Three-dimensional trajectory planning method and device, equipment, storage medium and program product

Abstract

The invention provides a three-dimensional trajectory planning method, device and equipment, a storage medium and a program product, and belongs to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining the collection data of an information collection point by an unmanned aerial vehicle; acquiring a communication node, and calculating path loss; acquiring a plurality of data clusters, determining a flight height and a cluster center according to a maximum value of a preset sampling precision requirement in the data clusters, and forming an initial three-dimensional trajectory; calculating information acquisition task time and communication interruption duration; based on the optimization model, minimizing the total task time of the unmanned aerial vehicle group is taken as a target, an optimized three-dimensional track is obtained, and the total task time is equal to the sum of weighted values of the information acquisition task time and the communication interruption duration; and dispersing the optimized three-dimensional trajectory into a plurality of trajectory segments, and optimizing the flight speed. According to the technical scheme of the invention, the accuracy of the sampling result can be ensured, the three-dimensional trajectory is optimized, and the total task time of the unmanned aerial vehicle group is minimized, so that the information acquisition efficiency is improved.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, and particularly relates to a three-dimensional trajectory planning method, device, equipment, storage medium, and program product. Background Technology

[0002] The fusion of communication and sensing technologies is a key development direction for future networks. It involves acquiring environmental information through multimodal sensing (such as cameras, LiDAR, and millimeter-wave radar) and intelligently fusing it with communication signals to improve environmental perception capabilities and efficiency. Unmanned aerial vehicles (UAVs), as aerial wireless remote sensing devices, can be flexibly deployed and possess flight capabilities. Currently, airspace resources are abundant, and UAVs are less restricted by environmental factors, allowing them to collect vast amounts of information using their onboard sensors. For areas with information collection needs, it is necessary to plan reasonable three-dimensional flight trajectories for UAV swarms, ensuring rapid response to information collection while maintaining effective communication connections and timely data transmission.

[0003] In related technologies, the total mission time is relatively long and the accuracy of the sampling results is poor when drones sample the information collection area. Summary of the Invention

[0004] This invention provides a three-dimensional trajectory planning method, apparatus, device, storage medium, and program product, which can optimize the three-dimensional trajectory and minimize the total task time of the UAV swarm while ensuring the accuracy of the sampling results, thereby improving the efficiency of information collection.

[0005] In a first aspect, embodiments of the present invention provide a method for three-dimensional trajectory planning of a drone swarm, comprising: acquiring data collected by multiple drones in a drone swarm from multiple information collection points within an area to be collected, wherein each drone in the drone swarm has a different sampling precision and a different sampling time for collecting information collection points, and the collected data includes data collected by each information collection point when the drone's sampling precision is greater than or equal to a preset sampling precision requirement and the drone's sampling time is greater than or equal to a preset minimum sampling time; acquiring multiple communication nodes within the area to be collected, calculating the path loss of the channel between the drone and the communication node based on the elevation angle between the drone and the communication node, the distance between the drone and the communication node, and additional path consumption, and determining that a communication connection is established between the drone and the communication node when the path loss is less than a first preset threshold; and grouping the collected data of the multiple information collection points according to the spatial location of the multiple information collection points and the preset sampling precision requirement, to obtain... The system identifies multiple data clusters. Based on the maximum value of multiple preset sampling accuracy requirements in each cluster, it determines the UAV's flight altitude and cluster center. Based on the flight altitude and cluster center, it forms an initial 3D trajectory for the UAV to access all information collection points within each cluster, where the cluster center is the UAV's hovering position at the specified flight altitude. The system calculates the UAV's information collection task time based on its sampling accuracy, sampling time, preset sampling accuracy requirements, and preset minimum sampling time. It also calculates the duration of communication interruption between the UAV and communication nodes based on path loss. Using an optimization model, the initial 3D trajectory is optimized to minimize the total task time of the UAV swarm, resulting in an optimized 3D trajectory. The total task time equals the weighted sum of the information collection task time and the communication interruption duration. The optimized 3D trajectory is then discretized into multiple trajectory segments of preset length. Based on a linear programming model, the UAV's flight speed in each trajectory segment is optimized.

[0006] In one feasible implementation, acquiring data collected by multiple drones from multiple data collection points within an area to be collected within a drone swarm includes: acquiring preset sampling accuracy requirements and preset minimum sampling times for each data collection point within the area to be collected, wherein the preset sampling accuracy requirement is the minimum sampling accuracy requirement for each data collection point; calculating the sampling accuracy of the drones based on their flight altitude, and calculating the radius of the sampling coverage area of ​​the drones based on their flight altitude and extreme sampling angle; and determining the data collected by the drones from data collection points within the sampling coverage area as collected data when the drones' sampling accuracy is greater than or equal to the preset sampling accuracy requirement and the drones' sampling time is greater than or equal to the preset minimum sampling time.

[0007] In one feasible implementation, the sampling accuracy of the UAV is calculated based on its flight altitude, and the radius of the sampling coverage area of ​​the UAV is calculated based on its flight altitude and the limit sampling angle, including: calculating the sampling accuracy of the UAV based on its flight altitude using a first calculation formula, and calculating the radius of the sampling coverage area based on its flight altitude and the limit sampling angle using a second calculation formula. The first calculation formula is: in, This represents the sampling accuracy of the UAV at time t. This represents the flight altitude of the drone at time t. This represents the sensing accuracy coefficient of the drone; The second calculation formula is: in, The radius of the sampling coverage area at time t. This represents the flight altitude of the drone at time t. Indicates the extreme acquisition angle.

[0008] In one feasible implementation, multiple communication nodes within the information collection area are acquired. Based on the elevation angle between the UAV and the communication nodes, the distance between the UAV and the communication nodes, and additional path consumption, the path loss of the channel between the UAV and the communication nodes is calculated. If the path loss is less than a first preset threshold, a communication connection is established between the UAV and the communication nodes. This includes: acquiring the number and location of communication nodes within the information collection area, where communication nodes include base stations, and base stations include the base station of the UAV's departure base; calculating a first probability and a first path loss for the channel between the UAV and the communication nodes to be a line-of-sight channel, and a second probability and a second path loss for a non-line-of-sight channel, based on the elevation angle between the UAV and the communication nodes, the distance between the UAV and the communication nodes, and additional path consumption; calculating an average path loss based on the first probability, the first path loss, the second probability, and the second path loss; and determining that a communication connection is established between the UAV and the communication nodes if the average path loss is less than a first preset threshold.

[0009] In one feasible implementation, based on the elevation angle between the UAV and the communication node, the distance between the UAV and the communication node, and the additional path cost, a first probability and a first path loss for the channel between the UAV and the communication node to be a line-of-sight (LAS) channel, and a second probability and a second path loss for a non-LAS channel are calculated. This includes: calculating the first probability that the channel between the UAV and the communication node is a LAS channel based on a third calculation formula, according to the elevation angle between the UAV and the communication node, a preset first environmental parameter, and a preset second environmental parameter; calculating the first path loss for the channel between the UAV and the communication node to be a LAS channel based on a fourth calculation formula, according to the distance between the UAV and the communication node, the operating frequency, the speed of light, and the additional path cost of the LAS channel; calculating the second probability that the channel between the UAV and the communication node is a non-LAS channel based on the first probability, wherein the sum of the first probability and the second probability is 1; and calculating the second path cost for the channel between the UAV and the communication node to be a non-LAS channel based on the first path loss. The third calculation formula is: in, Let represent the first probability that the channel between the m-th UAV and the y-th communication node at time t is a line-of-sight channel. Let represent the elevation angle between the m-th UAV and the y-th communication node at time t, where a is a preset first environmental parameter and b is a preset second environmental parameter. The fourth calculation formula is: in, The first path loss of the channel between the m-th UAV and the y-th communication node at time t is the line-of-sight channel. This represents the distance between the m-th UAV and the y-th communication node at time t. This represents the operating frequency, and c represents the speed of light. This represents the additional path cost of the line-of-sight channel.

[0010] In one feasible implementation, the average path loss is calculated based on the first probability, the first path loss, the second probability, and the second path loss, including: calculating the average path loss based on the first probability, the first path loss, the second probability, and the second path loss according to the fifth calculation formula. The fifth calculation formula is: in, This represents the average path loss of the channel between the m-th UAV and the y-th communication node at time t. Let represent the first probability that the channel between the m-th UAV and the y-th communication node at time t is a line-of-sight channel. Let t represent the first path loss of the line-of-sight channel between the m-th UAV and the y-th communication node at time t. Let represent the second probability that the channel between the m-th UAV and the y-th communication node at time t is a non-line-of-sight channel. The second path loss of the non-line-of-sight channel is represented by the channel between the m-th UAV and the y-th communication node at time t.

[0011] In one feasible implementation, the flight altitude and cluster center of the UAV are determined based on the maximum value of multiple preset sampling accuracy requirements in each data cluster, including: taking the maximum value of multiple preset sampling accuracy requirements in each data cluster as the sampling accuracy of the UAV, determining the flight altitude and cluster center of the UAV based on the sampling accuracy of the UAV, and determining the cluster radius based on the flight altitude and the extreme acquisition angle of the UAV.

[0012] In one feasible implementation, the information collection task time is equal to the sum of the UAV's flight time between multiple data clusters and the UAV's hovering sampling time within a data cluster, where the hovering sampling time is the maximum value of the preset minimum sampling time for all information collection points in the cluster.

[0013] In one feasible implementation, the duration of communication interruption between the UAV and the communication node is calculated based on path loss, including: calculating the cumulative duration of establishing a communication connection between the UAV and the communication node based on path loss; and calculating the duration of communication interruption between the UAV and the communication node based on the cumulative duration and the total duration.

[0014] In one feasible implementation, the weighted value of the communication interruption duration is equal to the product of the communication interruption duration and a preset weighting parameter.

[0015] Secondly, embodiments of the present invention provide a three-dimensional trajectory planning device for a drone swarm, comprising: a first processing unit, configured to acquire data collected by multiple drones in a drone swarm from multiple information collection points within an area to be collected, wherein each drone in the drone swarm has a different sampling precision and a different sampling time for collecting information collection points, and the acquired data includes data collected by each information collection point when the drone's sampling precision is greater than or equal to a preset sampling precision requirement and the drone's sampling time is greater than or equal to a preset minimum sampling time; a second processing unit, configured to acquire multiple communication nodes within the area to be collected, calculate the path loss of the channel between the drone and the communication node based on the elevation angle between the drone and the communication node, the distance between the drone and the communication node, and additional path consumption, and determine that a communication connection is established between the drone and the communication node when the path loss is less than a first preset threshold; and a three-dimensional trajectory forming unit, configured to process the acquired data of the multiple information collection points according to the spatial location of the multiple information collection points and the preset sampling precision requirement. The system is divided into several data clusters. The flight altitude and cluster center of the UAV are determined based on the maximum value of multiple preset sampling accuracy requirements within each cluster. Based on the flight altitude and cluster center, an initial 3D trajectory is formed for the UAV to access all information collection points within each cluster. The cluster center is the hovering position of the UAV at its flight altitude. A time calculation unit calculates the UAV's information collection task time based on its sampling accuracy, sampling time, preset sampling accuracy requirements, and preset minimum sampling time. It also calculates the communication interruption duration based on path loss. A 3D trajectory optimization unit optimizes the initial 3D trajectory based on an optimization model, aiming to minimize the total task time of the UAV swarm. The total task time is equal to the weighted sum of the information collection task time and the communication interruption duration. A flight speed optimization unit discretizes the optimized 3D trajectory into multiple trajectory segments of preset length and optimizes the UAV's flight speed in each trajectory segment based on a linear programming model.

[0016] Thirdly, embodiments of the present invention provide an electronic device, including: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the UAV swarm three-dimensional trajectory planning method of any of the technical solutions in the first aspect.

[0017] Fourthly, embodiments of the present invention provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the UAV swarm three-dimensional trajectory planning method of any of the technical solutions in the first aspect.

[0018] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the UAV swarm three-dimensional trajectory planning method of any of the technical solutions in the first aspect.

[0019] This invention aims to provide a three-dimensional trajectory planning method, apparatus, device, storage medium, and program product. Firstly, unlike traditional two-dimensional UAV flight trajectory design with a fixed flight altitude, this invention considers both sensory information acquisition and information feedback under effective communication connectivity. It combines the UAV's flight altitude with communication quality and sampling accuracy, enabling the optimization of the UAV swarm's three-dimensional flight trajectory to improve the quality of UAV communication and sensing, thereby increasing the efficiency of information acquisition and the accuracy of sampling results. Secondly, this invention employs a Point of Interest (PoI) model. Information (collection point) clustering algorithms can group all information collection points within a region into a minimum number of data clusters based on their spatial location and preset sampling accuracy requirements. Unlike traditional clustering methods, PoI clustering algorithms determine the UAV's flight altitude and cluster center based on the maximum value of multiple preset sampling accuracy requirements within each data cluster. The PoI clustering algorithm can optimize the location of the cluster center, ensuring that the UAV maintains effective communication connectivity as much as possible when flying to the cluster center. Thirdly, for information collection points with different preset sampling accuracy requirements within the area to be collected, the algorithm plans the three-dimensional flight trajectories of all UAVs, minimizing the time required to complete the information collection task and minimizing the time required for communication connection interruption. Fourthly, by optimizing the flight speed of the UAVs, the algorithm helps to further reduce the total task time (total flight time) of the UAV swarm.

[0020] The technical solution of this invention can optimize the three-dimensional trajectory and minimize the total mission time of the UAV swarm while ensuring the accuracy of the sampling results, thereby improving the efficiency of information collection. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is an architecture diagram of a three-dimensional trajectory planning system for a drone swarm provided in an embodiment of the present invention; Figure 2 This is a flowchart of a three-dimensional trajectory planning method for an unmanned aerial vehicle (UAV) swarm provided in an embodiment of the present invention; Figure 3 This is a three-dimensional flight trajectory diagram of an unmanned aerial vehicle (UAV) swarm provided in an embodiment of the present invention; Figure 4 yes Figure 2 The detailed flowchart of S202; Figure 5 This is a flowchart of another method for three-dimensional trajectory planning of unmanned aerial vehicle swarms provided in an embodiment of the present invention; Figure 6 yes Figure 2 The detailed flowchart of S204; Figure 7 This is a flowchart of another method for three-dimensional trajectory planning of unmanned aerial vehicle swarms provided in an embodiment of the present invention; Figure 8 This is a flowchart of another method for three-dimensional trajectory planning of unmanned aerial vehicle swarms provided in an embodiment of the present invention; Figure 9 This is a flowchart of another method for three-dimensional trajectory planning of unmanned aerial vehicle swarms provided in an embodiment of the present invention; Figure 10 This is a flowchart of another method for three-dimensional trajectory planning of unmanned aerial vehicle swarms provided in an embodiment of the present invention; Figure 11 This is a structural block diagram of a three-dimensional trajectory planning device for a drone swarm provided in an embodiment of the present invention; Figure 12 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention; Figure 13 This is a flight trajectory diagram of an unmanned aerial vehicle (UAV) swarm provided in an embodiment of the present invention; Figure 14 This is a time comparison bar chart provided in an embodiment of the present invention.

[0023] The attached figures are labeled as follows: 100: UAV swarm 3D trajectory planning system; 110: First model building module; 120: Second model building module; 130: UAV swarm 3D trajectory planning module; 140: PoI clustering module; 150: MTSP problem solving module; 160: UAV speed optimization module; 300: Electronic equipment; 301: Processor; 302: Memory; 303: Communication interface; 304: Bus; 400: UAV swarm 3D trajectory planning device; 410: First model building unit; 420: Second model building unit; 430: 3D trajectory planning unit; 440: Time calculation unit; 450: 3D trajectory optimization unit; 460: Flight speed optimization unit. Detailed Implementation

[0024] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present invention and not to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present invention by illustrating examples of the invention.

[0025] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0026] Communication and sensing fusion technology is a key development direction for 5G-A (5G-Advanced, an enhanced version of fifth-generation mobile communication technology) and future networks. It acquires environmental information through multimodal sensing (such as cameras, LiDAR, millimeter waves, etc.) and intelligently integrates it with communication signals to improve environmental perception capabilities and efficiency. Unmanned aerial vehicles (UAVs), as aerial wireless remote sensing devices, can be flexibly deployed and have flight capabilities. Currently, airspace resources are abundant, and UAVs are less restricted by environmental factors, allowing them to collect vast amounts of information using onboard sensors. For areas with information collection needs, it is necessary to plan reasonable three-dimensional flight trajectories for UAV swarms, maintaining effective communication connections and timely data transmission while rapidly responding to information collection.

[0027] In related technologies, the total mission time is relatively long and the accuracy of the sampling results is poor when drones sample the information collection area.

[0028] This invention aims to provide a three-dimensional trajectory planning method, apparatus, device, storage medium, and program product. Firstly, unlike traditional two-dimensional UAV flight trajectory design with a fixed flight altitude, this invention considers both sensory information acquisition and information feedback under effective communication connectivity. It combines the UAV's flight altitude with communication quality and sampling accuracy, enabling the optimization of the UAV swarm's three-dimensional flight trajectory to improve the quality of UAV communication and sensing, thereby increasing the efficiency of information acquisition and the accuracy of sampling results. Secondly, this invention employs a Point of Interest (PoI) model. Information (collection point) clustering algorithms can group all information collection points within a region into a minimum number of data clusters based on their spatial location and preset sampling accuracy requirements. Unlike traditional clustering methods, PoI clustering algorithms determine the UAV's flight altitude and cluster center based on the maximum value of multiple preset sampling accuracy requirements within each data cluster. The PoI clustering algorithm can optimize the location of the cluster center, ensuring that the UAV maintains effective communication connectivity as much as possible when flying to the cluster center. Thirdly, for information collection points with different preset sampling accuracy requirements within the area to be collected, the algorithm plans the three-dimensional flight trajectories of all UAVs, minimizing the time required to complete the information collection task and minimizing the time required for communication connection interruption. Fourthly, by optimizing the flight speed of the UAVs, the algorithm helps to further reduce the total task time (total flight time) of the UAV swarm.

[0029] The technical solution of this invention can optimize the three-dimensional trajectory and minimize the total mission time of the UAV swarm while ensuring the accuracy of the sampling results, thereby improving the efficiency of information collection.

[0030] To address the problems of the prior art, embodiments of the present invention provide a three-dimensional trajectory planning method, apparatus, device, storage medium, and program product.

[0031] Figure 1 An architecture diagram of a UAV swarm three-dimensional trajectory planning system 100 provided in one embodiment of the present invention is shown. Figure 1 As shown, the UAV swarm 3D trajectory planning system 100 may include a first model building module 110, a second model building module 120, a UAV swarm 3D trajectory planning module 130, and a UAV speed optimization module 160.

[0032] The first model building module 110 is used to build the PoI system model and the UAV information collection model. The second model building module 120 is used to build the UAV communication model.

[0033] The information collection point system model defines multiple information collection points within the area to be collected. Each information collection point has preset sampling accuracy requirements and preset minimum sampling time. The UAV information collection model defines the sampling accuracy of UAVs in a UAV swarm. A UAV is considered to have successfully sampled the information collection point if its sampling accuracy is greater than or equal to the preset sampling accuracy requirement and its sampling time is greater than or equal to the preset minimum sampling time.

[0034] The UAV communication model is used to define multiple communication nodes in the area where information is to be collected. Based on the elevation angle between the UAV and the communication node, the distance between the UAV and the communication node, and the additional path consumption, the path loss of the channel between the UAV and the communication node is calculated. If the path loss is less than a first preset threshold, a communication connection is established between the UAV and the communication node.

[0035] The UAV swarm 3D trajectory planning module 130 is used to plan the 3D flight trajectory of the UAV swarm. The UAV speed optimization module 160 is used to optimize the flight speed of the UAVs.

[0036] The UAV swarm 3D trajectory planning module 130 is used to initialize the trajectories of all UAVs and obtain a balanced feasible solution; the GVNS (General Variable Neighborhood Search) algorithm is used to solve the Min-Max task time and corresponding trajectory; the trajectory and hovering point of the UAV with the longest task time are fixed, and the steps of the GVNS algorithm are repeated for the remaining UAVs until the trajectories of all UAVs are assigned.

[0037] The UAV speed optimization module 160 is used to discretize the UAV's flight trajectory into several trajectory segments according to a preset length, extract the center position of each trajectory segment as the basis for sampling and communication judgment; establish a linear programming model, and the constraints may include: the effective sampling time of each information collection point is not shortened and the UAV's flight speed does not exceed the maximum speed; solve the linear programming model to obtain the optimized flight speed of each trajectory segment.

[0038] In one embodiment, such as Figure 1 As shown, the UAV swarm 3D trajectory planning module 130 may include a PoI clustering module 140 and an MTSP problem solving module 150.

[0039] It should be noted that MTSP (Multiple Traveling Salesman Problem) refers to the Multiple Traveling Salesman Problem.

[0040] The PoI clustering module is used to group the collected data from multiple information collection points according to their spatial locations and preset sampling accuracy requirements, resulting in multiple data clusters. Based on the maximum value of multiple preset sampling accuracy requirements in each data cluster, the flight altitude and cluster center of the UAV are determined. Based on the flight altitude and cluster center, the initial three-dimensional trajectory of the UAV visiting all information collection points in the data cluster is formed, where the cluster center is the hovering position of the UAV at the flight altitude.

[0041] The MTSP problem-solving module 150 is used to optimize the initial three-dimensional trajectory and obtain the optimized three-dimensional trajectory based on the Min-Max optimization model with the goal of minimizing the total mission time of the UAV swarm. The total mission time is equal to the weighted sum of the information acquisition mission time and the communication interruption duration.

[0042] Figure 2 A flowchart of a UAV swarm three-dimensional trajectory planning method according to an embodiment of the present invention is shown. In one embodiment, the UAV swarm three-dimensional trajectory planning method can be applied to a UAV swarm three-dimensional trajectory planning system 100.

[0043] like Figure 2 As shown, the UAV swarm 3D trajectory planning method may include the following steps: S202, acquire data collected by multiple drones in a drone swarm from multiple data collection points within the area to be collected. The sampling accuracy and sampling time of each drone in the drone swarm are different. The collected data includes data from each data collection point collected when the sampling accuracy of the drone is greater than or equal to the preset sampling accuracy requirement and the sampling time of the drone is greater than or equal to the preset minimum sampling time.

[0044] In one embodiment, an information collection point system model and a UAV information collection model are constructed. The information collection point system model is used to define multiple information collection points in the area where information is to be collected. Each information collection point has a preset sampling accuracy requirement and a preset minimum sampling time. The UAV information collection model is used to define the sampling accuracy of UAVs in a UAV swarm. If the sampling accuracy of a UAV is greater than or equal to the preset sampling accuracy requirement and the sampling time of a UAV is greater than or equal to the preset minimum sampling time, it is determined that the UAV has successfully sampled the information collection point.

[0045] In one embodiment, the number and location of information collection points within the area to be collected are obtained, and the preset sampling accuracy requirements and preset minimum sampling time for each information collection point are determined to complete the construction of the information collection point system model; based on the flight altitude of the UAV, the sampling accuracy of the UAV is calculated, and based on the flight altitude and the extreme sampling angle, the radius of the sampling coverage area of ​​the UAV is calculated to complete the construction of the UAV information collection model. In one embodiment, assume the area where information is to be collected (the information collection area) is a square region with a side length of L, distributed with information sources of different types and sampling precisions. For example, sampling high-definition on-site photos requires the drone to be close enough to the target and take several corresponding photos or videos of a certain duration; receiving radar waves allows the drone to be farther away from the signal source, etc. The information sources are defined as multiple points of information (PoIs) with different sampling precisions. For a large area requiring continuous information collection, it can also be discretized into a group of PoIs with the same sampling precision for processing.

[0046] Assuming there are K information collection points within the task area, the th... The ground location of each Point of Interest (PoI) is represented by the following vector: .

[0047] in, It is the first Ground location vectors of each PoI The vector used to represent the ground position is a 1-row, 2-column real number vector.

[0048] Introduction Indicates the first Minimum sampling accuracy requirement per Point of Interest (PoI). If the drone's sampling accuracy... exist time less than Then the first A PoI in At any given moment, sampling cannot be successfully completed. Furthermore, each data collection point has a corresponding minimum sampling time. That is, the drone maintains its position on the first The sampling time for each PoI is no less than Only when all information collection for the Point of Interest (PoI) is complete is the collection considered finished. Furthermore, to ensure the quality of the sampled information, the following definition is defined: express The maximum blur produced by the drone must not exceed a certain value. (Second preset threshold).

[0049] The first condition is true if and only if the following condition is met. Each PoI is considered to be in Successful sampling at any time: The sampling accuracy of the UAV is no less than the first... The minimum sampling accuracy requirement for each Point of Interest (PoI), i.e. The sampling time for drones shall not be less than the first... Minimum sampling time for each PoI Only then is it considered complete. Information collection task for each Point of Interest (PoI); the ambiguity of the drone does not exceed the set upper limit (second preset threshold), i.e. .

[0050] in and These are related to the drone's spatial location and flight speed, respectively.

[0051] In one embodiment, the spatial position vector of the UAV at time t can be represented as: .

[0052] in, This represents a real number vector with 1 row and 3 columns representing the spatial location vector.

[0053] The projection position of the spatial position vector is And the corresponding flight altitude is Assume the drone's data acquisition equipment is perpendicular to the ground and has a limiting acquisition angle. Therefore, when the sampling accuracy of the drone meets the sampling accuracy requirements of PoI, the drone can collect PoI information within a circular area centered on the ground projection position.

[0054] The ground area that the drone's sensing device can perceive at time t can be approximated by [formula missing]. The central circular area, radius It is given by the following equation (1.1): .

[0055] For UAV information collection, sampling accuracy is defined as the limit of the clarity of the ground Point of Interest (PoI) perceived by the UAV's sensing device. This invention uses Ground Sample Distance (GSD) to quantify the resolution of the UAV's sensing device. Taking image information collection as an example, GSD is defined as the ground distance length corresponding to one pixel in the image. One pixel corresponds to... The ground area. Generally speaking, the higher the GSD value, the lower the image resolution. Equivalent to the reciprocal of GSD, this invention quantizes the sampling precision of all information to 1 / GSD, then It can be obtained from the following formula (1.2): .

[0056] in This represents the perception accuracy coefficient of the drone. The actual perception accuracy decreases as the drone's flight altitude increases.

[0057] In one embodiment, a binary index function is introduced based on the information collection point system model and the UAV information collection model. ,when When, it indicates the first A PoI in The drone must successfully sample the data at all times, otherwise... The value indicates that sampling was unsuccessful. According to formulas (1.1) and (1.2), it can be expressed as formula (1.3): .

[0058] Here, "if" means if, and "otherwise" means otherwise. It represents the Euclidean norm between two vectors (which can be understood as the distance between two points).

[0059] The flight path of a single drone is as follows: Where T is the total flight time of the drone. If the drone is to successfully complete the [flight duration]... For each PoI information collection task, formula (1.4) must be satisfied: .

[0060] in, A binary indicator function to indicate whether sampling was successful. The minimum sampling time is T, and the total flight time of the UAV is T.

[0061] S204: Acquire multiple communication nodes within the information collection area. Calculate the path loss of the channel between the UAV and the communication node based on the elevation angle between the UAV and the communication node, the distance between the UAV and the communication node, and the additional path consumption. If the path loss is less than a first preset threshold, determine that a communication connection is established between the UAV and the communication node.

[0062] In one embodiment, a UAV communication model is constructed, wherein the UAV communication model is used to define multiple communication nodes in the area where information is to be collected, calculate the path loss of the channel between the UAV and the communication nodes, and determine that a communication connection is established between the UAV and the communication nodes when the path loss is less than a first preset threshold.

[0063] In one embodiment, the sampling accuracy is calculated based on the flight altitude, and the radius of the sampling coverage area is calculated based on the flight altitude and the extreme sampling angle according to the second calculation formula, so as to complete the construction of the UAV information collection model.

[0064] In one embodiment, if a communication link can be established, multiple drones are dispatched to collaboratively monitor the identified Point of Interest (PoI) and upload monitoring video via the nearest base station. This assumes drones can be dispatched from different rescue centers. Drones are being deployed for collaborative monitoring of disaster areas. In different scenarios, uneven distribution of ground communication infrastructure, equipment alarms, and base station damage can lead to communication vulnerabilities in the region. To support flight safety and the uploading of collected information, drones should minimize communication vulnerabilities and maximize their connection time with the network.

[0065] Assuming there are areas in the region where information is to be collected Each ground base station maintains communication capabilities. Without loss of generality, the drone's departure base is considered, in effect, as a normally functioning base station. The location of each base station is determined by It means that among them Indicates the location of the departure base.

[0066] The channel between the UAV and the ground base station is modeled as a LoS (Line-of-Sight) / NLoS (Non-Line-of-Sight) probabilistic channel.

[0067] Specifically, the drones and the first The probability that the channel between the base stations is controlled by the LoS link satisfies formula (1.5): .

[0068] in, yes Time of the first drones and the first The elevation angles between the ground base stations, a and b, are environmentally relevant parameters that can be obtained from real-world experience. The elevation angle is related to the UAV's trajectory and satisfies formula (1.6): .

[0069] in, yes Time of the first drones and the first The elevation angle between the ground base stations Let represent the flight altitude of the m-th UAV at time t. Let represent the ground projection position vector of the m-th UAV at time t. Indicates the first Each base station's ground location vector. It represents the Euclidean norm between two vectors (which can be understood as the distance between two points).

[0070] exist At that moment, if the first drones and the first If the path loss between the base stations is a Line-of-Sight (LoS) channel, then the path loss satisfies formula (1.7): .

[0071] in, It is the first drones and the first The distance between base stations This represents the operating frequency, and c represents the speed of light. This is the additional path loss for the Loss of Stream (LoS) channel.

[0072] Under NLoS (Non-Line-of-Sight) conditions, the channel typically experiences greater path loss, the difference of which is normalized to a constant. The path loss of the NLoS channel satisfies formula (1.8): .

[0073] Then, in Time of the first drones and the first The average path loss between the base stations satisfies formula (1.9): .

[0074] in, .

[0075] Assuming the average path loss is lower than a first preset threshold Then in At any time, it can be in the first drones and the first Communication is established between the base stations. Furthermore, if the first... A drone is considered to have successfully connected to the rescue network if it can communicate with at least one base station. This is achieved through binary indicator functions. Definition of the first The connection status of the drones can be expressed by formula (1.10). .

[0076] Here, "if" means "if" and "otherwise" means "otherwise". This represents the first preset threshold. Indicates the first drones and the first The average path loss of the channel between base stations.

[0077] In one embodiment, it is assumed that the aircraft are dispatched from the same or different initial bases. A drone was deployed to collaboratively monitor monitoring points within the disaster area. Among them, the first... The position vector of the UAV at time t is represented as: .

[0078] in, This is used to represent a position vector as a 3x3 real number vector.

[0079] No. The coverage area of ​​the drone satisfies formula (1.11): .

[0080] No. The sampling accuracy of the UAV satisfies formula (1.12): .

[0081] Define binary pointer functions If the first Each monitoring target point (information sampling point) at time by If a UAV (Unmanned Aerial Vehicle) successfully monitors the situation, then... ,otherwise Mathematically, the above can be represented by formula (1.13): .

[0082] Here, "if" means if, and "otherwise" means otherwise.

[0083] If the total information collection time exceeds a certain threshold, it can be considered that enough monitoring video data has been collected. Therefore, for the [missing information]... For each Point of Interest (PoI), successful information collection requires satisfying formula (1.14): .

[0084] in It is the first The information collection mission time of the drone includes the total time it takes for the drone to return to the base after completing all information collection tasks.

[0085] On the other hand, in order to receive control information and transmit it back in a timely manner, the drone needs to operate within a certain time frame. Maintaining communication with the base station during this period is equivalent to minimizing communication interruption time. If the drone is within effective communication range of any base station, a communication connection can be established. Therefore, the duration of the communication interruption is... It satisfies formula (1.15): .

[0086] In summary, the problem of designing the three-dimensional trajectory of a multi-target UAV swarm can be expressed as the following set of equations or formulas: .

[0087] Here, "P1" represents question 1. "st" indicates subject to. This is a weighted parameter between monitoring time and communication interruption time. The constraints sequentially ensure that all Points of Interest (PoIs) meet sampling accuracy requirements and guarantee sufficient data collection time; the UAV is constrained by its maximum flight speed. ;No. The drone's initial takeoff position and return termination position are the same, based on the corresponding base location. .

[0088] This represents the real-time velocity (with orientation) of the m-th UAV with a direction vector. The Euclidean norm of the velocity vector (i.e., the magnitude of the velocity vector) represents the real-time velocity of the m-th UAV.

[0089] S206. According to the spatial location of multiple information collection points and the preset sampling accuracy requirements, the collected data of multiple information collection points are grouped to obtain multiple data clusters. Based on the maximum value of multiple preset sampling accuracy requirements in each data cluster, the flight altitude of the UAV and the cluster center are determined. Based on the flight altitude and the cluster center, the initial three-dimensional trajectory of the UAV visiting all information collection points in the data cluster is formed, where the cluster center is the hovering position of the UAV at the flight altitude.

[0090] In one embodiment, considering the coverage area of ​​the UAV's sensing device, information from multiple Points of Interest (PoIs) can be collected simultaneously. Therefore, the information collection points can be grouped into multiple clusters (data clusters), and all PoIs within the same cluster can be successfully sampled by the UAV simultaneously, aiming to minimize the number of clusters required to cover all PoIs. However, since the area covered by the UAV is directly determined by its flight altitude, and the flight altitude is also related to the sampling accuracy requirements of each PoI, this invention employs a PoI clustering algorithm with heterogeneous sampling accuracy.

[0091] Assuming this Information collection points can be divided into The clusters, where the _ is the _ cluster .... Clusters are based on Centered on, and with height parameters Related.

[0092] in, The position vector used to represent the cluster center is a 1-row, 2-column real number vector.

[0093] According to formulas (1.11) and (1.12), if the drone hovers at a certain altitude... The first The center of each cluster Above, the camera coverage area is And the corresponding sampling precision is If the first A PoI can be located at a height of Cluster center If the drone above successfully samples, then it satisfies formula (1.16): .

[0094] Introduction Indicates the first Given a set of PoI indices for a cluster, minimizing the number of clusters can be equivalent to the following system of equations or formulas: .

[0095] Here, "P2" represents question 2. "st" indicates subject to. It means "for all those belonging to the first" The first cluster of the th cluster "One PoI". It represents the union of sets.

[0096] Note that the UAV sampling accuracy constraint can be converted into the UAV's flight altitude constraint. This further determines the first The coverage of each cluster. Therefore, "P2" can be relaxed and transformed into a clustering problem in two-dimensional space, as shown below: .

[0097] Here, "P2a" represents problem 2 after the transformation. Solving P2a yields the cluster center. and the corresponding set of PoIs for drones Then, the height parameter of each cluster center can be set to meet the sampling accuracy requirements of all PoIs belonging to that cluster. The height parameter of each cluster center satisfies formula (1.17): .

[0098] Initialize the uncovered PoI point set to Initialize the number of clusters to .

[0099] exist Find the Point of Interest (PoI) with the highest sampling accuracy requirement, denoted as... And generate a cluster to cover the first One Point of Interest (PoI). The hovering point height of the drone corresponding to this cluster is set to... .because This is the flight altitude with the highest sampling accuracy requirements, so if other Points of Interest (PoIs) are included in the cluster, information can also be successfully collected. Then, the cluster center... The following optimizations can be used to cover the first... One and as many PoIs as possible. Cluster centers It satisfies formula (1.18): .

[0100] Equation (1.18) can be solved using convex optimization. The information collection points covered by this cluster can be represented as: .

[0101] Then update the number of clusters and the set. , .

[0102] Repeat the above until This can be understood as: up to the set of uncovered PoIs. It becomes an empty set.

[0103] In one embodiment, the code for the PoI clustering algorithm may include the following: 1: Input: ; 2: Output: and ; 3: Initialization: and ; 4: While do; 5: ; 6: Find ; 7: Solving (1.18) yields ; 8: ; 9: Update ; 10: Return and ; 11: End while; 12: Return .

[0104] In one embodiment, the cluster center locations are further optimized based on the PoI clustering algorithm to reduce communication interruption time for the UAV. Since the cluster centers are equivalent to the UAV's hovering positions, it is desirable to place these cluster centers within the effective communication range of the ground base station. Note that the hovering points obtained from P2a are not the only locations where all PoI information within the corresponding cluster can be successfully collected. Indicates the first A sub-region of a cluster, which can successfully cover all PoI sets belonging to that cluster. To meet the resolution requirements of all Points of Interest (PoIs) within the cluster, sub-regions The height must not be greater than Assuming The altitude is fixed at ,use express The projection onto the ground satisfies formula (1.19): .

[0105] subregion It is projected onto The projection of the point set satisfies formula (1.20): .

[0106] When the drone is located in the sub-region Within this time, it can successfully sample the part belonging to the first... All PoIs of each cluster. It is easy to prove that... It is a convex region.

[0107] If hovering points located outside the effective communication coverage area of ​​a base station can be moved to the coverage area, communication downtime for drones can be effectively reduced. The goal is to maximize the number of hovering locations within the communication coverage area while minimizing the number of clusters. Based on the hovering point of P2a, this problem can be expressed as the following formula or system of equations: .

[0108] in, It is the first Base stations and 3D location Path loss between drones at the location.

[0109] Since the effective communication range is a convex region with a defined hovering height, P3 can be solved through efficient convex optimization. This strategy involves determining the range of each cluster center. Corresponding convex region And find a new hovering point in the convex region that is closest to the center of one of the base stations. After resolving P3, more drone hovering positions were moved into the effective communication range of the base station, which significantly reduced drone communication downtime.

[0110] In one embodiment, the communication clustering algorithm may include the following: 1: Input: ; 2: Output: ; 3: ; 4: Solve using Algorithm 1 ; 5: For do; 6: If then; 7: ; 8: else if then; 9: ; 10: else; 11: ; 12: ; 13: ; 14: End if; 15: End for; 16: Return .

[0111] S208 calculates the information acquisition task time of the UAV based on the sampling accuracy of the UAV, the sampling time of the UAV, the preset sampling accuracy requirements and the preset minimum sampling time, and calculates the duration of communication interruption between the UAV and the communication node based on path loss.

[0112] In one embodiment, the information collection task time is equal to the sum of the UAV's flight time between multiple data clusters and the UAV's hovering sampling time within a cluster, where the hovering sampling time is the maximum value of the minimum sampling time for all information collection points in the cluster.

[0113] S210, based on the optimization model, aims to minimize the total mission time of the UAV swarm, optimize the initial three-dimensional trajectory and obtain the optimized three-dimensional trajectory, where the total mission time is equal to the weighted sum of the information acquisition mission time and the communication interruption duration.

[0114] In one embodiment, the present invention employs the General Variable Neighborhood Search (GVNS) algorithm to solve the MTSP multiple traveling salesman problem.

[0115] In one specific embodiment, the optimization model is a Min-Max optimization model.

[0116] After determining the clusters with height parameters, the multi-UAV trajectory design for checking all PoIs involves sequentially visiting the center of each cluster and hovering over it for sufficient time to ensure that each PoI within the cluster is successfully sampled. The set of hovering points is: .

[0117] make Indicates the first The hovering point that the drone wants to visit. Furthermore, the... The flight trajectory of a drone can be described by the following vector: .

[0118] While the drone flies between two hovering points, it does not perform any sampling, thus allowing it to fly at maximum speed. Upon reaching a hovering point, the drone will remain there long enough to ensure successful sampling and information collection. Indicates that the drone is hovering. The shortest sampling time for location. The total task time for information collection by a single UAV satisfies formula (1.21): .

[0119] in, It is a set The first in The nth element. The first part of formula (1.21) is the nth element. The first part is the flight time between the hovering points of the UAVs, and the second part is the hovering time of the current cluster, which is related to the PoI with the longest sampling time requirement within the cluster. The hovering time of the current cluster satisfies formula (1.22): .

[0120] Drone communication interruption time It can be obtained by solving formulas (1.15), (1.21), and (1.22). Obtained through calculation.

[0121] For ease of definition, let The total mission time is defined as the weighted sum of the UAV sampling time and the communication terminal time. The minimum mission time for all UAVs can be programmed as a Min-Max problem. The minimum mission time for all UAVs satisfies formula (1.23): .

[0122] The Min-Max Multiple Traveling Salesman Problem (P4) has been proven to be NP-hard and cannot be solved in polynomial time. This invention employs an efficient heuristic algorithm to solve P4: the General Variable Neighborhood Search (GVNS) algorithm can be used to minimize the maximum monitoring time of all drones. When the number of drones is reduced to 1, the problem degenerates into the traditional Traveling Salesman Problem (TSP).

[0123] Assuming the initial position of the drone, i.e. All drones are located at the base. Different initialization strategies are used depending on whether all drones originate from the same base. Specifically, consider the following two scenarios: Scenario 1: If all drones depart from the same location, their flight paths will be initially balanced based on flight distance; that is, hovering points will be added one after another to the drone with the minimum weighted travel length. to indicate The distance matrix, Indicates the first Drone journey The last specified node. This is the current average inspection time.

[0124] Scenario 2: If the drone departs from different locations, the strategy will be initialized based on the area division, that is, hovering points will be assigned to the trip to the nearest initial location.

[0125] In one embodiment, the code for the initialization algorithm may include the following: 1: Input: and ; 2: Output: ; 3: Case 1: Same initial position; 4: According to Reordering ; 5: For do; 6: ; 7: ; 8: ; 9: ; 10: End for; 11: For do; 12: ; 13: ; 14: ; 15: ; 16: Update ; 17: End for; 18: Case 2: Different initial positions; 19: For do; 20: ; twenty one: ; 22: End for; 23: Calculate using formulas (1.15) and (1.21) ; 23: Return .

[0126] In one embodiment, the GVNS algorithm is used to improve the UAV trajectory. The pseudocode for the GVNS algorithm is given in Algorithm 4. It is the maximum number of neighborhood steps in Shaking to generate new possible solutions in a finite neighborhood. It is the maximum number of different neighborhood structures in SeqVND, which ensures that every possible solution can be optimized through these neighborhood structures to minimize the target time. This is the maximum allowed number of iterations in the process. If the objective function does not decrease after the second iteration, it is considered converged, and the iteration terminates. The Shaking (perturbation operation) and SeqVND (Sequential Variable Neighborhood Descent) algorithms are introduced in Algorithm 5 and Algorithm 6, respectively.

[0127] In one embodiment, the code for the GVNS algorithm (Algorithm 4) may include the following: 1: Input: ; 2: Output: ;in, See Algorithm 7 below for details; 3: ; 4: While do; 5: ; 6: ; 7: While do; 8: ; 9: ; 10: ; 11: If then; 12: ; 13: ; 14: ; 15: ; 16: else; 17: ; 18: End if; 19: End while; 20: ; 21: End while; 22: Return .

[0128] In one embodiment, the code for the Shaking algorithm (perturbation operation algorithm, Algorithm 5) may include the following: 1: Input: ; 2: Output: ; 3: While do; 4: ; 5: ; 6: ; 7: ; 8: ; 9: ; 10: ; 11: End while; 12: Return .

[0129] In one embodiment, the code for the SeqVND algorithm (Sequence-Variable Neighborhood Descent Algorithm, Algorithm 6) may include the following: 1: Input: ; 2: Output: ; 3: ; 4: While do; 5: If then; 6: ; 7: End if; 8: If then; 9: ; 10: End if; 11: If then; 12: ; 13: End if; 14: If then; 15: ; 16: End if; 17: If then; 18: ; 19: End if; 20: Calculate the track using formulas (1.15) and (1.21) Weighted time ; 21: If then; twenty two: ; 23: Calculate the track using formulas (1.15) and (1.21) Weighted time ; twenty four: ; 25: ; 26: else; 27: ; 28: End if; 29: End while; 30: Return .

[0130] In one embodiment, the neighborhood search method obtains the optimal solution by traversing a finite neighborhood. Five neighborhood search methods are used: one_move (single-node movement operation), two_move (two-node movement operation), two_exchange (two-node exchange operation), three_move (three-node movement operation), and three_exchange (three-node exchange operation), which are used for hovering point adjustment between different UAV flight paths. In addition, two_opt (two optimization algorithms) is applied to optimize the hovering point access order of a specific UAV flight path.

[0131] one_move: This strategy repositions the selected hover point to a new location on another drone's flight path, where the selected hover point belongs to the drone's flight path with the longest monitoring time. This neighborhood search has a solution set containing all possible cases. There must be a usable solution among them. Its maximum monitoring time meets The numbers in the solution set indices represent the range of the neighborhood search, i.e. This represents all possible neighborhood sets of only one element. Since the number of hover points for all drones is... Therefore, for a given solution, the computational complexity of this strategy is... Furthermore, the computational complexity of the next four neighborhood search methods is... .

[0132] two_move: This strategy is similar to one_move, but this method will have the longest monitoring time. Two adjacent hovering points of one drone's flight path are repositioned into the flight path of another drone. The two selected hovering points are treated as a single block without changing the order of access. The neighborhood search solution set is... including those with The optimal solution .

[0133] `two_exchange`: This strategy swaps two selected hover points, one belonging to the drone track with the longest monitoring time and the other drone track. This strategy works by traversing all possible solution sets. and find those with optimal solution .

[0134] three_move: This strategy is similar to one_move, but this method will have the longest monitoring time. Two adjacent hovering points of one drone's flight path are repositioned into the flight path of another drone. The two selected hovering points are treated as a single block without changing the order of access. The neighborhood search solution set is... including those with The optimal solution .

[0135] `three_exchange`: This strategy swaps three selected hover points, including two adjacent hover points from the UAV track with the longest monitoring time and one hover point from another UAV track. This method treats two adjacent hover points as a block without changing their order and swaps this block with another selected hover point using the `two_exchange` strategy. The neighborhood search solution set is... including those with The optimal solution .

[0136] two_opt: This method solves the hovering point access order of a single drone to minimize the specific drone monitoring time, which can be defined as the Traveling Salesman Problem (TSP).

[0137] In practice, while minimizing the maximum information sampling time, it may also be necessary to minimize the total energy consumption of the drones, which is proportional to the sum of the total sampling times of all drones. Therefore, the method of repeatedly calling GVNS to sequentially optimize the flight trajectory of each UAV is proposed in this section. A detailed description of the multi-UAV trajectory planning using repeated GVNS is as follows: Algorithm 3 is used to initialize the trajectory of each drone in order to find a set of feasible solutions in which the task time of each drone is relatively balanced.

[0138] Algorithm 4 is used to optimize this set of feasible solutions, and the Min-Max task time and the corresponding UAV flight trajectory are obtained.

[0139] Maintain the flight path of the drone with the longest mission time and remove the corresponding hover point. Then repeat Algorithm 4 for the remaining drones until all drone flight paths and corresponding hover points are fully assigned.

[0140] Algorithm 7 summarizes the pseudocode for obtaining all UAV trajectories by repeatedly using GVNS. The complexity of GVNS is... ,in This represents the total number of hovering points after clustering. The complexity of the proposed algorithm is no greater than that of GVNS. The problem size decreases as the number of iterations increases, because the problem size decreases.

[0141] In one embodiment, the code that repeats the GVNS algorithm may include the following: 1: Input: ; 2: Output: ; 3: ; 4: ; 5: While do; 6: ; 7: Find ; 8: ; 9: ; 10: ; 11: End while; 12: Return .

[0142] Based on the above algorithm, a three-dimensional flight trajectory of a drone swarm can be obtained (e.g., Figure 3As shown). The blue dots represent the PoI locations, the circles of varying radii represent the clustering of each PoI, and the broken line represents the flight trajectory (as shown). Figure 3 The three colored broken lines represent the flight paths of the three drones.

[0143] It should be noted that, Figure 13 This is a flight trajectory diagram of a drone swarm provided in an embodiment of the present invention. Figure 13 In the diagram, blue dots represent Points of Interest (PoIs), yellow stars represent Base Stations (BSs), and UAVs (Unmanned Aerial Vehicles) represent unmanned aerial vehicles (UAVs). "UAV1" represents the first UAV, "UAV2" represents the second UAV, and "UAV3" represents the third UAV. The broken line represents the flight path. Figure 13 The three colored broken lines represent the flight paths of the three drones.

[0144] S212 discretizes the optimized 3D trajectory into multiple trajectory segments according to a preset length, and optimizes the flight speed of the UAV in each trajectory segment based on a linear programming model.

[0145] The flight trajectory of the UAV is discretized into several trajectory segments of a preset length, and the center position of each trajectory segment is extracted as the basis for sampling and communication judgment. A linear programming model is established, and the constraints may include: the effective sampling time of each information collection point is not shortened and the flight speed of the UAV does not exceed the maximum speed. The linear programming model is solved to obtain the optimized flight speed of each trajectory segment.

[0146] In one embodiment, the drone flies at its maximum speed. During flight, Points of Interest (PoIs) can be successfully sampled within the required sampling accuracy range without needing to hover. Therefore, by optimizing the drone's flight speed, the mission time can be further reduced. Since the flight path of each drone has already been determined, speed optimization for a single drone is only considered.

[0147] Record all flight node positions of the drone as a set. The sampled PoI set is , No. The time for each PoI to be successfully sampled is Discretize its trajectory into several segments of length . .when When the time is very small, the time for each discrete trajectory segment It is also very small, equivalent to the instantaneous velocity of the drone. The center position of each discrete trajectory segment is extracted. This serves as the basis for determining whether the drone can successfully sample Points of Interest (PoIs) and establish a valid communication connection within that trajectory segment.

[0148] The center position of the discrete trajectory segment is: .

[0149] Formula (1.24) is: .

[0150] Formula (1.25) is: .

[0151] Formula (1.26) is: .

[0152] Formula (1.27) is: .

[0153] Binary indicator function It can be done Substitution Transform it into a discrete binary index function. To reasonably discretize the trajectory between every two nodes, let... Indicates the first The number of segmented trajectory segments is obtained by rounding up the trajectory length / discrete segment length to get the number of segments. Segment trajectory This allows us to obtain the total number of discrete segments. .

[0154] Therefore, the drone speed optimization problem can be equivalently represented by the following system of equations or formulas: .

[0155] According to the constraints in P5, the effective sampling time for each PoI by the UAV must not be shorter than the original successful sampling time, and the UAV is constrained by its maximum flight speed. This problem is actually a linear programming problem, which can be solved efficiently. Therefore, the flight speed of each UAV can be optimized using this method, ensuring the successful completion of all information collection tasks while reducing the task time.

[0156] Figure 14 This is a time-comparison bar chart provided in an embodiment of the present invention. Figure 14 In this context, "round-trip flight CPP" refers to the traditional technical solution of round-trip flight CPP. CPP (Coverage Path Planning) refers to round-trip coverage path planning. This technical solution involves many redundant paths and is time-consuming.

[0157] "Traditional MTSP" refers to the technical solution of traditional MTSP (Multiple Traveling Salesman Problem). This technical solution does not incorporate the sampling accuracy constraints and communication connectivity constraints of the UAV, resulting in a longer processing time.

[0158] "This solution" refers to the technical solution adopted by this invention, specifically the technical solution that executes steps S202 to S212. "This solution without speed optimization" refers to a technical solution that only executes steps S202 to S210, without executing S212.

[0159] It is clear that the time of "this plan" (total mission time, or total flight time) and the time of "this plan without speed optimization" are less than the time of "round-trip flight CPP" and less than the time of "traditional MTSP".

[0160] Furthermore, the time of "this solution" is shorter than that of "this solution without speed optimization". By optimizing the flight speed of the drones, it is beneficial to further reduce the total mission time (total flight time) of the drone swarm.

[0161] This invention aims to provide a three-dimensional trajectory planning method for UAV swarms. Firstly, unlike traditional two-dimensional UAV flight trajectory design with a fixed flight altitude, this invention considers both sensory information acquisition and information feedback under effective communication connectivity. It combines the UAV's flight altitude with communication quality and sampling accuracy, enabling the optimization of the UAV swarm's three-dimensional flight trajectory to improve the quality of UAV communication and sensing, thereby increasing the efficiency of information acquisition and the accuracy of sampling results. Secondly, this invention employs a Point of Interest (PoI) method. Information (collection point) clustering algorithms can group all information collection points within a region into a minimum number of data clusters based on their spatial location and preset sampling accuracy requirements. Unlike traditional clustering methods, PoI clustering algorithms determine the UAV's flight altitude and cluster center based on the maximum value of multiple preset sampling accuracy requirements within each data cluster. The PoI clustering algorithm can optimize the location of the cluster center, ensuring that the UAV maintains effective communication connectivity as much as possible when flying to the cluster center. Thirdly, for information collection points with different preset sampling accuracy requirements within the area to be collected, the algorithm plans the three-dimensional flight trajectories of all UAVs, minimizing the time required to complete the information collection task and minimizing the time required for communication connection interruption. Fourthly, by optimizing the flight speed of the UAVs, the algorithm helps to further reduce the total task time (total flight time) of the UAV swarm.

[0162] The technical solution of this invention can optimize the three-dimensional trajectory and minimize the total mission time of the UAV swarm while ensuring the accuracy of the sampling results, thereby improving the efficiency of information collection.

[0163] In one embodiment, such as Figure 4 As shown, S202 (acquiring the sampling accuracy of multiple information collection points within the information collection area and the drones in the drone swarm, where each information collection point has a sampling accuracy requirement and a minimum sampling time; determining that the drone has successfully sampled the information collection point when the sampling accuracy is greater than or equal to the sampling accuracy requirement and the drone's sampling time is greater than or equal to the minimum sampling time) may include the following steps: S2022, respectively obtain the preset sampling accuracy requirements and preset minimum sampling time of multiple information collection points in the information area to be collected. The preset sampling accuracy requirements are the minimum sampling accuracy requirements of each information collection point.

[0164] For the first time, the location of the Point of Interest (PoI), the minimum sampling accuracy (preset sampling accuracy requirement), and the preset minimum sampling time are used as the core parameters of the model (information collection point system model). This solves the problem of vague PoI sampling requirements in existing technologies (such as only vaguely requiring "data collection" without specifying accuracy and time standards), and provides "quantifiable target basis" for subsequent trajectory planning and task allocation.

[0165] S2024 calculates the sampling accuracy of the UAV based on its flight altitude, and calculates the radius of the UAV's sampling coverage area based on its flight altitude and the limit sampling angle.

[0166] This approach breaks through the limitations of the existing technology that "the sampling capability of drones is fixed", and clarifies the direct relationship between flight altitude and sampling accuracy and coverage radius (e.g., the lower the altitude, the higher the sampling accuracy and the smaller the coverage radius; the higher the altitude, the lower the sampling accuracy and the smaller the coverage radius). This allows the sampling capability of drones to be dynamically adjusted according to mission requirements, adapting to PoI collection scenarios with different accuracy requirements.

[0167] S2026, when the sampling accuracy of the UAV is greater than or equal to the preset sampling accuracy requirement, the sampling time of the UAV is greater than or equal to the preset minimum sampling time, and the ambiguity of the UAV is less than or equal to the second preset threshold, the data obtained by the UAV from the data collection points within the sampling coverage area is determined as the collected data; wherein, the ambiguity is related to the spatial position and flight speed of the UAV.

[0168] By establishing clear triple success criteria, the drone can verify in real time whether the conditions are met during the data collection process, avoiding repeated flights and data collection caused by "data collection that has been completed but does not meet the ambiguity requirements," thus shortening the overall mission time. This approach also provides feedback for subsequent trajectory optimization (e.g., if multiple data collection attempts for a certain Point of Interest fail due to excessive ambiguity, the drone's flight attitude can be optimized or its altitude reduced to decrease jitter).

[0169] In one embodiment, such as Figure 5 As shown, S2024 (calculating the sampling accuracy of the drone based on its flight altitude, and calculating the radius of the drone's sampling coverage area based on its flight altitude and the extreme sampling angle) may include the following steps: S2025, based on the first calculation formula, calculates the sampling accuracy of the UAV according to the flight altitude, and based on the second calculation formula, calculates the radius of the sampling coverage area according to the flight altitude and the extreme sampling angle.

[0170] The first calculation formula is: .

[0171] in, This represents the sampling accuracy of the UAV at time t. This represents the flight altitude of the drone at time t. This represents the perception accuracy coefficient of the drone.

[0172] The second calculation formula is: .

[0173] in, The radius of the sampling coverage area at time t. This represents the flight altitude of the drone at time t. Indicates the extreme acquisition angle.

[0174] Both formulas introduce a dynamic parameter at "time t" (flight altitude at time t), enabling sampling accuracy and coverage radius to be updated in real time according to the UAV's flight status, breaking through the limitations of traditional "static models" (fixed sampling accuracy / coverage). When the UAV adjusts its altitude at different flight stages, the formulas can calculate new sampling accuracy and coverage radius in real time, ensuring that the judgment of the PoI sampling capability is always consistent with the actual flight status, giving the UAV information collection model dynamic adaptability.

[0175] This invention takes into account both the acquisition of sensory information and the transmission of information under effective communication connection. It combines the flight altitude of the UAV with the communication quality and sampling accuracy, so that optimizing the three-dimensional flight trajectory of the UAV swarm can improve the quality of UAV communication and sensing, which is conducive to improving the efficiency of information acquisition and the accuracy of sampling results.

[0176] In one embodiment, such as Figure 6 As shown, S204 (acquiring multiple communication nodes within the area to be collected, calculating the path loss of the channel between the UAV and the communication nodes based on the elevation angle between the UAV and the communication nodes, the distance between the UAV and the communication nodes, and the additional path consumption, and determining that a communication connection is established between the UAV and the communication nodes if the path loss is less than a first preset threshold) may include the following steps: S2042, obtain the number and location of communication nodes within the information area to be collected. Communication nodes include base stations, and base stations include the base station of the UAV's departure base. Based on the elevation angle between the UAV and the communication node, the distance between the UAV and the communication node, and the additional path loss, calculate the first probability and first path loss of the channel between the UAV and the communication node as a line-of-sight channel, and the second probability and second path loss of a non-line-of-sight channel. Based on the probability and path loss of the line-of-sight channel and the probability and path loss of the non-line-of-sight channel, calculate the average path loss.

[0177] This approach breaks through the limitations of traditional communication models that rely on a single channel (calculating path loss only for line-of-sight or non-line-of-sight scenarios). By incorporating the dynamic characteristics of UAV flight (altitude changes, channel type switching due to obstacle obstruction), it simultaneously calculates the probabilities and corresponding path losses for both line-of-sight (LoS) and non-line-of-sight (NLoS) channels. The average path loss is obtained through probability weighting, reflecting both the objective existence of the two channels (e.g., the UAV sometimes has no obstructions during flight, and sometimes is obstructed by buildings / terrain) and quantifying the statistical characteristics of channel states, making the path loss calculation results more closely reflect actual operational scenarios.

[0178] S2044, if the average path loss is less than the first preset threshold, determine that a communication connection is established between the UAV and the communication node.

[0179] This approach solves the problem in traditional technologies where the determination of whether a communication connection has been established relies on subjective judgment or a single instantaneous parameter (such as path loss at a certain moment). By quantitatively comparing the "average path loss" (a statistically significant channel quality indicator) with a first preset threshold, the determination results are made objective and consistent, avoiding misjudgments of the communication connection status due to instantaneous channel fluctuations (such as a sudden increase in path loss caused by brief obstruction), and ensuring the stability of the determination logic.

[0180] In one embodiment, such as Figure 7 As shown, based on the elevation angle between the UAV and the communication node, the distance between the UAV and the communication node, and the additional path loss, the calculation of the first probability and first path loss of the channel between the UAV and the communication node being a line-of-sight channel, and the second probability and second path loss of a non-line-of-sight channel, may include the following steps: S2045, based on the third calculation formula, calculates the first probability that the channel between the UAV and the communication node is a line-of-sight channel according to the elevation angle between the UAV and the communication node, the preset first environmental parameter, and the preset second environmental parameter.

[0181] S2046, based on the fourth calculation formula, calculates the first path loss of the channel between the UAV and the communication node as a line-of-sight channel, taking into account the distance between the UAV and the communication node, the operating frequency, the speed of light, and the additional path consumption of the line-of-sight channel.

[0182] S2047, Based on the first probability, calculate the second probability that the channel between the UAV and the communication node is a non-line-of-sight channel, where the sum of the first probability and the second probability is 1.

[0183] S2048, Based on the first path loss, calculate the second path loss of the channel between the UAV and the communication node as a non-line-of-sight channel.

[0184] The third calculation formula is: .

[0185] in, Let represent the first probability that the channel between the m-th UAV and the y-th communication node at time t is a line-of-sight channel. Let represent the elevation angle between the m-th UAV and the y-th communication node at time t, where a is the first environmental parameter and b is the second environmental parameter; The fourth calculation formula is: .

[0186] in, The first path loss of the channel between the m-th UAV and the y-th communication node at time t is the line-of-sight channel. This represents the distance between the m-th UAV and the y-th communication node at time t. This represents the operating frequency, and c represents the speed of light. This represents the additional path cost of the line-of-sight channel.

[0187] This approach breaks through the limitations of traditional communication models that rely on a single channel (calculating path loss only for line-of-sight or non-line-of-sight scenarios). By incorporating the dynamic characteristics of UAV flight (altitude changes, channel type switching due to obstacle obstruction), it simultaneously calculates the probabilities and corresponding path losses for both line-of-sight (LoS) and non-line-of-sight (NLoS) channels. The average path loss is obtained through probability weighting, reflecting both the objective existence of the two channels (e.g., the UAV sometimes has no obstructions during flight, and sometimes is obstructed by buildings / terrain) and quantifying the statistical characteristics of channel states, making the path loss calculation results more closely reflect actual operational scenarios.

[0188] In one embodiment, such as Figure 8 As shown, the average path loss is calculated based on the first probability, the first path loss, the second probability, and the second path loss, including: S2049, based on the fifth calculation formula, calculates the average path loss according to the first probability, the first path loss, the second probability, and the second path consumption.

[0189] The fifth calculation formula is: .

[0190] in, This represents the average path loss of the channel between the m-th UAV and the y-th communication node at time t. Let represent the first probability that the channel between the m-th UAV and the y-th communication node at time t is a line-of-sight channel. Let t represent the first path loss of the line-of-sight channel between the m-th UAV and the y-th communication node at time t. Let represent the second probability that the channel between the m-th UAV and the y-th communication node at time t is a non-line-of-sight channel. The second path loss of the non-line-of-sight channel is represented by the channel between the m-th UAV and the y-th communication node at time t.

[0191] This approach solves the problem in traditional technologies where the determination of whether a communication connection has been established relies on subjective judgment or a single instantaneous parameter (such as path loss at a certain moment). By quantitatively comparing the "average path loss" (a statistically significant channel quality indicator) with a first preset threshold, the determination results are made objective and consistent, avoiding misjudgments of the communication connection status due to instantaneous channel fluctuations (such as a sudden increase in path loss caused by brief obstruction), and ensuring the stability of the determination logic.

[0192] In one embodiment, such as Figure 9 As shown, determining the drone's flight altitude and cluster center based on the maximum value of multiple preset sampling accuracy requirements in each data cluster can include the following steps: S2062, the maximum value of multiple preset sampling accuracy requirements in each data cluster is taken as the sampling accuracy of the UAV, the flight altitude and cluster center of the UAV are determined according to the sampling accuracy of the UAV, and the cluster radius is determined according to the flight altitude and the extreme acquisition angle of the UAV.

[0193] Based on the PoI sampling accuracy (the maximum value of multiple preset sampling accuracy requirements), the cluster center and the drone hovering altitude (flight altitude) are dynamically adjusted to ensure communication coverage by optimizing the location of the cluster center.

[0194] Standardized flight altitudes and cluster centers provide clear constraints for subsequent "initial 3D trajectory planning," which helps reduce the computational complexity of trajectory planning and ensures that the flight trajectories of multiple UAVs do not conflict (e.g., the flight altitudes of different clusters are clearly distinguished), thereby improving the orderliness of cluster collaborative operations.

[0195] In one embodiment, the information collection task time is equal to the sum of the UAV's flight time between multiple data clusters and the UAV's hovering sampling time within a data cluster, where the hovering sampling time is the maximum value of the preset minimum sampling time for all information collection points in the cluster.

[0196] Breaking down the information collection task time into mobile flight time and hover sampling time allows subsequent optimization efforts to be targeted (such as shortening mobile flight time by optimizing flight paths between data clusters, and reducing the number of clusters by reasonable clustering to reduce the total hovering time), avoiding the ambiguity caused by "generally optimizing task time" and improving optimization efficiency.

[0197] It should be noted that there is no need to set a separate hovering time for each PoI. You only need to hover once according to the longest preset minimum sampling time within the cluster to complete the sampling of all PoIs within the cluster. This reduces the redundant time spent on multiple start-stop hovering operations and helps to reduce the energy consumption of the drone.

[0198] In one embodiment, such as Figure 10 As shown, calculating the duration of communication interruption between the drone and the communication node based on path loss can include the following steps: S2082, based on path loss, calculates the cumulative time for establishing a communication connection between the UAV and the communication node.

[0199] S2084, calculates the duration of the communication interruption between the drone and the communication node based on the cumulative duration and the total duration.

[0200] The cumulative communication connection duration adopts the logic of "path loss - connection determination" without any additional empirical settings. The core of connection determination is the quantified average path loss, and the cumulative duration is the time dimension integration of the determination result. This ensures that the entire technical chain of "path loss calculation, connection determination, duration accumulation, and interruption duration derivation" is rigorous and avoids calculation errors caused by ambiguity in intermediate links.

[0201] In one embodiment, the weighted value of the communication interruption duration is equal to the product of the communication interruption duration and a preset weighting parameter.

[0202] By introducing a weighted parameter α to balance the information acquisition task time and communication interruption time, a Min-Max optimization model is constructed to minimize the maximum task time of a multi-UAV swarm.

[0203] In one embodiment, the trajectories of all UAVs are initialized to obtain a balanced feasible solution; the GVNS (General Variable Neighborhood Search) algorithm is used to solve for the Min-Max task time and corresponding trajectory; the trajectory and hovering point of the UAV with the longest task time are fixed, and the steps of the GVNS algorithm are repeated for the remaining UAVs until the trajectories of all UAVs are assigned.

[0204] Figure 11 This is a structural block diagram of the UAV swarm three-dimensional trajectory planning device 400 provided in an embodiment of the present invention. Figure 11 As shown, the UAV swarm three-dimensional trajectory planning device 400 includes a first processing unit 410, a second processing unit 420, a three-dimensional trajectory forming unit 430, a time calculation unit 440, a three-dimensional trajectory optimization unit 450, and a flight speed optimization unit 460.

[0205] The first processing unit 410 is used to acquire data collected by multiple drones in a drone swarm from multiple information collection points within the information area to be collected. The sampling accuracy and sampling time of each drone in the drone swarm are different. The collected data includes data from each information collection point collected when the sampling accuracy of the drone is greater than or equal to the preset sampling accuracy requirement and the sampling time of the drone is greater than or equal to the preset minimum sampling time.

[0206] In one embodiment, the number and location of information collection points within the area to be collected are obtained, and the preset sampling accuracy requirements and preset minimum sampling time for each information collection point are determined to complete the construction of the information collection point system model; based on the flight altitude of the UAV, the sampling accuracy of the UAV is calculated, and based on the flight altitude and the extreme sampling angle, the radius of the sampling coverage area of ​​the UAV is calculated to complete the construction of the UAV information collection model. In one embodiment, assume the area where information is to be collected (the information collection area) is a square region with a side length of L, distributed with information sources of different types and sampling precisions. For example, sampling high-definition on-site photos requires the drone to be close enough to the target and take several corresponding photos or videos of a certain duration; receiving radar waves allows the drone to be farther away from the signal source, etc. The information sources are defined as multiple points of information (PoIs) with different sampling precisions. For a large area requiring continuous information collection, it can also be discretized into a group of PoIs with the same sampling precision for processing.

[0207] Assuming there are K information collection points within the task area, the th... The ground location of each Point of Interest (PoI) is represented by the following vector: .

[0208] in, It is the first Ground location vectors of each PoI The vector used to represent the ground position is a 1-row, 2-column real number vector.

[0209] Introduction Indicates the first Minimum sampling accuracy requirement per Point of Interest (PoI). If the drone's sampling accuracy... exist time less than Then the first A PoI in At any given moment, sampling cannot be successfully completed. Furthermore, each data collection point has a corresponding minimum sampling time. That is, the drone maintains its position on the first The sampling time for each PoI is no less than Only when all information collection for the Point of Interest (PoI) is complete is the collection considered finished. Furthermore, to ensure the quality of the sampled information, the following definition is defined: express The maximum blur produced by the drone must not exceed a certain value. (Second preset threshold).

[0210] The first condition is true if and only if the following condition is met. Each PoI is considered to be in Successful sampling at any time: The sampling accuracy of the UAV is no less than the first... The minimum sampling accuracy requirement for each Point of Interest (PoI), i.e. The sampling time for drones shall not be less than the first... Minimum sampling time for each PoI Only then is it considered complete. Information collection task for each Point of Interest (PoI); the ambiguity of the drone does not exceed the set upper limit (second preset threshold), i.e. .

[0211] in and These are related to the drone's spatial location and flight speed, respectively.

[0212] In one embodiment, the spatial position vector of the UAV at time t can be represented as: .

[0213] in, This represents a real number vector with 1 row and 3 columns representing the spatial location vector.

[0214] The projection position of the spatial position vector is And the corresponding flight altitude is Assume the drone's data acquisition equipment is perpendicular to the ground and has a limiting acquisition angle. Therefore, when the sampling accuracy of the drone meets the sampling accuracy requirements of PoI, the drone can collect PoI information within a circular area centered on the ground projection position.

[0215] The ground area that the drone's sensing device can perceive at time t can be approximated by [formula missing]. The central circular area, radius It is given by the following equation (1.1): .

[0216] For UAV information collection, sampling accuracy is defined as the limit of the clarity of the ground Point of Interest (PoI) perceived by the UAV's sensing device. This invention uses Ground Sample Distance (GSD) to quantify the resolution of the UAV's sensing device. Taking image information collection as an example, GSD is defined as the ground distance length corresponding to one pixel in the image. One pixel corresponds to... The ground area. Generally speaking, the higher the GSD value, the lower the image resolution. Equivalent to the reciprocal of GSD, this invention quantizes the sampling precision of all information to 1 / GSD, then It can be obtained from the following formula (1.2): .

[0217] in This represents the perception accuracy coefficient of the drone. The actual perception accuracy decreases as the drone's flight altitude increases.

[0218] In one embodiment, a binary index function is introduced based on the information collection point system model and the UAV information collection model. ,when When, it indicates the first A PoI in The drone must successfully sample the data at all times, otherwise... The value indicates that sampling was unsuccessful. According to formulas (1.1) and (1.2), it can be expressed as formula (1.3): .

[0219] Here, "if" means if, and "otherwise" means otherwise. It represents the Euclidean norm between two vectors (which can be understood as the distance between two points).

[0220] The flight path of a single drone is as follows: Where T is the total flight time of the drone. If the drone is to successfully complete the [flight duration]... For each PoI information collection task, formula (1.4) must be satisfied: .

[0221] in, A binary indicator function to indicate whether sampling was successful. The minimum sampling time is T, and the total flight time of the UAV is T.

[0222] The second processing unit 420 is used to acquire multiple communication nodes in the area to be collected. Based on the elevation angle between the UAV and the communication node, the distance between the UAV and the communication node, and the additional path consumption, it calculates the path loss of the channel between the UAV and the communication node. If the path loss is less than a first preset threshold, it determines that a communication connection is established between the UAV and the communication node.

[0223] In one embodiment, the sampling accuracy is calculated based on the flight altitude, and the radius of the sampling coverage area is calculated based on the flight altitude and the extreme sampling angle according to the second calculation formula, so as to complete the construction of the UAV information collection model.

[0224] In one embodiment, if a communication link can be established, multiple drones are dispatched to collaboratively monitor the identified Point of Interest (PoI) and upload monitoring video via the nearest base station. This assumes drones can be dispatched from different rescue centers. Drones are being deployed for collaborative monitoring of disaster areas. In different scenarios, uneven distribution of ground communication infrastructure, equipment alarms, and base station damage can lead to communication vulnerabilities in the region. To support flight safety and the uploading of collected information, drones should minimize communication vulnerabilities and maximize their connection time with the network.

[0225] Assuming there are areas in the region where information is to be collected Each ground base station maintains communication capabilities. Without loss of generality, the drone's departure base is considered, in effect, as a normally functioning base station. The location of each base station is determined by It means that among them Indicates the location of the departure base.

[0226] The channel between the UAV and the ground base station is modeled as a LoS (Line-of-Sight) / NLoS (Non-Line-of-Sight) probabilistic channel.

[0227] Specifically, the drones and the first The probability that the channel between the base stations is controlled by the LoS link satisfies formula (1.5): .

[0228] in, yes Time of the first drones and the first The elevation angles between the ground base stations, a and b, are environmentally relevant parameters that can be obtained from real-world experience. The elevation angle is related to the UAV's trajectory and satisfies formula (1.6): .

[0229] in, yes Time of the first drones and the first The elevation angle between the ground base stations Let represent the flight altitude of the m-th UAV at time t. Let represent the ground projection position vector of the m-th UAV at time t. Indicates the first Each base station's ground location vector. It represents the Euclidean norm between two vectors (which can be understood as the distance between two points).

[0230] exist At that moment, if the first drones and the first If the path loss between the base stations is a Line-of-Sight (LoS) channel, then the path loss satisfies formula (1.7): .

[0231] in, It is the first drones and the first The distance between base stations This represents the operating frequency, and c represents the speed of light. This is the additional path loss for the Loss of Stream (LoS) channel.

[0232] Under NLoS (Non-Line-of-Sight) conditions, the channel typically experiences greater path loss, the difference of which is normalized to a constant. The path loss of the NLoS channel satisfies formula (1.8): .

[0233] Then, in Time of the first drones and the first The average path loss between the base stations satisfies formula (1.9): .

[0234] in, .

[0235] Assuming the average path loss is lower than a first preset threshold Then in At any time, it can be in the first drones and the first Communication is established between the base stations. Furthermore, if the first... A drone is considered to have successfully connected to the rescue network if it can communicate with at least one base station. This is achieved through binary indicator functions. Definition of the first The connection status of the drones can be expressed by formula (1.10). .

[0236] Here, "if" means "if" and "otherwise" means "otherwise". This represents the first preset threshold. Indicates the first drones and the first The average path loss of the channel between base stations.

[0237] In one embodiment, it is assumed that the aircraft are dispatched from the same or different initial bases. A drone was deployed to collaboratively monitor monitoring points within the disaster area. Among them, the first... The position vector of the UAV at time t is represented as: .

[0238] in, This is used to represent a position vector as a 3x3 real number vector.

[0239] No. The coverage area of ​​the drone satisfies formula (1.11): .

[0240] No. The sampling accuracy of the UAV satisfies formula (1.12): .

[0241] Define binary pointer functions If the first Each monitoring target point (information sampling point) at time by If a UAV (Unmanned Aerial Vehicle) successfully monitors the situation, then... ,otherwise Mathematically, the above can be represented by formula (1.13): .

[0242] Here, "if" means if, and "otherwise" means otherwise.

[0243] If the total information collection time exceeds a certain threshold, it can be considered that enough monitoring video data has been collected. Therefore, for the [missing information]... For each Point of Interest (PoI), successful information collection requires satisfying formula (1.14): .

[0244] in It is the first The information collection mission time of the drone includes the total time it takes for the drone to return to the base after completing all information collection tasks.

[0245] On the other hand, in order to receive control information and transmit it back in a timely manner, the drone needs to operate within a certain time frame. Maintaining communication with the base station during this period is equivalent to minimizing communication interruption time. If the drone is within effective communication range of any base station, a communication connection can be established. Therefore, the duration of the communication interruption is... It satisfies formula (1.15): .

[0246] In summary, the problem of designing the three-dimensional trajectory of a multi-target UAV swarm can be expressed as the following set of equations or formulas: .

[0247] Here, "P1" represents question 1. "st" indicates subject to. This is a weighted parameter between monitoring time and communication interruption time. The constraints sequentially ensure that all Points of Interest (PoIs) meet sampling accuracy requirements and guarantee sufficient data collection time; the UAV is constrained by its maximum flight speed. ;No. The drone's initial takeoff position and return termination position are the same, based on the corresponding base location. .

[0248] This represents the real-time velocity (with orientation) of the m-th UAV with a direction vector. The Euclidean norm of the velocity vector (i.e., the magnitude of the velocity vector) represents the real-time velocity of the m-th UAV.

[0249] The three-dimensional trajectory forming unit 430 is used to group the collected data of multiple information collection points according to the spatial location of multiple information collection points and the preset sampling accuracy requirements to obtain multiple data clusters. Based on the maximum value of multiple preset sampling accuracy requirements in each data cluster, the flight altitude of the UAV and the cluster center are determined. Based on the flight altitude and the cluster center, the initial three-dimensional trajectory of the UAV visiting all information collection points in the data cluster is formed, where the cluster center is the hovering position of the UAV at the flight altitude.

[0250] In one embodiment, considering the coverage area of ​​the UAV's sensing device, information from multiple Points of Interest (PoIs) can be collected simultaneously. Therefore, the information collection points can be grouped into multiple clusters (data clusters), and all PoIs within the same cluster can be successfully sampled by the UAV simultaneously, aiming to minimize the number of clusters required to cover all PoIs. However, since the area covered by the UAV is directly determined by its flight altitude, and the flight altitude is also related to the sampling accuracy requirements of each PoI, this invention employs a PoI clustering algorithm with heterogeneous sampling accuracy.

[0251] Assuming this Information collection points can be divided into The clusters, where the _ is the _ cluster .... Clusters are based on Centered on, and with height parameters Related.

[0252] in, The position vector used to represent the cluster center is a 1-row, 2-column real number vector.

[0253] According to formulas (1.11) and (1.12), if the drone hovers at a certain altitude... The first The center of each cluster Above, the camera coverage area is And the corresponding sampling precision is If the first A PoI can be located at a height of Cluster center If the drone above successfully samples, then it satisfies formula (1.16): .

[0254] Introduction Indicates the first Given a set of PoI indices for a cluster, minimizing the number of clusters can be equivalent to the following system of equations or formulas: .

[0255] Here, "P2" represents question 2. "st" indicates subject to. It means "for all those belonging to the first" The first cluster of the th cluster "One PoI". It represents the union of sets.

[0256] Note that the UAV sampling accuracy constraint can be converted into the UAV's flight altitude constraint. This further determines the first The coverage of each cluster. Therefore, "P2" can be relaxed and transformed into a clustering problem in two-dimensional space, as shown below: .

[0257] Here, "P2a" represents problem 2 after the transformation. Solving P2a yields the cluster center. and the corresponding set of PoIs for drones Then, the height parameter of each cluster center can be set to meet the sampling accuracy requirements of all PoIs belonging to that cluster. The height parameter of each cluster center satisfies formula (1.17): .

[0258] Initialize the uncovered PoI point set to Initialize the number of clusters to .

[0259] exist Find the Point of Interest (PoI) with the highest sampling accuracy requirement, denoted as... And generate a cluster to cover the first One Point of Interest (PoI). The hovering point height of the drone corresponding to this cluster is set to... .because This is the flight altitude with the highest sampling accuracy requirements, so if other Points of Interest (PoIs) are included in the cluster, information can also be successfully collected. Then, the cluster center... The following optimizations can be used to cover the first... One and as many PoIs as possible. Cluster centers It satisfies formula (1.18): ...

[0260] Equation (1.18) can be solved using convex optimization. The information collection points covered by this cluster can be represented as: .

[0261] Then update the number of clusters and the set. , .

[0262] Repeat the above until This can be understood as: up to the set of uncovered PoIs. It becomes an empty set.

[0263] In one embodiment, the code for the PoI clustering algorithm may include the following: 1: Input: ; 2: Output: and ; 3: Initialization: and ; 4: While do; 5: ; 6: Find ; 7: Solving (1.18) yields ; 8: ; 9: Update ; 10: Return and ; 11: End while; 12: Return .

[0264] In one embodiment, the cluster center locations are further optimized based on the PoI clustering algorithm to reduce communication interruption time for the UAV. Since the cluster centers are equivalent to the UAV's hovering positions, it is desirable to place these cluster centers within the effective communication range of the ground base station. Note that the hovering points obtained from P2a are not the only locations where all PoI information within the corresponding cluster can be successfully collected. Indicates the first A sub-region of a cluster, which can successfully cover all PoI sets belonging to that cluster. To meet the resolution requirements of all Points of Interest (PoIs) within the cluster, sub-regions The height must not be greater than Assuming The altitude is fixed at ,use express The projection onto the ground satisfies formula (1.19): .

[0265] subregion It is projected onto The projection of the point set satisfies formula (1.20): .

[0266] When the drone is located in the sub-region Within this time, it can successfully sample the part belonging to the first... All PoIs of each cluster. It is easy to prove that... It is a convex region.

[0267] If hovering points located outside the effective communication coverage area of ​​a base station can be moved to the coverage area, communication downtime for drones can be effectively reduced. The goal is to maximize the number of hovering locations within the communication coverage area while minimizing the number of clusters. Based on the hovering point of P2a, this problem can be expressed as the following formula or system of equations: .

[0268] in, It is the first Base stations and 3D location Path loss between drones at the location.

[0269] Since the effective communication range is a convex region with a defined hovering height, P3 can be solved through efficient convex optimization. This strategy involves determining the range of each cluster center. Corresponding convex region And find a new hovering point in the convex region that is closest to the center of one of the base stations. After resolving P3, more drone hovering positions were moved into the effective communication range of the base station, which significantly reduced drone communication downtime.

[0270] The time calculation unit 440 is used to calculate the information acquisition task time of the UAV based on the sampling accuracy of the UAV, the sampling time of the UAV, the preset sampling accuracy requirement and the preset minimum sampling time, and to calculate the duration of communication interruption based on path loss.

[0271] In one embodiment, the information collection task time is equal to the sum of the UAV's flight time between clusters and the UAV's hovering sampling time within a cluster, where the hovering sampling time is the maximum value of the minimum sampling time for all information collection points in the cluster.

[0272] The 3D trajectory optimization unit 450 is used to optimize the initial 3D trajectory and obtain the optimized 3D trajectory based on the optimization model with the goal of minimizing the total mission time of the UAV swarm. The total mission time is equal to the weighted sum of the information acquisition mission time and the communication interruption duration.

[0273] In one embodiment, the present invention employs the General Variable Neighborhood Search (GVNS) algorithm to solve the MTSP multiple traveling salesman problem.

[0274] In one specific embodiment, the optimization model is a Min-Max optimization model.

[0275] After determining the clusters with height parameters, the multi-UAV trajectory design for checking all PoIs involves sequentially visiting the center of each cluster and hovering over it for sufficient time to ensure that each PoI within the cluster is successfully sampled. The set of hovering points is: .

[0276] make Indicates the first The hovering point that the drone wants to visit. Furthermore, the... The flight trajectory of a drone can be described by the following vector: .

[0277] While the drone flies between two hovering points, it does not perform any sampling, thus allowing it to fly at maximum speed. Upon reaching a hovering point, the drone will remain there long enough to ensure successful sampling and information collection. Indicates that the drone is hovering. The shortest sampling time for location. The total task time for information collection by a single UAV satisfies formula (1.21): .

[0278] in, It is a set The first in The nth element. The first part of formula (1.21) is the nth element. The first part is the flight time between the hovering points of the UAVs, and the second part is the hovering time of the current cluster, which is related to the PoI with the longest sampling time requirement within the cluster. The hovering time of the current cluster satisfies formula (1.22): .

[0279] Drone communication interruption time It can be obtained by solving formulas (1.15), (1.21), and (1.22). Obtained through calculation.

[0280] For ease of definition, let The total mission time is defined as the weighted sum of the UAV sampling time and the communication terminal time. The minimum mission time for all UAVs can be programmed as a Min-Max problem. The minimum mission time for all UAVs satisfies formula (1.23): .

[0281] The Min-Max Multiple Traveling Salesman Problem (P4) has been proven to be NP-hard and cannot be solved in polynomial time. This invention employs an efficient heuristic algorithm to solve P4: the General Variable Neighborhood Search (GVNS) algorithm can be used to minimize the maximum monitoring time of all drones. When the number of drones is reduced to 1, the problem degenerates into the traditional Traveling Salesman Problem (TSP).

[0282] Assuming the initial position of the drone, i.e. All drones are located at the base. Different initialization strategies are used depending on whether all drones originate from the same base. Specifically, consider the following two scenarios: Scenario 1: If all drones depart from the same location, their flight paths will be initially balanced based on flight distance; that is, hovering points will be added one after another to the drone with the minimum weighted travel length. to indicate The distance matrix, Indicates the first Drone journey The last specified node. This is the current average inspection time.

[0283] Scenario 2: If the drone departs from different locations, the strategy will be initialized based on the area division, that is, hovering points will be assigned to the trip to the nearest initial location.

[0284] In one embodiment, the code for the initialization algorithm may include the following: 1: Input: and ; 2: Output: ; 3: Case 1: Same initial position; 4: According to Reordering ; 5: For do; 6: ; 7: ; 8: ; 9: ; 10: End for; 11: For do; 12: ; 13: ; 14: ; 15: ; 16: Update ; 17: End for; 18: Case 2: Different initial positions; 19: For do; 20: ; twenty one: ; 22: End for; 23: Calculate using formulas (1.15) and (1.21) ; 23: Return .

[0285] In one embodiment, the GVNS algorithm is used to improve the UAV trajectory. The pseudocode for the GVNS algorithm is given in Algorithm 4. It is the maximum number of neighborhood steps in Shaking to generate new possible solutions in a finite neighborhood. It is the maximum number of different neighborhood structures in SeqVND, which ensures that every possible solution can be optimized through these neighborhood structures to minimize the target time. This is the maximum allowed number of iterations in the process. If the objective function does not decrease after the second iteration, it is considered converged, and the iteration terminates. The Shaking (perturbation operation) and SeqVND (Sequential Variable Neighborhood Descent) algorithms are introduced in Algorithm 5 and Algorithm 6, respectively.

[0286] In one embodiment, the code for the GVNS algorithm (Algorithm 4) may include the following: 1: Input: ; 2: Output: ;in, See Algorithm 7 below for details; 3: ; 4: While do; 5: ; 6: ; 7: While do; 8: ; 9: ; 10: ; 11: If then; 12: ; 13: ; 14: ; 15: ; 16: else; 17: ; 18: End if; 19: End while; 20: ; 22: End while; 22: Return .

[0287] In one embodiment, the code for the Shaking algorithm (perturbation operation algorithm, Algorithm 5) may include the following: 1: Input: ; 2: Output: ; 3: While do; 4: ; 5: ; 6: ; 7: ; 8: ; 9: ; 10: ; 11: End while; 12: Return .

[0288] In one embodiment, the code for the SeqVND algorithm (Sequence-Variable Neighborhood Descent Algorithm, Algorithm 6) may include the following: 1: Input: ; 2: Output: ; 3: ; 4: While do; 5: If then; 6: ; 7: End if; 8: If then; 9: ; 10: End if; 11: If then; 12: ; 13: End if; 14: If then; 15: ; 16: End if; 17: If then; 18: ; 19: End if; 20: Calculate the track using formulas (1.15) and (1.21) Weighted time ; 21: If then; twenty two: ; 23: Calculate the track using formulas (1.15) and (1.21) Weighted time ; twenty four: ; 25: ; 26: else; 27: ; 28: End if; 29: End while; 30: Return .

[0289] In one embodiment, the neighborhood search method obtains the optimal solution by traversing a finite neighborhood. Five neighborhood search methods are used: one_move (single-node movement operation), two_move (two-node movement operation), two_exchange (two-node exchange operation), three_move (three-node movement operation), and three_exchange (three-node exchange operation), which are used for hovering point adjustment between different UAV flight paths. In addition, two_opt (two optimization algorithms) is applied to optimize the hovering point access order of a specific UAV flight path.

[0290] one_move: This strategy repositions the selected hover point to a new location on another drone's flight path, where the selected hover point belongs to the drone's flight path with the longest monitoring time. This neighborhood search has a solution set containing all possible cases. There must be a usable solution among them. Its maximum monitoring time meets The numbers in the solution set indices represent the range of the neighborhood search, i.e. This represents all possible neighborhood sets of only one element. Since the number of hover points for all drones is... Therefore, for a given solution, the computational complexity of this strategy is... Furthermore, the computational complexity of the next four neighborhood search methods is... .

[0291] two_move: This strategy is similar to one_move, but this method will have the longest monitoring time. Two adjacent hovering points of one drone's flight path are repositioned into the flight path of another drone. The two selected hovering points are treated as a single block without changing the order of access. The neighborhood search solution set is... including those with The optimal solution .

[0292] `two_exchange`: This strategy swaps two selected hover points, one belonging to the drone track with the longest monitoring time and the other drone track. This strategy works by traversing all possible solution sets. and find those with optimal solution .

[0293] three_move: This strategy is similar to one_move, but this method will have the longest monitoring time. Two adjacent hovering points of one drone's flight path are repositioned into the flight path of another drone. The two selected hovering points are treated as a single block without changing the order of access. The neighborhood search solution set is... including those with The optimal solution .

[0294] `three_exchange`: This strategy swaps three selected hover points, including two adjacent hover points from the UAV track with the longest monitoring time and one hover point from another UAV track. This method treats two adjacent hover points as a block without changing their order and swaps this block with another selected hover point using the `two_exchange` strategy. The neighborhood search solution set is... including those with The optimal solution .

[0295] two_opt: This method solves the hovering point access order of a single drone to minimize the specific drone monitoring time, which can be defined as the Traveling Salesman Problem (TSP).

[0296] In practice, while minimizing the maximum information sampling time, it may also be necessary to minimize the total energy consumption of the drones, which is proportional to the sum of the total sampling times of all drones. Therefore, the method of repeatedly calling GVNS to sequentially optimize the flight trajectory of each UAV is proposed in this section. A detailed description of the multi-UAV trajectory planning using repeated GVNS is as follows: Algorithm 3 is used to initialize the trajectory of each drone in order to find a set of feasible solutions in which the task time of each drone is relatively balanced.

[0297] Algorithm 4 is used to optimize this set of feasible solutions, and the Min-Max task time and the corresponding UAV flight trajectory are obtained.

[0298] Maintain the flight path of the drone with the longest mission time and remove the corresponding hover point. Then repeat Algorithm 4 for the remaining drones until all drone flight paths and corresponding hover points are fully assigned.

[0299] Algorithm 7 summarizes the pseudocode for obtaining all UAV trajectories by repeatedly using GVNS. The complexity of GVNS is... ,in This represents the total number of hovering points after clustering. The complexity of the proposed algorithm is no greater than that of GVNS. The problem size decreases as the number of iterations increases, because the problem size decreases.

[0300] In one embodiment, the code that repeats the GVNS algorithm may include the following: 1: Input: ; 2: Output: ; 3: ; 4: ; 5: While do; 6: ; 7: Find ; 8: ; 9: ; 10: ; 11: End while; 12: Return .

[0301] Based on the above algorithm, a three-dimensional flight trajectory of a drone swarm can be obtained (e.g., Figure 3 (As shown). Points represent PoI locations, circles of varying radii represent the clustering of each PoI, and broken lines represent flight trajectories.

[0302] The flight speed optimization unit 460 is used to discretize the optimized three-dimensional trajectory into multiple trajectory segments according to a preset length, and optimize the flight speed of the UAV in each trajectory segment based on a linear programming model.

[0303] The flight trajectory of the UAV is discretized into several trajectory segments of a preset length, and the center position of each trajectory segment is extracted as the basis for sampling and communication judgment. A linear programming model is established, and the constraints may include: the effective sampling time of each information collection point is not shortened and the flight speed of the UAV does not exceed the maximum speed. The linear programming model is solved to obtain the optimized flight speed of each trajectory segment.

[0304] In one embodiment, the drone flies at its maximum speed. During flight, Points of Interest (PoIs) can be successfully sampled within the required sampling accuracy range without needing to hover. Therefore, by optimizing the drone's flight speed, the mission time can be further reduced. Since the flight path of each drone has already been determined, speed optimization for a single drone is only considered.

[0305] Record all flight node positions of the drone as a set. The sampled PoI set is , No. The time for each PoI to be successfully sampled is Discretize its trajectory into several segments of length . .when When the time is very small, the time for each discrete trajectory segment It is also very small, equivalent to the instantaneous velocity of the drone. The center position of each discrete trajectory segment is extracted. This serves as the basis for determining whether the drone can successfully sample Points of Interest (PoIs) and establish a valid communication connection within that trajectory segment.

[0306] The center position of the discrete trajectory segment is: .

[0307] Formula (1.24) is: .

[0308] Formula (1.25) is: .

[0309] Formula (1.26) is: .

[0310] Formula (1.27) is: .

[0311] Binary indicator function It can be done Substitution Transform it into a discrete binary index function. To reasonably discretize the trajectory between every two nodes, let... Indicates the first The number of segmented trajectory segments is obtained by rounding up the trajectory length / discrete segment length to get the number of segments. Segment trajectory This allows us to obtain the total number of discrete segments. .

[0312] Therefore, the drone speed optimization problem can be equivalently represented by the following system of equations or formulas: .

[0313] According to the constraints in P5, the effective sampling time for each PoI by the UAV must not be shorter than the original successful sampling time, and the UAV is constrained by its maximum flight speed. This problem is actually a linear programming problem, which can be solved efficiently. Therefore, the flight speed of each UAV can be optimized using this method, ensuring the successful completion of all information collection tasks while reducing the task time.

[0314] This invention aims to provide a three-dimensional trajectory planning device 400 for unmanned aerial vehicle (UAV) swarms. Firstly, unlike traditional two-dimensional UAV flight trajectory design with a fixed flight altitude, this invention considers both sensory information acquisition and information feedback under effective communication connectivity. It combines the UAV's flight altitude with communication quality and sampling accuracy, enabling the optimization of the UAV swarm's three-dimensional flight trajectory to improve the quality of UAV communication and sensing, thereby increasing the efficiency of information acquisition and the accuracy of sampling results. Secondly, this invention employs a Point of Interest (PoI) system. The PoI (Point of Information) clustering algorithm can group all information collection points within a region into a minimum number of data clusters based on their spatial location and preset sampling accuracy requirements. Unlike traditional clustering methods, it determines the drone's flight altitude and cluster center based on the maximum value of multiple preset sampling accuracy requirements within each data cluster. The PoI clustering algorithm can optimize the location of the cluster center, ensuring that the drone maintains effective communication connectivity as much as possible when flying to the cluster center. Thirdly, for information collection points with different preset sampling accuracy requirements within the area to be collected, it plans the three-dimensional flight trajectories of all drones, minimizing the time required to complete the information collection task and minimizing the communication connection interruption time. By optimizing the drone's flight speed, it is beneficial to further reduce the total task time (total flight time) of the drone swarm.

[0315] The technical solution of this invention can optimize the three-dimensional trajectory and minimize the total mission time of the UAV swarm while ensuring the accuracy of the sampling results, thereby improving the efficiency of information collection.

[0316] Figure 12 A schematic diagram of the hardware structure of the electronic device 300 provided in an embodiment of the present invention is shown.

[0317] Electronic device 300 may include processor 301 and memory 302 storing computer program instructions.

[0318] Specifically, the processor 301 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of the present invention.

[0319] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be non-volatile solid-state memory. Memory 302 may be internal or external to the integrated gateway disaster recovery device.

[0320] In one instance, memory 302 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0321] The processor 301 reads and executes computer program instructions stored in the memory 302 to achieve... Figure 2 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 and Figure 10 The embodiment shown illustrates a method for planning the three-dimensional trajectory of a drone swarm.

[0322] In one example, the electronic device 300 may also include a communication interface 303 and a bus 304. For example, Figure 12 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 304 and complete communication with each other.

[0323] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of the present invention.

[0324] Bus 304 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 304 may include one or more buses. While specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0325] Electronic devices can achieve Figure 2 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 and Figure 10 The embodiment shown illustrates a method for planning the three-dimensional trajectory of a drone swarm.

[0326] Furthermore, in conjunction with the UAV swarm three-dimensional trajectory planning method in the above embodiments, this invention can be implemented using a computer storage medium. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the UAV swarm three-dimensional trajectory planning methods described in the above embodiments.

[0327] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the UAV swarm three-dimensional trajectory planning methods described in the above embodiments.

[0328] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0329] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0330] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0331] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0332] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for three-dimensional trajectory planning of a UAV swarm, characterized in that, The method comprises the following steps: acquiring collection data of multiple information collection points in a to-be-collected information area by multiple unmanned aerial vehicles in an unmanned aerial vehicle group, wherein the sampling accuracy and sampling time of each unmanned aerial vehicle in the unmanned aerial vehicle group are different, and the collection data comprises data of each information collection point collected by an unmanned aerial vehicle whose sampling accuracy is greater than or equal to a preset sampling accuracy requirement and whose sampling time is greater than or equal to a preset minimum sampling time; acquiring multiple communication nodes in the to-be-collected information area, calculating path loss of a channel between the unmanned aerial vehicle and the communication node based on an elevation angle between the unmanned aerial vehicle and the communication node, a distance between the unmanned aerial vehicle and the communication node and additional path consumption, and determining that a communication connection is established between the unmanned aerial vehicle and the communication node when the path loss is less than a first preset threshold; grouping the collection data of the multiple information collection points according to spatial positions of the multiple information collection points and the preset sampling accuracy requirement, obtaining multiple data clusters, determining a flight height of the unmanned aerial vehicle and a cluster center according to a maximum value of multiple preset sampling accuracy requirements in each data cluster, and forming an initial three-dimensional trajectory of the unmanned aerial vehicle accessing all the information collection points in the data cluster based on the flight height and the cluster center, wherein the cluster center is a hovering position of the unmanned aerial vehicle at the flight height; calculating an information collection task time of the unmanned aerial vehicle based on the sampling accuracy of the unmanned aerial vehicle, the sampling time of the unmanned aerial vehicle, the preset sampling accuracy requirement and the preset minimum sampling time, and calculating a communication interruption duration between the unmanned aerial vehicle and the communication node based on the path loss; optimizing the initial three-dimensional trajectory based on an optimization model to minimize a total task time of the unmanned aerial vehicle group, and obtaining an optimized three-dimensional trajectory, wherein the total task time is equal to a sum of weighted values of the information collection task time and the communication interruption duration; discretizing the optimized three-dimensional trajectory into multiple trajectory segments according to a preset length, and optimizing a flight speed of the unmanned aerial vehicle in each trajectory segment based on a linear programming model. 2.The method of claim 1, wherein, The method of acquiring the collection data of the multiple information collection points in the to-be-collected information area by the multiple unmanned aerial vehicles in the unmanned aerial vehicle group comprises the following steps: respectively acquiring a preset sampling accuracy requirement and a preset minimum sampling time of the multiple information collection points in the to-be-collected information area, wherein the preset sampling accuracy requirement is a minimum sampling accuracy requirement of each information collection point; calculating the sampling accuracy of the unmanned aerial vehicle based on the flight height of the unmanned aerial vehicle, and calculating a radius of a sampling coverage range of the unmanned aerial vehicle based on the flight height and a limit collection angle; determining that data collected by the unmanned aerial vehicle on the information collection points in the sampling coverage range is the collection data when the sampling accuracy of the unmanned aerial vehicle is greater than or equal to the preset sampling accuracy requirement and the sampling time of the unmanned aerial vehicle is greater than or equal to the preset minimum sampling time. 3.The method of claim 2, wherein, The flight height based on the unmanned aerial vehicle is used to calculate the sampling accuracy of the unmanned aerial vehicle, and the flight height and the limit collection angle are used to calculate the radius of the sampling coverage range of the unmanned aerial vehicle, comprising: The flight height based on the unmanned aerial vehicle is used to calculate the sampling accuracy of the unmanned aerial vehicle, and the flight height and the limit collection angle are used to calculate the radius of the sampling coverage range of the unmanned aerial vehicle, comprising: The first calculation formula is: wherein, represents a sampling accuracy of the UAV at time t, represents a flight height of the UAV at time t, represents a perception accuracy coefficient of the UAV; The second calculation formula is: wherein, represents a radius of the sampling coverage range at time t, represents a flight height of the unmanned aerial vehicle at time t, represents the limit collection angle. 4.The method of claim 1, wherein, The acquisition of the plurality of communication nodes in the to-be-collected information region, based on the elevation angle between the unmanned aerial vehicle and the communication node, the distance between the unmanned aerial vehicle and the communication node and the additional path consumption, the path loss of the channel between the unmanned aerial vehicle and the communication node is calculated, and the communication connection between the unmanned aerial vehicle and the communication node is established in the case that the path loss is less than the first preset threshold, comprising: The number and position of the communication nodes in the to-be-collected information region are acquired, the communication nodes include base stations, and the base stations include base stations at the departure place of the unmanned aerial vehicle; based on the elevation angle between the unmanned aerial vehicle and the communication node, the distance between the unmanned aerial vehicle and the communication node and the additional path consumption, the first probability and the first path loss of the line-of-sight channel and the second probability and the second path loss of the non-line-of-sight channel between the unmanned aerial vehicle and the communication node are calculated, and the average path loss is calculated based on the first probability, the first path loss, the second probability and the second path loss; In the case that the average path loss is less than the first preset threshold, the communication connection between the unmanned aerial vehicle and the communication node is established. 5.The method of claim 4, wherein, The first probability and the first path loss of the line-of-sight channel and the second probability and the second path loss of the non-line-of-sight channel between the unmanned aerial vehicle and the communication node are calculated based on the elevation angle between the unmanned aerial vehicle and the communication node, the distance between the unmanned aerial vehicle and the communication node and the additional path consumption, comprising: The first probability of the line-of-sight channel between the unmanned aerial vehicle and the communication node is calculated based on the third calculation formula according to the elevation angle between the unmanned aerial vehicle and the communication node, the preset first environmental parameter and the preset second environmental parameter; The first path loss of the line-of-sight channel between the unmanned aerial vehicle and the communication node is calculated based on the fourth calculation formula according to the distance between the unmanned aerial vehicle and the communication node, the working frequency, the speed of light and the additional path consumption of the line-of-sight channel; The second probability of the non-line-of-sight channel between the unmanned aerial vehicle and the communication node is calculated according to the first probability, wherein the sum of the first probability and the second probability is 1; The second path consumption of the non-line-of-sight channel between the unmanned aerial vehicle and the communication node is calculated according to the first path loss; The third calculation formula is: wherein, denotes the first probability that the channel between the mth unmanned aerial vehicle and the yth communication node at time t is the line-of-sight channel, denotes the elevation angle between the mth unmanned aerial vehicle and the yth communication node at time t, a is the preset first environmental parameter, and b is the preset second environmental parameter; The fourth calculation formula is: wherein, denotes the first path loss of the line-of-sight channel between the mth UAV and the yth communication node at time t; denotes the distance between the mth UAV and the yth communication node at time t, denotes the operating frequency, and c denotes the speed of light, denotes the additional path loss of the line-of-sight channel. 6.The method of claim 5, wherein, The average path loss is calculated based on the first probability, the first path loss, the second probability and the second path loss, comprising: calculating the average path loss based on the first probability, the first path loss, the second probability, and the second path loss according to a fifth calculation formula; the fifth calculation formula is: wherein, denotes the average path loss of the channel between the mth UAV and the yth communication node at time t, denotes the first probability that the channel between the mth UAV and the yth communication node at time t is the line-of-sight channel, denotes the first path loss of the channel between the mth UAV and the yth communication node at time t if the channel is the line-of-sight channel, denotes the second probability that the channel between the mth UAV and the yth communication node at time t is the non-line-of-sight channel, denotes the second path loss of the channel between the mth UAV and the yth communication node at time t if the channel is the non-line-of-sight channel. 7.The method of claim 1, wherein, determining the flight height and the cluster center of the UAV based on the maximum value of the plurality of preset sampling accuracy requirements in each data cluster, including: taking the maximum value of the plurality of preset sampling accuracy requirements in each data cluster as the sampling accuracy of the UAV, determining the flight height and the cluster center of the UAV according to the sampling accuracy of the UAV, and determining the cluster radius according to the flight height and the limit collection angle of the UAV. 8.The method of claim 1, wherein, the information collection task time is equal to the sum of the moving flight time of the UAV between a plurality of data clusters and the hovering sampling time of the UAV in the data cluster, and the hovering sampling time is the maximum value of the preset minimum sampling time of all information collection points in the data cluster. 9.The method of claim 1, wherein, calculating the communication interruption duration between the UAV and the communication node based on the path loss, including: calculating the cumulative duration of establishing a communication connection between the UAV and the communication node based on the path loss; calculating the communication interruption duration between the UAV and the communication node based on the cumulative duration and the total duration. 10.The method of claim 1, wherein, The weighted value of the communication interruption duration is equal to the product of the communication interruption duration and a preset weighting parameter. 11.A device for three-dimensional trajectory planning of a UAV swarm, characterized in that, including: a first processing unit configured to obtain collection data of a plurality of information collection points in a to-be-collected information region by a plurality of UAVs in a UAV group, wherein the sampling accuracy and sampling time of each UAV in the UAV group for collecting the information collection points are different, and the collection data includes data of each information collection point collected by a UAV with sampling accuracy greater than or equal to a preset sampling accuracy requirement and sampling time greater than or equal to a preset minimum sampling time; a second processing unit configured to obtain a plurality of communication nodes in the to-be-collected information region, calculate a path loss of a channel between the UAV and the communication node based on an elevation angle between the UAV and the communication node, a distance between the UAV and the communication node, and an additional path loss, and determine that a communication connection is established between the UAV and the communication node when the path loss is less than a first preset threshold; a three-dimensional trajectory forming unit configured to group the collection data of the plurality of information collection points according to spatial positions of the plurality of information collection points and preset sampling accuracy requirements, to obtain a plurality of data clusters, to determine a flight height and a cluster center of the UAV based on a maximum value of a plurality of preset sampling accuracy requirements in each data cluster, and to form an initial three-dimensional trajectory of the UAV accessing all information collection points in the data cluster based on the flight height and the cluster center, wherein the cluster center is a hovering position of the UAV at the flight height. A time calculation unit is configured to calculate an information collection task time of the UAVs based on a sampling precision of the UAVs, a sampling time of the UAVs, a preset sampling precision requirement and a preset minimum sampling time, and calculate a communication interruption duration based on the path loss; A three-dimensional trajectory optimization unit is configured to optimize the initial three-dimensional trajectory based on an optimization model to minimize a total task time of the UAV group, and obtain an optimized three-dimensional trajectory, where the total task time is equal to a sum of weighted values of the information collection task time and the communication interruption duration. A flight speed optimization unit is configured to discretize the optimized three-dimensional trajectory into a plurality of trajectory segments according to a preset length, and optimize a flight speed of the UAVs in each trajectory segment based on a linear programming model.

12. An electronic device, comprising: The processor reads and executes the computer program instructions to implement the three-dimensional trajectory planning method for a UAV group according to any one of claims 1 to 10.

13. A readable storage medium, characterized by, The storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the three-dimensional trajectory planning method for a UAV group according to any one of claims 1 to 10.

14. A computer program product, characterised in that, The computer program program is executed by the processor to implement the three-dimensional trajectory planning method for a UAV group according to any one of claims 1 to 10.

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