track optimization method and system in a large-scale sensor network.
Background
Various potential applications in the existing network and the future 6G network depend on real-time acquisition and transmission of perception data to realize quick and accurate response, so that the era of everything intelligent association is promoted. However, conventional terrestrial communication networks present challenges in providing ubiquitous wireless services to ever-increasing-size sensor networks, especially where there are few or no base stations. Under such circumstances, driven by high mobility and flexibility, unmanned aerial vehicles play an increasingly important role in communication networks, being widely used in various scenarios such as environmental monitoring, search and rescue, air reconnaissance, etc., by providing enhanced communication connections, wide wireless coverage and strong perceptibility. The unmanned aerial vehicle can be used as an air base station to establish high-quality wireless connection between the sensor node and a ground base station, so that sensing data of a task area are collected in real time and transmitted back to the base station, and quick task response is realized.
Limited service range and energy of a single unmanned aerial vehicle, the efficiency is often poor when facing the task of data acquisition and transmission of a large-scale sensor network, and therefore, the task is jointly executed through the cooperation of a plurality of unmanned aerial vehicles to be a more efficient solution. In practical application, it is very important to maintain the connection between the cooperation network of multiple unmanned aerial vehicles and the connection between unmanned aerial vehicle and base station, on one hand, the maintenance of communication connection can ensure that the perception data can be transmitted back to the base station in real time so as to make more accurate and quick response, on the other hand, good communication link can also enable the base station to track the state of unmanned aerial vehicle and task execution, ensure the real-time safety control of unmanned aerial vehicle, and enable team cooperation of unmanned aerial vehicle. Therefore, in order to ensure seamless communication service in a task area, the unmanned aerial vehicle not only bears the task of data acquisition but also bears the task of data relay, the task completion efficiency is improved through reasonable acquisition task allocation, and the real-time performance of data transmission is ensured through the maintenance of a cooperative return link.
Disclosure of Invention
Aiming at the requirements of real-time data acquisition and transmission of a large-scale wireless sensor network, the invention provides a multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission track optimization method and system, the data acquisition of a sensor is carried out through cooperation of a plurality of unmanned aerial vehicles, and a return link between the unmanned aerial vehicle and a ground base station is maintained in the whole acquisition process, so that the acquired data can be ensured to be returned to the ground base station in real time for subsequent data processing.
According to an aspect of the present disclosure, a method for optimizing a multi-unmanned plane collaborative real-time data acquisition and transmission track is provided, including:
Step 1, aiming at a large-scale wireless sensor network data acquisition and transmission scene, a system model of real-time data acquisition and transmission of multi-unmanned aerial vehicle cooperation is established, and the problem of minimizing the time for completion is solved;
step 2, clustering the sensor nodes, determining the position of a clustering center as a data acquisition point, and dividing a task area according to the communication radius between the unmanned aerial vehicle and a base station and between the unmanned aerial vehicle and the unmanned aerial vehicle, wherein the acquisition points in the same area are responsible for the same unmanned aerial vehicle;
step 3, converting the optimization problem constructed in the step 1 into a new optimization problem about a multi-unmanned aerial vehicle hovering point, hovering time, flying speed and acquisition sequence through track discretization;
And 4, solving the new optimization problem converted in the step 3 through a track planning algorithm based on point matching to obtain the flight track of the multi-unmanned aerial vehicle.
As a further technical solution, the problem of minimizing the completion time in the step1 is:
Wherein T represents the completion time, T is one of the objective function and the optimization variable, a m,k represents the association relation between the acquisition point k and the unmanned aerial vehicle m, q m (T) represents the position of the unmanned aerial vehicle m at the time T, lambda m,k (T) represents whether the unmanned aerial vehicle m is located at the acquisition point k at the time T, z ij (T) represents whether the node i is connected with the node j at the time T, and the node represents the unmanned aerial vehicle or the base station.
As a further technical solution, the new optimization problem after the conversion in the step 3 is as follows:
wherein T represents the completion time and wherein, Represents the hover position of the drone m at the time of acquiring the kth acquisition point,The suspension time of the unmanned plane m when the kth acquisition point is acquired is represented, and I represents the acquisition sequence of the acquisition points.
As a further technical scheme, the track planning algorithm based on the point matching in the step 4 comprises connectable acquisition point set mapping, acquisition point pair matching, generation of navigation points corresponding to unconnected acquisition points and track planning.
According to an aspect of the present disclosure, there is provided a multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission trajectory optimization system, including:
The first processing module is used for establishing a system model of real-time data acquisition and transmission of multi-unmanned aerial vehicle cooperation aiming at a large-scale wireless sensor network data acquisition and transmission scene, and establishing a problem of minimizing the completion time;
the second processing module is used for clustering the sensor nodes, determining the position of a clustering center as a data acquisition point, dividing a task area according to the communication radius between the unmanned aerial vehicle and the base station and between the unmanned aerial vehicle and the unmanned aerial vehicle, and enabling the acquisition points in the same area to be in charge of the same unmanned aerial vehicle;
The third processing module is used for converting the optimization problem constructed in the first processing module into a new optimization problem about the hovering point, hovering time, flying speed and acquisition sequence of the multiple unmanned aerial vehicles through track discretization;
and the fourth processing module is used for solving the new optimization problem converted in the third processing module through a track planning algorithm based on point matching to obtain the flight track of the multiple unmanned aerial vehicles.
According to an aspect of the present disclosure, a multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission track optimization apparatus is provided, which includes a memory and a processor, where the memory stores program instructions executed by the processor, and the processor invokes the program instructions to execute the steps of the multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission track optimization method.
According to an aspect of the present description, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the steps of the multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission trajectory optimization method.
Compared with the prior art, the invention has the beneficial effects that:
The invention provides a method for optimizing the cooperation real-time data acquisition and transmission track of a plurality of unmanned aerial vehicles in a large-scale wireless sensor network according to the requirements of real-time data acquisition and transmission of the large-scale wireless sensor network. In order to improve the execution efficiency of tasks, the sensor nodes are clustered to determine the positions of the acquisition points, then task areas are divided according to the communication radiuses between the unmanned aerial vehicle and the base station and between the unmanned aerial vehicle and the unmanned aerial vehicle, and the acquisition points in the same area are responsible for the same unmanned aerial vehicle. The invention provides a track planning algorithm based on point matching, which comprises four steps of collection mapping of connectable acquisition points, acquisition point pair matching, navigation point generation corresponding to unconnected acquisition points and track planning, and is used for optimizing the collaborative track of a plurality of unmanned aerial vehicles. The invention finally reduces the total task completion time and improves the task execution efficiency.
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For a clearer description of embodiments of the invention or of solutions according to the prior art, a brief description will be given below of the drawings used in the description of the embodiments or of the prior art, it being obvious that the drawings in the description below are some embodiments of the invention, and that other drawings are obtained from them without the aid of inventive labour for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for optimizing a multi-unmanned plane collaborative real-time data acquisition and transmission track according to an embodiment of the invention.
Fig. 2 is a schematic diagram of collaborative real-time data acquisition and transmission of multiple unmanned aerial vehicles in a large-scale sensor network according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a data acquisition point location and a region division result according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of an optimized multi-unmanned aerial vehicle flight trajectory according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a multi-unmanned plane collaborative real-time data acquisition and transmission track optimization system provided by an embodiment of the invention.
Detailed Description
The terms "comprises" and "comprising," along with any variations thereof, in the description and claims of the invention and in the foregoing drawings, are intended to cover non-exclusive inclusion, such as a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. In addition, the technical features of each embodiment or the single embodiment provided by the invention are combined with each other at will to form a new technical scheme, and the combination is not limited by the sequence of steps and/or the structural composition mode, but is necessarily based on the fact that the technical scheme can be realized by one of ordinary skill in the art, and when the technical scheme combination is contradictory or can not be realized, the technical scheme combination is considered to be absent and is not within the protection scope of the invention claimed.
The embodiment of the invention provides a method for optimizing a multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission track in a large-scale sensor network, which comprises the following specific steps:
Step 1, establishing a system model of real-time data acquisition and transmission of multi-unmanned aerial vehicle cooperation aiming at a large-scale wireless sensor network data acquisition and transmission scene, and establishing a problem of minimum completion time.
Step 1.1, firstly constructing a system model.
Let the task area have N sensor nodes in total, represented by the set S= { S n, 1. Ltoreq.n. Ltoreq.N }, the coordinates of each sensor node areThe total M unmanned aerial vehicles are dispatched to the task area to execute data acquisition and transmission tasks, the flying height of the unmanned aerial vehicle is H, and the unmanned aerial vehicle is assembled byThe coordinates of the unmanned plane m at time t are shown asThe coordinates of the base station are
The sensor nodes in the task area can be divided into K sensor node clusters which are not overlapped with each other and can be expressed asWherein the method comprises the steps ofEach sensor node cluster corresponds to one data acquisition point, the unmanned aerial vehicle performs data acquisition on nodes in the sensor node clusters through hovering at the data acquisition points, K sensor node clusters correspond to K data acquisition points, the K sensor node clusters are represented by a set C= { C k, 1-K is less than or equal to K, and the coordinate of each data acquisition point is p k.
The unmanned aerial vehicle collects data of sensor nodes through frequency division multiple access at a data collection point, and in order to collect data of all the sensor nodes, the shortest hovering time of the unmanned aerial vehicle at a data collection point k is as follows:
Wherein Q n represents the amount of data that needs to be uploaded by the sensor node s n, B represents the communication bandwidth of the unmanned aerial vehicle, w n represents the position of the sensor node s n, p k represents the position of the data acquisition point k, and γ G2U(||wn-pk |) represents the signal-to-noise ratio of the channel between the unmanned aerial vehicle and the sensor node s n when the unmanned aerial vehicle is at the data acquisition point k.
And 1.2, constructing a problem of minimizing the completion time based on the model.
Where T represents the completion time, i.e. the total time it takes to complete the data acquisition and transmission of all nodes, T is both the objective function and one of the optimization variables. Other optimization variables include a m,k representing the association of the acquisition point k with the unmanned aerial vehicle m, q m (t) representing the position of the unmanned aerial vehicle m at time t, λ m,k (t) representing whether the unmanned aerial vehicle m is at the acquisition point k at time t, λ m,k (t) =1 representing that the unmanned aerial vehicle m is at the acquisition point k at time t, λ m,k (t) =0 representing that the unmanned aerial vehicle m is not at the acquisition point k at time t, z ij (t) representing whether the node i is connected with the node j at time t, z ij (t) =1 representing that the node i is connected with the node j at time t, and z ij (t) =0 representing that the node i is not connected with the node j at time t, the node representing either the unmanned aerial vehicle or the base station.
The constraint condition of the association relation between the acquisition point and the unmanned aerial vehicle is specifically as follows:
Wherein a m,k represents an association relationship between the acquisition point k and the unmanned aerial vehicle m, a m,k =1 represents that the acquisition point k is responsible for acquisition by the unmanned aerial vehicle m, and a m,k =0 represents that the acquisition point k is not responsible for acquisition by the unmanned aerial vehicle m.
The constraint conditions of the acquisition time schedule are as follows:
λm,k(t)≤am,k,m∈[1,M],k∈[1,K],t∈[0,T]
-∈-φ(1-λm,k(t))≤||qm(t)-pk||≤∈+φ(1-λm,k(t)),m∈[1,M],k∈[1,K],t∈[0,T]
Wherein λ m,k (t) represents whether the unmanned plane m is located at the collection point k at the time t, λ m,k (t) =1 represents that the unmanned plane m is located at the collection point k at the time t, λ m,k (t) =0 represents that the unmanned plane m is not located at the collection point k at the time t, a m,k represents the association relationship between the collection point k and the unmanned plane m, q m (t) represents the position of the unmanned plane m at the time t, p k represents the position of the collection point k, e represents an arbitrarily small constant, and Φ represents a sufficiently large constant.
The constraint conditions of the unmanned aerial vehicle acquisition time are as follows:
Wherein lambda m,k (t) indicates whether or not the unmanned aerial vehicle m is located at the acquisition point k at the time t, lambda m,k (t) =1 indicates that the unmanned aerial vehicle m is located at the acquisition point k at the time t, lambda m,k (t) =0 indicates that the unmanned aerial vehicle m is not located at the acquisition point k at the time t, Indicating the minimum time that the drone needs to hover at acquisition point k.
Constraint conditions of a backhaul link between the unmanned aerial vehicle and the base station are specifically as follows:
wherein z ij (t) denotes whether node i is connected to node j at time t, z ij (t) =1 denotes that node i is connected to node j at time t, z ij (t) =0 denotes that node i is not connected to node j at time t, node denotes a drone or a base station, d ij (t) denotes the distance between node i and node j at time t, Representing the maximum communication distance between node i and node j that can be communicatively coupled, q i (t) and q j (t) represent the positions of drone i and drone j at time t,Represents the set of the positions of all unmanned aerial vehicles and the positions of the base stations at the time t,And represents a subset of the set of all unmanned aerial vehicle positions at time t, and q 0 represents the position of the base station.
The constraint conditions of the unmanned aerial vehicle speed are as follows:
Wherein, The speed of the unmanned plane m at time t is represented, and V max represents the maximum speed of the unmanned plane.
Constraint conditions for keeping safe distance between unmanned aerial vehicles are as follows:
‖qi(t)-qj(t)‖≥Ds,i,j∈[1,M],i≠j,t∈[0,T]
wherein q i (t) and q j (t) respectively represent positions of the unmanned aerial vehicle i and the unmanned aerial vehicle j at the time t, and D s represents a minimum safe distance between the unmanned aerial vehicles.
The constraint conditions of the starting point and the ending point of the unmanned aerial vehicle are as follows:
qm(0)＝qm(T),m∈[1,M]
Where q m (0) represents the start position of unmanned plane m, and q m (T) represents the end position of unmanned plane m.
The constraint condition of the association relation between the acquisition points and the unmanned aerial vehicle indicates that each acquisition point can be associated with only one unmanned aerial vehicle.
The constraint condition of the acquisition time schedule represents the position where the unmanned aerial vehicle must hover at the acquisition point when acquiring data.
The constraint condition of the unmanned aerial vehicle acquisition time indicates that the hovering time of the unmanned aerial vehicle when acquiring data is at leastThe data acquisition at acquisition point k can be completed.
The constraint condition of the backhaul link between the unmanned aerial vehicle and the base station indicates that when at least M communication links exist between the unmanned aerial vehicle and the base station and no loop exists, each unmanned aerial vehicle can be guaranteed to have at least one direct or indirect link connected with the base station.
The constraint condition of the unmanned aerial vehicle speed indicates that the unmanned aerial vehicle speed does not exceed the maximum flying speed.
The constraint condition of keeping the safety distance between the unmanned aerial vehicles indicates that the distance between the unmanned aerial vehicles in the process of executing tasks is not smaller than the minimum safety distance.
The constraint conditions of the starting point and the ending point of the unmanned aerial vehicle represent that the unmanned aerial vehicle returns to the starting point position after completing the data acquisition and transmission tasks.
And 2, clustering the sensor nodes, determining the positions of the clustering centers as data acquisition points, and dividing task areas according to the communication radius between the unmanned aerial vehicle and the base station and between the unmanned aerial vehicle and the unmanned aerial vehicle, wherein the acquisition points in the same area are responsible for the same unmanned aerial vehicle.
Step 2.1, sensor node clustering and data acquisition point position determination;
Step 2.1.1 initializing the cluster number to be N th is the maximum number of sensor nodes which can be simultaneously serviced by one unmanned aerial vehicle, and r G2U is the ground coverage radius of the unmanned aerial vehicle;
step 2.1.2, inputting the positions of the sensor nodes, clustering the number, running a K-means++ algorithm to obtain a clustering result S k, and calculating the clustering center of each sensor node cluster:
step 2.1.3, calculating the maximum distance between the sensor nodes in all the node clusters and the cluster center:
Step 2.1.4, calculating the maximum number of sensor nodes in all the node clusters, namely N max＝max1≤k≤K|Sk I;
Step 2.1.5, if d max＞rG2U or N max＞Nth is carried out, updating the cluster number K=K+1, and returning to step 2.1.2, otherwise, ending the clustering;
Step 2.2, task area division;
In order to meet the data acquisition of all nodes in a task area, the number of unmanned aerial vehicles used is as follows:
Wherein r U2B represents a communication radius between the unmanned aerial vehicle and the base station, and r U2U represents a communication radius between the unmanned aerial vehicle and the unmanned aerial vehicle.
In order to guarantee that unmanned aerial vehicle still can be connected to the basic station under worst case, divide the task area according to the communication radius between unmanned aerial vehicle and basic station, unmanned aerial vehicle and unmanned aerial vehicle, wherein the regional scope that first unmanned aerial vehicle is responsible for is: The area range that the mth unmanned aerial vehicle is responsible for is: (x B,yB) represents the coordinates of the base station. And the data acquisition points in the mth area are distributed to the mth unmanned aerial vehicle to be responsible for acquisition.
And 3, converting the optimization problem constructed in the step 1 into a new optimization problem about the hovering point, hovering time, flying speed and acquisition sequence of the multiple unmanned aerial vehicles through track discretization.
Discretizing the track of each unmanned aerial vehicle into K waypoints, which are expressed as Representing the hover position of the drone m when the sensor node cluster k is acquired. I= (I 1,I2,...,IK) represents the acquisition order of K sensor node clusters, therefore, the flight time of unmanned aerial vehicle m is:
Wherein, Representing the hover position of drone m when the kth sensor node cluster is acquired,Representing unmanned plane m slaveTo the point ofIs a flying speed of the vehicle.
The hover time of unmanned plane m is:
Wherein, Representing that unmanned plane m is inHover time at.
Thus, the new optimization problem after transformation is:
where T represents the completion time, i.e., the total time it takes to complete data acquisition and transmission for all nodes. The optimization variables are: Represents the hover position of the drone m at the time of acquiring the kth acquisition point, The suspension time of the unmanned plane m when the kth acquisition point is acquired is represented, and I represents the acquisition sequence of the acquisition points.
Constraint conditions of the unmanned aerial vehicle hovering point are specifically as follows:
Where q 0 denotes the location of the base station, Representing the hover position of the unmanned aerial vehicle m when the kth acquisition point is acquired, r U2B representing the communication radius between the unmanned aerial vehicle and the base station, and r U2U representing the communication radius between the unmanned aerial vehicle and the unmanned aerial vehicle.
The constraint conditions of the unmanned aerial vehicle speed are as follows:
Wherein, Represents the hover position of the drone m at the time of acquiring the kth acquisition point,Representing unmanned plane m slaveTo the point ofV max represents the maximum speed of the drone.
The constraint conditions for the unmanned aerial vehicle to collect data are specifically as follows:
Wherein, Representing an intersection of a hover position of the drone with a data acquisition point position when acquiring a kth sensor node cluster,Representing that unmanned plane m is inThe hover time at which the user is hovering,Representing the shortest hover time when the drone collects the kth sensor node cluster,An index set representing the collection points in P Ik.
Constraint conditions of relation between hovering points and collecting points of the unmanned aerial vehicle are as follows:
Wherein, Represents the hover position of the unmanned plane m when the kth acquisition point is acquired, p Ik represents the position of the kth acquisition point,The association relation between the kth acquisition point and the unmanned plane m is represented, epsilon represents an arbitrarily small constant, and phi represents a sufficiently large constant.
The constraint condition of the safety distance between unmanned aerial vehicles is as follows:
Wherein, Representing the hover position of drone m at the time of acquisition of the kth acquisition point, D s represents the minimum safe distance between drones.
The constraints of the unmanned aerial vehicle hover point represent that each unmanned aerial vehicle is in an assigned area during execution of a task.
The constraint condition of the unmanned aerial vehicle speed indicates that the flight time of all unmanned aerial vehicles from the previous hovering point to the next hovering point is the same, and the unmanned aerial vehicle speed does not exceed the maximum flight speed.
The constraint condition of the unmanned aerial vehicle for collecting data indicates that at least one unmanned aerial vehicle is located at a collection point, and the total hovering time of all unmanned aerial vehicles at a kth collection point is not less than the shortest hovering time.
The constraint condition of the relation between the hovering point and the collecting point of the unmanned aerial vehicle indicates that the unmanned aerial vehicle should hover at the position of the kth collecting point when collecting the kth collecting point.
The distance between the unmanned aerial vehicles should be not less than the minimum safe distance under the constraint condition of the safe distance between the unmanned aerial vehicles.
And 4, solving the new optimization problem converted in the step 3 through a track planning algorithm based on point matching to obtain the flight track of the multi-unmanned aerial vehicle.
Step 4.1, firstly calculating a tourist path formed by acquisition points of each area, wherein the area with the longest tourist path is marked as m a＝ma';
Step 4.2, calculating a connectable collection of acquisition points of the region m a -1:
Wherein, Indicating that acquisition point k 1 within region m a -1 may be integrated with the connectable acquisition points within region m a,Acquisition point k 2 representing region m a,Representing the location of acquisition point k 1 within region m a -1,Representing the position of the acquisition point k 2 within the region m a, r U2U representing the radius of communication between the drones,Representing the collection of acquisition points within region m a.
Step 4.3, matching acquisition points in the region m a and the region m a -1, wherein the matched acquisition point pairs in the two regions meet the following conditions:
Wherein, Representing the shortest hover time for acquisition point k 1 within region m a -1,Representing the shortest hover time for acquisition point k' 1 within region m a that matches acquisition point k 1 within region m a -1;
wherein the method comprises the steps of AndIndicating the location of acquisition point k 1 and acquisition point k 2 within region m a -1,Representing the traveller's path from acquisition point k ' 1 to acquisition point k ' 2 within region m a -1 where acquisition point k 1 and acquisition point k 2 match within region m a.
Step 4.4, calculating that the acquisition points which cannot be connected to the region m a in the region m a -1 correspond to the optimal waypoints in the region m a:
Wherein, Representing the position of the optimal waypoint, X represents the sum of the abscissas of the acquisition points before and after the inserted waypoint within region m a, Y represents the sum of the ordinates of the acquisition points before and after the inserted waypoint, X 0,y0 represents the abscissa of the acquisition point within region m a -1 that is not connectable to region m a, and r U2U represents the radius of communication between unmanned aerial vehicles.
Step 4.5, inserting the newly added waypoints into the travel paths in the area;
Step 4.6, repeating the steps 4.2 to 4.5 for the region m a' +1;
Step 4.7 update M a＝ma-1,ma'＝ma '+1, return to step 4.2 until M a =1 and M a' =m.
FIG. 1 shows a schematic flow chart of a method according to an embodiment of the invention. According to the method, the data of the sensor are collected through cooperation of the unmanned aerial vehicles, and meanwhile, a return link between the unmanned aerial vehicle and the ground base station is maintained in the whole collection process, so that collected data can be guaranteed to be returned to the ground base station in real time for subsequent data processing.
The parameter setting is that the number of sensor nodes is 1000, the sensor nodes are distributed in an area of 8km multiplied by 8km, the positions of base stations are (0 m,0 m), the height of the base stations is 20m, the flying height H of the unmanned aerial vehicle is 100m, and the maximum speed V max of the unmanned aerial vehicle is 30m/s. The communication bandwidth B is 2MHz, the node transmitting power is 0.05W, the unmanned aerial vehicle transmitting power is 0.1W, the noise power is-110 dBm, the communication radius r U2U between unmanned aerial vehicles is 3979m, the communication radius r U2B between unmanned aerial vehicles and a base station is 4059m, the ground covering radius r G2U of unmanned aerial vehicles is 1513m, the minimum safe distance D s between unmanned aerial vehicles is 30m, the maximum number of sensor nodes N th which can be simultaneously served by unmanned aerial vehicles is 60, and the data quantity Q of the sensor nodes is 10Mbits.
As a specific implementation manner, the collaborative real-time data collection and transmission of multiple unmanned aerial vehicles in the large-scale sensor network is shown in fig. 2. The method of the embodiment of the invention specifically comprises the following steps:
step 1, according to the parameter setting, substituting the parameter setting into a system model to construct a problem of minimizing the completion time;
And 2, clustering the sensor nodes, determining the positions of the clustering centers as data acquisition points, and dividing task areas according to the communication radius between the unmanned aerial vehicle and the base station and between the unmanned aerial vehicle and the unmanned aerial vehicle, wherein the acquisition points in the same area are responsible for the same unmanned aerial vehicle. The data acquisition point location and region division results are shown in fig. 3.
Step 3, converting the optimization problem constructed in the step 1 into a new optimization problem about a multi-unmanned aerial vehicle hovering point, hovering time, flying speed and acquisition sequence through track discretization;
And 4, solving the new optimization problem converted in the step 3 through a track planning algorithm based on point matching to obtain the flight track of the multi-unmanned aerial vehicle. The optimized multi-unmanned aerial vehicle flight trajectory is shown in fig. 4.
The implementation basis of the embodiments of the present invention is realized by a device with a processor function to perform programmed processing. Therefore, in engineering practice, the technical solutions and the functions of the embodiments of the present invention are packaged into various modules. Based on the actual situation, on the basis of the above embodiments, the embodiment of the invention provides a multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission track optimization system, which is used for executing the multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission track optimization method in the method embodiment.
The system comprises a first processing module, a second processing module, a third processing module and a fourth processing module, wherein the first processing module is used for establishing a system model of real-time data acquisition and transmission of multi-unmanned aerial vehicle cooperation aiming at a large-scale wireless sensor network data acquisition and transmission scene, the second processing module is used for clustering sensor nodes, determining the position of a clustering center for data acquisition points, dividing task areas according to the communication radius between an unmanned aerial vehicle and a base station and between the unmanned aerial vehicle and the unmanned aerial vehicle, the acquisition points in the same area are responsible for the same unmanned aerial vehicle, the third processing module is used for converting the optimization problem established in the first processing module into a new optimization problem about hovering points, hovering time, flying speed and acquisition sequence of the multi-unmanned aerial vehicle through track discretization, and the fourth processing module is used for solving the new optimization problem converted in the third processing module through a track planning algorithm based on point matching, so that the flying track of the multi-unmanned aerial vehicle is obtained.
The multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission track optimization system provided by the embodiment of the invention is oriented to the requirements of real-time data acquisition and transmission of a large-scale wireless sensor network, adopts a plurality of modules in fig. 5, performs data acquisition of a sensor through cooperation of a plurality of unmanned aerial vehicles, and simultaneously maintains a return link between the unmanned aerial vehicle and a ground base station in the whole acquisition process, thereby ensuring that acquired data can be returned to the ground base station in real time for subsequent data processing.
It should be noted that, in addition to the method used for implementing the above method embodiment, the system embodiment provided by the present invention is also used for implementing the method in other method embodiments provided by the present invention, which is different only in that corresponding functional modules are provided, the principle of which is basically the same as that of the above system embodiment provided by the present invention, so long as a person skilled in the art obtains corresponding technical means by combining technical features with reference to specific technical solutions in other method embodiments on the basis of the above system embodiment, and the technical solutions formed by these technical means, and improves the modules in the above system embodiment to obtain corresponding system class embodiments on the premise of ensuring that the technical solutions have practicability, so as to implement the methods in other method class embodiments.
Based on the same inventive concept as the above embodiment, the embodiment of the present invention further provides a multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission track optimization device, which includes a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor invokes the program instructions to execute the steps of the multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission track optimization method.
In the embodiment of the present invention, the memory may be a nonvolatile memory, such as a hard disk (HARD DISK DRIVE, HDD) or a solid-state disk (SSD), or may be a volatile memory (RAM). The memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory in embodiments of the present invention may also be circuitry or any other device capable of performing memory functions for storing program instructions and/or data.
In the embodiment of the present invention, the processor may be a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps and logic blocks disclosed in the embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution.
Based on the same inventive concept as the above embodiments, the embodiments of the present invention further provide a non-transitory computer readable storage medium storing computer instructions that cause the computer to execute the steps of the multi-unmanned aerial vehicle collaborative real-time data acquisition and transmission trajectory optimization method.
In summary, the embodiment of the invention provides a method for optimizing the cooperation real-time data acquisition and transmission track of multiple unmanned aerial vehicles in a large-scale sensor network, wherein the data acquisition of the sensor is performed through the cooperation of the multiple unmanned aerial vehicles, and meanwhile, a return link between the unmanned aerial vehicle and a ground base station is maintained in the whole acquisition process, so that the acquired data can be ensured to be returned to the ground base station in real time for subsequent data processing. In order to improve the execution efficiency of tasks, sensor nodes are clustered to determine the positions of acquisition points, task areas are divided according to communication radiuses among unmanned aerial vehicles, base stations and unmanned aerial vehicles, the acquisition points in the same areas are responsible for the same unmanned aerial vehicle, and then the invention provides a track planning algorithm based on point matching. And finally, the total task completion time is reduced, and the task execution efficiency is improved.
It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some or all of the technical features may be equivalently replaced, and these modifications or substitutions do not deviate from the essence of the corresponding technical solution from the technical solution of the embodiment of the present invention.
Claims (7)
1. The multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission track optimization method is characterized by comprising the following steps of:
Step 1, aiming at a large-scale wireless sensor network data acquisition and transmission scene, a system model of real-time data acquisition and transmission of multi-unmanned aerial vehicle cooperation is established, and the problem of minimizing the time for completion is solved;
step 2, clustering the sensor nodes, determining the position of a clustering center as a data acquisition point, and dividing a task area according to the communication radius between the unmanned aerial vehicle and a base station and between the unmanned aerial vehicle and the unmanned aerial vehicle, wherein the acquisition points in the same area are responsible for the same unmanned aerial vehicle;
step 3, converting the optimization problem constructed in the step 1 into a new optimization problem about a multi-unmanned aerial vehicle hovering point, hovering time, flying speed and acquisition sequence through track discretization;
And 4, solving the new optimization problem converted in the step 3 through a track planning algorithm based on point matching to obtain the flight track of the multi-unmanned aerial vehicle.
2. The method for optimizing the collaborative real-time data acquisition and transmission trajectory of multiple unmanned aerial vehicles according to claim 1, wherein the problem of minimizing the completion time in step 1 is:
Wherein T represents the completion time, T is one of the objective function and the optimization variable, a m,k represents the association relation between the acquisition point k and the unmanned aerial vehicle m, q m (T) represents the position of the unmanned aerial vehicle m at the time T, lambda m,k (T) represents whether the unmanned aerial vehicle m is located at the acquisition point k at the time T, z ij (T) represents whether the node i is connected with the node j at the time T, and the node represents the unmanned aerial vehicle or the base station.
3. The method for optimizing the collaborative real-time data acquisition and transmission trajectory of multiple unmanned aerial vehicles according to claim 1, wherein the new optimization problem after conversion in step 3 is:
wherein T represents the completion time and wherein, Represents the hover position of the drone m at the time of acquiring the kth acquisition point,The suspension time of the unmanned plane m when the kth acquisition point is acquired is represented, and I represents the acquisition sequence of the acquisition points.
4. The method for optimizing the collaborative real-time data acquisition and transmission trajectory of multiple unmanned aerial vehicles according to claim 1, wherein the trajectory planning algorithm based on point matching in step 4 comprises connectable acquisition point set mapping, acquisition point pair matching, and non-connectable acquisition point corresponding waypoint generation and trajectory planning.
5. Multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission track optimizing system, which is characterized by comprising:
The first processing module is used for establishing a system model of real-time data acquisition and transmission of multi-unmanned aerial vehicle cooperation aiming at a large-scale wireless sensor network data acquisition and transmission scene, and establishing a problem of minimizing the completion time;
the second processing module is used for clustering the sensor nodes, determining the position of a clustering center as a data acquisition point, dividing a task area according to the communication radius between the unmanned aerial vehicle and the base station and between the unmanned aerial vehicle and the unmanned aerial vehicle, and enabling the acquisition points in the same area to be in charge of the same unmanned aerial vehicle;
The third processing module is used for converting the optimization problem constructed in the first processing module into a new optimization problem about the hovering point, hovering time, flying speed and acquisition sequence of the multiple unmanned aerial vehicles through track discretization;
and the fourth processing module is used for solving the new optimization problem converted in the third processing module through a track planning algorithm based on point matching to obtain the flight track of the multiple unmanned aerial vehicles.
6. A multi-unmanned aerial vehicle cooperative real-time data collection and transmission trajectory optimization device comprising a memory and a processor, the memory storing program instructions for execution by the processor, the processor invoking the program instructions to perform the steps of the multi-unmanned aerial vehicle cooperative real-time data collection and transmission trajectory optimization method of any one of claims 1 to 4.
7. A non-transitory computer readable storage medium storing computer instructions that cause the computer to perform the steps of the multi-unmanned cooperative real-time data acquisition and transmission trajectory optimization method of any one of claims 1 to 4.
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