Multi-UAV collaborative real-time data collection and transmission trajectory optimization method and system

Abstract

The present invention discloses a method and system for optimizing the trajectory of real-time data collection and transmission of multiple unmanned aerial vehicles (UAVs) in collaboration. The method comprises the following steps: step 1: for a large-scale wireless sensor network data collection and transmission scenario, establishing a system model for real-time data collection and transmission of multiple UAVs in collaboration, and constructing a completion time minimization problem; step 2: clustering sensor nodes, determining the cluster center as the location of the 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 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 regarding the hovering points, hovering time, flight speed and collection sequence of multiple UAVs through trajectory discretization; and step 4: solving the new optimization problem converted in step 3 by a trajectory planning algorithm based on point matching to obtain the flight trajectories of the multiple UAVs.

Description

Technical Field

[0001] The present invention belongs to the field of sensor network data acquisition and unmanned aerial vehicle (UAV) trajectory planning, and in particular relates to a method and system for real-time data acquisition and transmission trajectory optimization of multiple UAVs in a large-scale sensor network. Background Art

[0002] Various potential applications in existing networks and future 6G networks rely on the real-time collection and transmission of sensor data to achieve rapid and accurate responses, ushering in the era of intelligent connectivity. However, traditional terrestrial communication networks face challenges in providing ubiquitous wireless services for the ever-expanding sensor networks, especially in environments with few or no base stations. In this context, drones, driven by their high mobility and flexibility, are playing an increasingly important role in communication networks. By providing enhanced communication connectivity, extensive wireless coverage, and powerful perception capabilities, they are being widely used in various scenarios such as environmental monitoring, search and rescue, and aerial reconnaissance. UAVs, acting as aerial base stations, can establish high-quality wireless connections between sensor nodes and ground base stations, thereby collecting and transmitting sensor data from the mission area in real time back to the base station, enabling rapid mission response.

[0003] Due to the limited range and energy of individual drones, data collection and transmission tasks within large-scale sensor networks are often inefficient. Therefore, collaborative task execution by multiple drones is a more efficient solution. In practical applications, maintaining connectivity within a multi-drone collaborative network and between drones and base stations is crucial. Maintaining communication links ensures real-time transmission of sensor data back to the base station, enabling more accurate and rapid responses. Furthermore, a robust communication link allows the base station to track the status of drones and mission execution, ensuring real-time and safe control of drones and enabling team collaboration. Therefore, to ensure seamless communication within the mission area, drones undertake both data collection and data relay tasks. This improves task completion efficiency through the proper allocation of collection tasks, while maintaining collaborative backhaul links ensures real-time data transmission. Summary of the Invention

[0004] Aiming at the demand for real-time data collection and transmission in large-scale wireless sensor networks, the present invention provides a method and system for real-time data collection and transmission trajectory optimization of multiple UAVs. Sensor data is collected through the collaboration of multiple UAVs, and the backhaul link between the UAVs and the ground base station is maintained throughout the entire collection process, thereby ensuring that the collected data can be transmitted back to the ground base station in real time for subsequent data processing.

[0005] According to one aspect of the present invention, a method for multi-UAV collaborative real-time data collection and transmission trajectory optimization is provided, comprising:

[0006] Step 1: For large-scale wireless sensor network data collection and transmission scenarios, a system model for multi-UAV collaborative real-time data collection and transmission is established to solve the completion time minimization problem.

[0007] Step 2: Cluster the sensor nodes and determine the cluster center as the location of the data collection point. Then, divide the mission area according to the communication radius between the UAV and the base station, and between UAVs. The collection points in the same area are responsible for the same UAV.

[0008] Step 3: Through trajectory discretization, the optimization problem constructed in step 1 is transformed into a new optimization problem regarding the hovering points, hovering time, flight speed and acquisition sequence of multiple UAVs;

[0009] Step 4: For the new optimization problem transformed in step 3, solve it through the trajectory planning algorithm based on point matching to obtain the flight trajectory of multiple UAVs.

[0010] As a further technical solution, the completion time minimization problem in step 1 is:

[0011]

[0012] Among them, T represents the completion time, T is both the objective function and one of the optimization variables, a m,k represents the relationship between the collection point k and the drone m, q m (t) represents the position of UAV m at time t, λ m，k (t) indicates whether the UAV m is located at the collection point k at time t, z ij (t) indicates whether node i is connected to node j at time t, and node represents a drone or a base station.

[0013] As a further technical solution, the new optimization problem after transformation in step 3 is:

[0014]

[0015] Where T represents the completion time, represents the hovering position of the UAV m when collecting the kth collection point, It represents the hovering time of UAV m when collecting the kth collection point, and I represents the collection order of the collection points.

[0016] As a further technical solution, the point matching-based trajectory planning algorithm in step 4 includes: mapping of connectable collection point sets, matching of collection point pairs, generating waypoints corresponding to unconnectable collection points, and trajectory planning.

[0017] According to one aspect of the present invention, a multi-UAV collaborative real-time data collection and transmission trajectory optimization system is provided, comprising:

[0018] The first processing module is used to establish a system model for real-time data collection and transmission of multiple UAVs in a large-scale wireless sensor network data collection and transmission scenario, and to solve the problem of minimizing the completion time.

[0019] The second processing module is used to cluster the sensor nodes and determine the cluster center as the location of the data collection point. The task area is then divided according to the communication radius between the UAV and the base station, and between UAVs. The collection points in the same area are responsible for the same UAV.

[0020] A third processing module is used to transform the optimization problem constructed in the first processing module into a new optimization problem regarding the hovering points, hovering time, flight speed and acquisition sequence of multiple UAVs through trajectory discretization;

[0021] The fourth processing module is used to solve the new optimization problem converted in the third processing module through a trajectory planning algorithm based on point matching to obtain the flight trajectories of multiple UAVs.

[0022] According to one aspect of the present invention, a device for optimizing the collaborative real-time data collection and transmission trajectory of multiple UAVs is provided, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the method for optimizing the collaborative real-time data collection and transmission trajectory of multiple UAVs.

[0023] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the multi-UAV collaborative real-time data collection and transmission trajectory optimization method.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] To address the need for real-time data collection and transmission in large-scale wireless sensor networks, the present invention provides a method for optimizing the collaborative real-time data collection and transmission trajectories of multiple unmanned aerial vehicles (UAVs) in large-scale sensor networks. This method uses multiple UAVs to collaboratively collect sensor data while maintaining a backhaul link between the UAVs and a ground base station throughout the entire collection process, ensuring that the collected data can be transmitted back to the ground base station in real time for subsequent data processing. To improve task execution efficiency, the present invention first clusters sensor nodes to determine the locations of collection points. The task area is then divided based on the communication radius between UAVs and base stations, and between UAVs. Collection points in the same area are assigned to the same UAV. The present invention then proposes a trajectory planning algorithm based on point matching. This algorithm specifically includes four steps: mapping a set of connectable collection points, matching collection point pairs, generating waypoints corresponding to disconnected collection points, and trajectory planning. This algorithm is used to optimize the collaborative trajectories of multiple UAVs. Ultimately, the present invention reduces total task completion time and improves task execution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 Schematic diagram of the process flow of the multi-UAV collaborative real-time data collection and transmission trajectory optimization method provided by an embodiment of the present invention.

[0028] Figure 2 Schematic diagram of multi-UAV collaborative real-time data collection and transmission in a large-scale sensor network provided by an embodiment of the present invention.

[0029] Figure 3 This is a schematic diagram of the data collection point locations and area division results provided by an embodiment of the present invention.

[0030] Figure 4 A schematic diagram of optimized multi-UAV flight trajectories provided by an embodiment of the present invention.

[0031] Figure 5 Schematic diagram of a multi-UAV collaborative real-time data acquisition and transmission trajectory optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0033] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0034] The present invention provides a method for real-time data collection and transmission trajectory optimization of multiple UAVs in a large-scale sensor network, which includes the following specific steps:

[0035] Step 1: For the large-scale wireless sensor network data collection and transmission scenario, a system model for real-time data collection and transmission of multiple UAVs is established to construct the completion time minimization problem.

[0036] Step 1.1: First build a system model.

[0037] Assume that there are N sensor nodes in the task area, and the set S = {s n ,1≤n≤N} means that the coordinates of each sensor node are A total of M drones are dispatched to the mission area to perform data collection and transmission tasks. The flight altitude of the drones is H. The drones are assembled Indicates that the coordinates of UAV m at time t are The coordinates of the base station are

[0038] Assume that the sensor nodes in the task area can be divided into K non-overlapping sensor node clusters, which can be expressed as in Each sensor node cluster corresponds to a data collection point. The drone collects data from the nodes in the sensor node cluster by hovering at the data collection point. K sensor node clusters correspond to K data collection points. The set C = {c k ,1≤k≤K} means that the coordinates of each data collection point are p k .

[0039] The UAV collects data from sensor nodes at the data collection point through frequency division multiple access. In order to collect data from all sensor nodes, the shortest hovering time of the UAV at the data collection point k is:

[0040]

[0041] Among them, Q n Represents sensor node s n The amount of data that needs to be uploaded, B represents the communication bandwidth of the drone, w n Represents sensor node s n The position of p k represents the location of data collection point k, γ G2U (||w n -p k ||) represents the distance between the UAV and the sensor node s when the UAV is at the data collection point k. n The signal-to-noise ratio of the channel.

[0042] Step 1.2: Based on the above model, construct the completion time minimization problem.

[0043]

[0044] Where T represents the completion time, that is, the total time required to complete data collection and transmission of all nodes. T is both the objective function and one of the optimization variables. Other optimization variables include: a m,k represents the relationship between the collection point k and the drone m, q m (t) represents the position of UAV m at time t, λ m，k (t) indicates whether the UAV m is located at the collection point k at time t, λ m，k (t) = 1 means that the UAV m is located at the collection point k at time t, m，k (t) = 0 means that UAV m is not at the collection point k at time t, z ij (t) indicates whether node i is connected to node j at time t, z ij (t) = 1 means that node i is connected to node j at time t, z ij (t) = 0 means that node i is not connected to node j at time t, and the node represents a drone or a base station.

[0045] The constraints on the association between the collection points and the drones are as follows:

[0046]

[0047] Among them, a m,k represents the relationship between the collection point k and the drone m, a m,k =1 means that the collection point k is collected by drone m, a m,k =0 means that the collection point k is not collected by drone m.

[0048] The constraints of the acquisition time scheduling are as follows:

[0049] λ m，k (t)≤a m，k , m∈[1,M],k∈[1,K],t∈[0,T]

[0050] -∈-φ(1-λ m，k (t))≤||q m (t)-p k ||≤∈+φ(1-λ m，k (t)), m∈[1, M], k∈[1, K], t∈[0, T]

[0051] Among them, λ m，k (t) indicates whether the UAV m is located at the collection point k at time t, λ m，k (t) = 1 means that the UAV m is located at the collection point k at time t, m，k (t) = 0 means that the UAV m is not at the collection point k at time t, m，k represents the relationship between the collection point k and the drone m, q m (t) represents the position of UAV m at time t, p k represents the position of the acquisition point k, ∈ represents an arbitrarily small constant, and φ represents a sufficiently large constant.

[0052] The constraints on the drone collection time are as follows:

[0053]

[0054] Among them, λ m，k (t) indicates whether the UAV m is located at the collection point k at time t, λ m，k (t) = 1 means that the UAV m is located at the collection point k at time t, m，k (t) = 0 means that the UAV m is not at the collection point k at time t, It represents the minimum time that the UAV needs to hover at the collection point k.

[0055] The constraints of the backhaul link between the drone and the base station are as follows:

[0056]

[0057] Among them, z ij (t) indicates whether node i is connected to node j at time t, z ij (t) = 1 means that node i is connected to node j at time t, z ij (t) = 0 means that node i is not connected to node j at time t, and node j represents a drone or a base station. ij (t) represents the distance between node i and node j at time t, represents the maximum communication distance between node i and node j, q i (t) and q j (t) represents the position of UAV i and UAV j at time t, represents the set of positions of all drones and base stations at time t, represents a subset of the set of all UAV positions at time t, and q0 represents the position of the base station.

[0058] The constraints on the drone speed are as follows:

[0059]

[0060] in, represents the speed of UAV m at time t, V max Indicates the maximum speed of the drone.

[0061] The constraints for maintaining a safe distance between the drones are as follows:

[0062] ‖q i (t)-q j (t)‖≥D s ,i,j∈[1,M],i≠j,t∈[0,T]

[0063] Among them, q i (t) and q j (t) represents the position of UAV i and UAV j at time t, D s Indicates the minimum safe distance between drones.

[0064] The constraints of the starting point and end point of the drone are as follows:

[0065] q m (0) = q m (T),m∈[1,M]

[0066] Among them, qm (0) represents the starting position of UAV m, q m (T) represents the final position of UAV m.

[0067] The constraint condition of the association relationship between the collection point and the drone indicates that each collection point can only be associated with one drone.

[0068] The constraint condition of the collection time scheduling means that the UAV must hover at the location of the collection point when collecting data.

[0069] The constraint condition of the drone collection time means that the drone must hover for at least Only then can the data collection at the collection point k be completed.

[0070] The constraint condition of the backhaul link between the drone and the base station indicates that only when there are at least M communication links between the drone and the base station and there is no loop can each drone be guaranteed to be connected to the base station by at least one direct or indirect link.

[0071] The constraint condition of the UAV speed indicates that the speed of the UAV does not exceed the maximum flight speed.

[0072] The constraint condition of maintaining a safe distance between the drones means that the distance between the drones is not less than the minimum safe distance when the drones are performing a mission.

[0073] The constraints of the starting point and the end point of the UAV indicate that the UAV returns to the starting point after completing the data collection and transmission tasks.

[0074] Step 2: Cluster the sensor nodes and determine the cluster center as the location of the data collection point. Then, divide the mission area according to the communication radius between the UAV and the base station and between UAVs. The collection points in the same area are responsible for the same UAV.

[0075] Step 2.1: Clustering of sensor nodes and determination of data collection point locations;

[0076] Step 2.1.1: Initialize the number of clusters to N th is the maximum number of sensor nodes that a UAV can serve simultaneously, r G2U The ground coverage radius of the drone;

[0077] Step 2.1.2: Input the sensor node location and the number of clusters, run the K-Means++ algorithm, and get the clustering result S k , calculate the cluster center of each sensor node cluster:

[0078] Step 2.1.3: Calculate the maximum distance between the sensor node and the cluster center in all node clusters:

[0079] Step 2.1.4: Calculate the maximum number of sensor nodes in all node clusters: N max =max 1≤k≤K |S k |;

[0080] Step 2.1.5: If d max >r G2U or N max >N th , then update the number of clusters K = K + 1 and return to step 2.1.2; otherwise, end clustering;

[0081] Step 2.2: Divide the task area;

[0082] To meet the data collection requirements of all nodes in the mission area, the number of drones used is:

[0083]

[0084] Among them, r U2B represents the communication radius between the UAV and the base station, r U2U Indicates the communication radius between drones.

[0085] To ensure that the drone can still connect to the base station in the worst case, the mission area is divided according to the communication radius between the drone and the base station, and between drones. The area responsible for the first drone is: The area covered by the mth drone is: (x B ,y B ) represents the coordinates of the base station. The data collection point located in the mth area is assigned to the mth UAV for collection.

[0086] Step 3: Through trajectory discretization, the optimization problem constructed in step 1 is transformed into a new optimization problem regarding the hovering points, hovering time, flight speed, and acquisition sequence of multiple UAVs.

[0087] The trajectory of each UAV is discretized into K waypoints, expressed as I=(I1,I2,...,I K ) represents the acquisition order of K sensor node clusters, so the flight time of UAV m is:

[0088]

[0089] in, represents the hovering position of UAV m when the kth sensor node cluster is collected, Indicates that the drone m is from arrive flight speed.

[0090] The hovering time of drone m is:

[0091]

[0092] in, Indicates that the drone m is The hovering time at .

[0093] Therefore, the new optimization problem after transformation is:

[0094]

[0095] Where T represents the completion time, that is, the total time required to complete data collection and transmission of all nodes. The optimization variables are: represents the hovering position of the UAV m when collecting the kth collection point, It represents the hovering time of UAV m when collecting the kth collection point, and I represents the collection order of the collection points.

[0096] The constraints of the drone's hovering point are as follows:

[0097]

[0098] Among them, q0 represents the location of the base station, represents the hovering position of the UAV m when collecting the kth collection point, r U2B represents the communication radius between the UAV and the base station, r U2U Indicates the communication radius between drones.

[0099] The constraints on the drone speed are as follows:

[0100]

[0101] in, represents the hovering position of the UAV m when collecting the kth collection point, Indicates that the drone m is from arrive Flight speed, V max Indicates the maximum speed of the drone.

[0102] The constraints for drone data collection are as follows:

[0103]

[0104] in, represents the intersection of the drone’s hovering position and the data collection point position when collecting the kth sensor node cluster, Indicates that the drone m is The hovering time at represents the shortest hovering time when the UAV collects the kth sensor node cluster, Indicates P Ik The index collection of the collection points in .

[0105] The constraints on the relationship between the drone hovering point and the collection point are as follows:

[0106]

[0107] in, represents the hovering position of the UAV m when collecting the kth collection point, p Ik represents the position of the kth collection point, represents the association between the kth collection point and the UAV m, ∈ represents an arbitrarily small constant, and φ represents a sufficiently large constant.

[0108] The constraints of the safe distance between the drones are as follows:

[0109]

[0110] in, represents the hovering position of UAV m when collecting the kth collection point, D s Indicates the minimum safe distance between drones.

[0111] The constraint condition of the drone hovering point means that each drone is in the assigned area during the execution of the mission.

[0112] The constraint condition of the drone speed indicates that the flight time of all drones from their respective last hovering point to the next hovering point is the same, and the speed of the drone does not exceed the maximum flight speed.

[0113] The constraint condition for the drone data collection indicates that at least one drone is located at the collection point, and the total hovering time of all drones at the kth collection point is not less than the shortest hovering time.

[0114] The constraint condition of the relationship between the drone hovering point and the collection point indicates that when the drone collects the kth collection point, it should hover at the position of the kth collection point.

[0115] The constraint condition of the safety distance between the drones is that the distance between the drones should not be less than the minimum safety distance.

[0116] Step 4: For the new optimization problem transformed in step 3, solve it through the trajectory planning algorithm based on point matching to obtain the flight trajectory of multiple UAVs.

[0117] Step 4.1: First calculate the traveling salesman path formed by the collection points in each area, where the area with the longest traveling salesman path is denoted as m a =m a ';

[0118] Step 4.2: Calculate the area m a -1 connectable collection point set:

[0119]

[0120] in, Represents area m a -1 The collection point k1 can be combined with the area m a A collection of connectable collection points within the Represents area m a The collection point k2, Represents area m a -1, the position of the sampling point k1, Represents area m a The position of the inner sampling point k2, r U2U represents the communication radius between UAVs, Represents area m a The collection points within.

[0121] Step 4.3: For region m a and area m a -1, the matching collection points in the two areas meet the following conditions:

[0122]

[0123] in, Represents area m a -1 The shortest hovering time of the acquisition point k1, Represents the region m a -1 The area m where the sampling point k1 matches a The shortest hovering time of the acquisition point k′1 within the

[0124]

[0125] in and Represents area m a -1, the positions of the collection points k1 and k2, Represents area ma -1 The area m that the acquisition point k1 and the acquisition point k2 match a The traveling salesman path from the inner collection point k′1 to the collection point k′2.

[0126] Step 4.4: Calculate the area m a -1 cannot connect to area m a The collection points correspond to area m a The best waypoints within:

[0127]

[0128] in, Indicates the location of the optimal waypoint, X represents the area m a The sum of the horizontal coordinates of the collection points before and after the inserted waypoint, Y represents the sum of the vertical coordinates of the collection points before and after the inserted waypoint, x0, y0 represents the area m a -1 cannot connect to area m a The horizontal and vertical coordinates of the collection point, r U2U Indicates the communication radius between drones.

[0129] Step 4.5: Insert the newly added waypoints into the traveling salesman path within the region;

[0130] Step 4.6: For region m a '+1Repeat steps 4.2 to 4.5;

[0131] Step 4.7: Update m a =m a -1,m a '=m a '+1, return to step 4.2 until m a =1 and m a '=M.

[0132] Figure 1 A schematic flow chart of a method according to an embodiment of the present invention is provided. The method utilizes multiple drones to collaborate to collect sensor data, while maintaining a backhaul link between the drones and a ground base station throughout the entire collection process, thereby ensuring that the collected data can be transmitted back to the ground base station in real time for subsequent data processing.

[0133] Parameter settings: the number of sensor nodes is 1000, distributed in an area of ​​8km×8km, the base station position is (0m, 0m), the base station height is 20m, the UAV flight height H is 100m, and the UAV maximum speed V is 0. max The communication bandwidth B is 2MHz, the node transmission power is 0.05W, the UAV transmission power is 0.1W, the noise power is -110dBm, and the communication radius between UAVs is r U2UThe communication radius between the drone and the base station is r U2B The UAV’s ground coverage radius is 4059m. G2U The minimum safe distance D between drones is 1513m. s The maximum number of sensor nodes N that a drone can serve simultaneously is 30m. th is 60, and the data volume Q of the sensor node is 10Mbits.

[0134] As a specific implementation method, multi-UAV collaborative real-time data collection and transmission in large-scale sensor networks is as follows: Figure 2 The method described in the embodiment of the present invention specifically includes the following steps:

[0135] Step 1: Based on the above parameter settings, substitute the system model and construct the completion time minimization problem;

[0136] Step 2: Cluster the sensor nodes and determine the cluster center as the location of the data collection point. Then divide the mission area according to the communication radius between the UAV and the base station and between UAVs. The collection points in the same area are responsible for the same UAV. The data collection point location and area division results are shown in Figure 2. Figure 3 shown.

[0137] Step 3: Through trajectory discretization, the optimization problem constructed in step 1 is transformed into a new optimization problem regarding the hovering points, hovering time, flight speed and acquisition sequence of multiple UAVs;

[0138] Step 4: For the new optimization problem transformed in step 3, solve it through the trajectory planning algorithm based on point matching to obtain the flight trajectory of multiple UAVs. The optimized flight trajectory of multiple UAVs is as follows: Figure 4 shown.

[0139] The implementation of each embodiment of the present invention is based on programmed processing by a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, and in addition to the aforementioned embodiments, an embodiment of the present invention provides a multi-UAV collaborative real-time data acquisition and transmission trajectory optimization system. This system is used to implement the multi-UAV collaborative real-time data acquisition and transmission trajectory optimization method described in the aforementioned method embodiment.

[0140] See also Figure 5The system includes: a first processing module, which is used to establish a system model for real-time data collection and transmission of multiple UAVs in a large-scale wireless sensor network data collection and transmission scenario, and construct a completion time minimization problem; a second processing module, which is used to cluster sensor nodes, determine the cluster center as the location of the data collection point, and then divide the task area according to the communication radius between the UAV and the base station and between the UAVs. The collection points in the same area are responsible for the same UAV; a third processing module, which is used to transform the optimization problem constructed in the first processing module into a new optimization problem about the hovering points, hovering time, flight speed and collection sequence of multiple UAVs through trajectory discretization; a fourth processing module, which is used to solve the new optimization problem after transformation in the third processing module through a trajectory planning algorithm based on point matching, and obtain the flight trajectory of multiple UAVs.

[0141] The multi-UAV collaborative real-time data collection and transmission trajectory optimization system provided by the embodiment of the present invention is aimed at the needs of real-time data collection and transmission of large-scale wireless sensor networks. Figure 5 Several modules in it collect sensor data through the collaboration of multiple drones. At the same time, the backhaul link between the drone and the ground base station is maintained throughout the entire collection process, thereby ensuring that the collected data can be transmitted back to the ground base station in real time for subsequent data processing.

[0142] It should be noted that the system embodiments provided by the present invention are not only used to implement the methods in the above-mentioned method embodiments, but also used to implement the methods in other method embodiments provided by the present invention. The only difference lies in the setting of corresponding functional modules, and the principles thereof are basically the same as the principles of the above-mentioned system embodiments provided by the present invention. As long as those skilled in the art refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned system embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above-mentioned system embodiments to obtain corresponding system class embodiments for implementing the methods in other method class embodiments.

[0143] Based on the same inventive concept as the above embodiment, an embodiment of the present invention also provides a multi-UAV collaborative real-time data collection and transmission trajectory optimization device, including a memory and a processor, the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the multi-UAV collaborative real-time data collection and transmission trajectory optimization method.

[0144] In an embodiment of the present invention, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory (volatile memory), such as a random-access 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 can be accessed by a computer, but is not limited thereto. The memory in an embodiment of the present invention may also be a circuit or any other device that can implement a storage function, for storing program instructions and / or data.

[0145] In the embodiments 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 block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention may be directly implemented and executed by a hardware processor, or by a combination of hardware and software modules within the processor.

[0146] Based on the same inventive concept as the above embodiment, an embodiment of the present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the steps of the multi-UAV collaborative real-time data collection and transmission trajectory optimization method.

[0147] In summary, the present invention proposes a method for optimizing the real-time data collection and transmission trajectory of multiple UAVs in a large-scale sensor network. The method collects sensor data through the collaboration of multiple UAVs, and maintains the backhaul link between the UAV and the ground base station throughout the entire collection process, thereby ensuring that the collected data can be transmitted back to the ground base station in real time for subsequent data processing. In order to improve the execution efficiency of the task, the sensor nodes are first clustered to determine the location of the collection points. Then, the task area is divided according to the communication radius between the UAV and the base station, and between UAVs. The collection points in the same area are under the responsibility of the same UAV. Then, the present invention proposes a trajectory planning algorithm based on point matching. The algorithm specifically includes four steps: mapping a set of connectable collection points, matching collection point pairs, generating waypoints corresponding to unconnectable collection points, and trajectory planning. The collaborative trajectory of multiple UAVs is optimized. Ultimately, the total task completion time is reduced and the efficiency of task execution is improved.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing multi-UAV collaborative real-time data collection and transmission trajectory, characterized in that: include: Step 1: For large-scale wireless sensor network data collection and transmission scenarios, a system model for multi-UAV collaborative real-time data collection and transmission is established to solve the completion time minimization problem. Step 2: Cluster the sensor nodes and determine the cluster center as the location of the data collection point. Then, divide the mission area according to the communication radius between the UAV and the base station, and between UAVs. The collection points in the same area are responsible for the same UAV. Step 3: Through trajectory discretization, the optimization problem constructed in step 1 is transformed into a new optimization problem about the hovering points, hovering time, flight speed and acquisition order of multiple UAVs; the trajectory of each UAV is discretized into K waypoints, expressed as represents the hovering position of the drone m when the sensor node cluster k is collected, I=(I1,I2,...,I K ) represents the acquisition order of K sensor node clusters, and the flight time of UAV m is: in, represents the hovering position of UAV m when the kth sensor node cluster is collected, Indicates that the drone m is from arrive Flight speed; The hovering time of drone m is: in, Indicates that the drone m is The hovering time at The new optimization problem after transformation is: Step 4: For the new optimization problem transformed in step 3, solve it through the trajectory planning algorithm based on point matching to obtain the flight trajectory of multiple UAVs.

2. The method for optimizing multi-UAV collaborative real-time data collection and transmission trajectory according to claim 1 is characterized in that: The completion time minimization problem in step 1 is: Among them, T represents the completion time, T is both the objective function and one of the optimization variables, a m,k represents the relationship between the collection point k and the drone m, q m (t) represents the position of UAV m at time t, λ m，k (t) indicates whether the UAV m is located at the collection point k at time t, z ij (t) indicates whether node i is connected to node j at time t, and node represents a drone or a base station.

3. The method for optimizing multi-UAV collaborative real-time data collection and transmission trajectory according to claim 1 is characterized in that: The point matching-based trajectory planning algorithm in step 4 includes: mapping of a set of connectable collection points, matching of collection point pairs, generating waypoints corresponding to unconnectable collection points, and trajectory planning.

4. A multi-UAV collaborative real-time data acquisition and transmission trajectory optimization system, used to implement the method according to any one of claims 1 to 3, characterized in that: include: The first processing module is used to establish a system model for real-time data collection and transmission of multiple UAVs in a large-scale wireless sensor network data collection and transmission scenario, and to solve the problem of minimizing the completion time. The second processing module is used to cluster the sensor nodes and determine the cluster center as the location of the data collection point. The task area is then divided according to the communication radius between the UAV and the base station, and between UAVs. The collection points in the same area are responsible for the same UAV. A third processing module is used to transform the optimization problem constructed in the first processing module into a new optimization problem regarding the hovering points, hovering time, flight speed and acquisition sequence of multiple UAVs through trajectory discretization; The fourth processing module is used to solve the new optimization problem converted in the third processing module through a trajectory planning algorithm based on point matching to obtain the flight trajectories of multiple UAVs.

5. A multi-UAV collaborative real-time data collection and transmission trajectory optimization device, characterized in that: It includes a memory and a processor, the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the multi-UAV collaborative real-time data collection and transmission trajectory optimization method as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which enable the computer to execute the steps of the multi-UAV collaborative real-time data collection and transmission trajectory optimization method according to any one of claims 1 to 3.

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