Unmanned aerial vehicle data acquisition obstacle avoidance trajectory optimization method based on information age

Abstract

The invention discloses an unmanned aerial vehicle data acquisition obstacle avoidance trajectory optimization method based on information age, and belongs to the technical field of wireless communication, and the method comprises the steps: according to the hovering point position of an unmanned aerial vehicle, each type of sensor selection set and the current data acquisition trajectory of the unmanned aerial vehicle, minimizing the average information age of a user as a target; a preset communication threshold value of the unmanned aerial vehicle and the user and a coverage range and precision requirement of each type of sensor are taken as constraints, an information age minimization problem of data acquired by the user is constructed, and the information age minimization problem is decomposed into an unmanned aerial vehicle hovering point problem, a sensor selection problem and an unmanned aerial vehicle trajectory optimization problem. And respectively obtaining an optimal solution of a hovering point of the unmanned aerial vehicle and an optimal solution of a sensor set participating in data acquisition, optimizing a flight path of the unmanned aerial vehicle, solving an unmanned aerial vehicle path optimization problem, and obtaining an unmanned aerial vehicle data acquisition obstacle avoidance path. The problem that the average information age of a user cannot be effectively reduced when hovering data acquisition is not carried out in a sensor area in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to a method for optimizing the obstacle avoidance trajectory of UAV data collection based on the age of information, belonging to the field of wireless communication technology. Background Art

[0002] In recent years, Internet of Things (IoT) technology has shown great value and potential. In the IoT environment, information collection is the basis for maintaining the normal operation of the system and realizing intelligent decision-making. However, in some special cases, such as natural disasters like earthquakes, floods, and typhoons, users and sensors may be unable to communicate with the base station. Many applications have high requirements for data freshness. To effectively characterize data freshness, the age of information (AoI) is used as a standard to measure data freshness. Unmanned Aerial Vehicles (UAVs), with their high efficiency, flexibility, and precision, have become key tools for data collection and analysis.

[0003] However, in the technical application of UAVs for collecting sensor information, existing methods mainly involve having the UAV hover above the sensor or cluster node for data collection, without considering that the UAV can collect data as long as it is within the effective communication range of the sensor. The mode of collecting data while flying allows the UAV to continuously collect data during movement, thus greatly shortening the total time of data collection. When performing data collection tasks, the UAV may encounter obstacles during flight, and existing research on UAV flight for information collection rarely considers the impact of obstacles. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for optimizing the obstacle avoidance trajectory of UAV data collection based on the age of information. Under the condition that both the user and the sensor are unable to communicate with the base station, a problem of minimizing the age of information for the user to obtain data is constructed to optimize the obstacle avoidance flight trajectory of the UAV, so as to solve the problem that the existing technology cannot effectively reduce the average age of information of the user when there is no hovering data collection in the sensor area.

[0005] To solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] The present invention provides a method for optimizing the obstacle avoidance trajectory of UAV data collection based on the age of information, including:

[0007] Based on the position of the UAV hovering point, the selection set of each type of sensor, and the current data collection trajectory of the UAV, with the goal of minimizing the average age of information of the user, and with the preset communication threshold between the UAV and the user, as well as the coverage range and accuracy requirements of each type of sensor as constraints, a problem of minimizing the age of information for the user to obtain data is constructed;

[0008] Decompose the problem of minimizing the information age of the data obtained by the user into a UAV hovering point problem, a sensor selection problem, and a UAV trajectory optimization problem;

[0009] Optimize the hovering position of the UAV in the user's area and the set of sensors participating in data collection respectively, solve the UAV hovering point problem and the sensor selection problem, and obtain the optimal solution of the UAV hovering point and the optimal solution of the set of sensors participating in data collection;

[0010] Based on the optimal solution of the UAV hovering point and the optimal solution of the set of sensors participating in data collection, optimize the UAV flight trajectory to solve the UAV trajectory optimization problem, and obtain the obstacle avoidance trajectory for UAV data collection.

[0011] Furthermore, it also includes that before constructing the problem of minimizing the information age of the data obtained by the user, based on users, one UAV, and types of sensors, construct a collection system;

[0012] The problem of minimizing the information age of the data obtained by the user is constructed and obtained based on the collection system;

[0013] In the collection system, users are randomly distributed in the first specific area; types of sensors are mixed and randomly distributed in the second specific area, and the number of each type of sensor can completely cover the second specific area; the communication model between the UAV and the sensors is a probabilistic line-of-sight / non-line-of-sight channel model. When the transmission rate between the th sensor of the th type and the UAV is greater than the transmission threshold , it is regarded as effective communication; the UAV communicates with the users in the same group in a broadcast form, only considering the line-of-sight link. The transmission rate between the UAV and the user is greater than the transmission threshold , where the users in the same group refer to the users who collect the same type of data.

[0014] Furthermore, the method for obtaining the average information age of the users includes:

[0015] Using the information age of the data received by the user from the th sensor of the th type and the selected best acquisition sensors from each type of sensor, construct the average information age of the data of the sensors of the required types for the user ;

[0016] According to the average information age of the sensor data of the required category of the user and the best acquisition sensors, the average information age of the user is calculated;

[0017] Among them, the user receives the category the information age of the data of the th sensor

[0018] ;

[0019] In the formula, represents the time taken by the UAV from the sensor with the acquisition order of to the sensor with the acquisition order of where, represents the total number of acquisition orders participating in data acquisition, , represents the user needs the category of data, represents the acquisition order of; represents the time taken by the UAV from the sensor at the end of the acquisition order in the sensor area to the end point of the sensor area ; represents the time taken by the sensor to fly from the end point of the second specific area where the sensor is located to the first specific area where the user is located; ; represents the UAV arrives at the group of users before the time taken for data transmission and flight; represents the time taken by the UAV to deliver data to the group of users, represents the UAV arrives at the group of users when the longest time taken for data transmission; represents the UAV from the group of hover points to the group of hover points; represents the time taken by the UAV to deliver to the group of the nth user;

[0020] The average information age of the sensor data of the required category of the user ​ , expressed as:

[0021] ;

[0022] The average information age of the user , expressed as:

[0023] .

[0024] Furthermore, the problem of minimizing the information age of the data obtained by the user is defined as , expressed as:

[0025] ;

[0026] In the formula, It indicates that the objective of the optimization problem is to minimize the information age of the data obtained by the user, and the optimization variables are the hovering point position of the UAV , the selection set of each type of sensor and the UAV trajectory , the first constraint C1 is At time For the th sensor of the th type The transmission rate between the sensor and the UAV is greater than the transmission threshold , indicating that the constraint holds for any sensor; the second constraint C2 is At time The transmission rate between the user and the UAV is greater than the transmission threshold ; the third constraint C3 is that the coverage range of the selected th type of sensor is greater than the coverage threshold , represents the coverage range of each type of sensor, represents the formula for calculating the sensor coverage range, represents selecting the best acquisition combination of the th type of sensor; the fourth constraint C4 is that the average data accuracy of the selected th type of sensor is greater than the accuracy threshold , where , represents taking the average of the total data accuracy of the th type of sensor, , represents the th th sensor of the Variance of data Fisher information amount of denotes taking the expected value, denotes the th sensor of the Variance of data maximum likelihood function of denotes taking the partial derivative; The fifth constraint C5 is that each user can and can only propose one requirement. Indicates the user whether the th type of sensor is needed; The sixth constraint C6 is to ensure that the variable

[0027] Further, the UAV hovering point problem is defined as , expressed as:

[0028] ;

[0029] In the formula, it means that the objective of the optimization problem is to minimize the age of information of the data obtained by the user, and the optimization variable is the UAV hovering point position .

[0030] Further, the sensor selection problem is defined as , expressed as:

[0031] ;

[0032] In the formula, it means that the objective of the optimization problem is to minimize the age of information of the data obtained by the user, and the optimization variable is the sensor selection set .

[0033] Further, the UAV trajectory optimization problem is defined as , expressed as:

[0034] ;

[0035] In the formula, it means that the objective of the optimization problem is to minimize the age of information of the data obtained by the user, and the optimization variable is the UAV trajectory .

[0036] Further, solving the UAV hovering point problem, the optimal solution of the UAV hovering point is obtained, including:

[0037] Input a set of user coordinate points with the same type of sensor data requirements, and use the Welzl algorithm to solve the UAV hovering point problem to obtain the minimum covering circle for each group of users. Among them, the center of the minimum covering circle is the horizontal coordinate of the UAV hovering position;

[0038] According to the UAV and the sensor transmission threshold , Identify as a threshold to obtain the height of the UAV hovering position;

[0039] According to the horizontal coordinate of the UAV hovering position and the height of the UAV hovering position, obtain the optimal solution of the UAV hovering point.

[0040] Furthermore, solve the sensor selection problem to obtain the optimal solution of the sensor set participating in data collection, including:

[0041] Use the NSGA-II algorithm to obtain the Pareto solution set of the type of sensor required by the user;

[0042] Select the minimum number of sensors that meet the constraint conditions C3 and C4 in the Pareto solution set of the user required sensors as the best acquisition set for selecting the type of sensor .

[0043] Furthermore, solve the UAV trajectory optimization problem to obtain the UAV data collection obstacle avoidance trajectory, including:

[0044] Based on the optimal solution of the UAV hovering point and the optimal solution of the sensor set participating in data collection, use the ant colony algorithm to solve the traveling salesman problem of the optimal solution of the UAV hovering point and the sensor set participating in data collection to obtain the acquisition order of the sensors for data collection;

[0045] Grid the sensor area for data collection, and dynamically quantify and score the grid cells according to the obstacle influence and transmission rate to obtain the initial shortest path;

[0046] Regard the first sensor for data collection in the initial shortest path and the intersection point of the shortest path as the starting point, and regard the last sensor and the intersection point of the shortest path as the end point;

[0047] Repeat the following steps until the UAV completes the data collection of all sensors and reaches the end point of the sensor area to obtain the UAV data collection obstacle avoidance trajectory:

[0048] The UAV starts from the starting point of the sensor area, searches in eight moving directions around the starting point, and selects the grid with the minimum score as the next moving point;

[0049] After the drone enters the sensor area with the acquisition sequence of modify the drone's target point to the center of the sensor with the acquisition sequence of

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0051] By comprehensively considering the position of the drone hovering point, sensor selection, and drone trajectory optimization, the present invention minimizes the average age of information of users while ensuring data acquisition accuracy and communication effectiveness. By decomposing the problem of minimizing the age of information of users obtaining data into the problems of drone hovering point, sensor selection, and drone trajectory optimization and solving them one by one, it not only ensures that the drone can efficiently collect high-precision data required by users, but also effectively avoids obstacles by optimizing the flight trajectory of the drone, improving the continuity and stability of data acquisition. Based on the optimized obstacle avoidance acquisition trajectory, the present invention can significantly reduce the average age of information of users, ensuring that users can obtain the latest and most accurate data information in a timely manner, thereby improving the timeliness and reliability of the entire data acquisition and transmission system.

[0052] The present invention randomly distributes users in the first specific area and randomly distributes a variety of sensors in the second specific area, ensuring that the sensors can completely cover the area. At the same time, a probabilistic LOS / NLOS channel model is used as the communication model between the drone and the sensors, effectively distinguishing between effective communication and ineffective communication, and improving the accuracy and efficiency of data transmission. In addition, by communicating with users in the same group in the form of drone broadcasting and only considering the LOS link, the stability and speed of communication are further improved, providing users with efficient and reliable data acquisition and transmission services.

[0053] The present invention realizes intelligent obstacle avoidance and trajectory optimization of the drone in a complex environment by using a heuristic algorithm based on the greedy algorithm idea, combined with the grid technology and the dynamic quantization scoring strategy, enabling the drone to select the optimal path according to the real-time situation, avoid obstacles, and at the same time ensure the continuity and stability of data transmission, not only improving the safety and reliability of the drone, but also providing strong technical support for the data acquisition task of the drone in a complex environment. Brief Description of the Drawings

[0054] Figure 1 The figure shows a schematic flow chart of a method for optimizing the obstacle avoidance trajectory of drone data acquisition based on the age of information provided by an embodiment of the present invention;

[0055] Figure 2 The figure shows a schematic system model diagram of a method for optimizing the obstacle avoidance trajectory of drone data acquisition based on the age of information provided by an embodiment of the present invention; ​

[0056] Figure 3 The following is a schematic flowchart of a heuristic algorithm based on the idea of the greedy algorithm provided by an embodiment of the present invention;

[0057] Figure 4 The following is a schematic diagram of a collection sensor under different coverage rate thresholds provided by an embodiment of the present invention;

[0058] Figure 5 The following is a schematic diagram of the flight trajectory of a drone provided by an embodiment of the present invention;

[0059] Figure 6 The following is a simulation schematic diagram of the change of the average age of information with the amount of uploaded data under different comparison algorithms provided by an embodiment of the present invention;

[0060] Figure 7 The following is a simulation schematic diagram of the change of the average age of information with the amount of uploaded data under different demand types provided by an embodiment of the present invention;

[0061] Figure 8 The following is a simulation schematic diagram of the change of the average age of information with the demand type under different sensor coverage ranges provided by an embodiment of the present invention;

[0062] Figure 9 The following is a simulation schematic diagram of the change of the average age of information with the amount of uploaded data under different flight speeds provided by an embodiment of the present invention. Detailed implementation manners

[0063] The technical solution of the present invention will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0064] The term "and / or" is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after.

[0065] Embodiment 1

[0066] As Figure 1 shown, in this embodiment, for the situation where neither the user nor the sensor can communicate with the base station, a method for optimizing the obstacle avoidance trajectory of drone data collection based on the age of information is introduced, including:

[0067] Step 1: Based on the UAV hovering point position, the set of each type of sensor selection, and the current data collection trajectory of the UAV, with the goal of minimizing the average age of information of the user, and with the preset communication threshold between the UAV and the user, as well as the coverage range and accuracy requirements of each type of sensor as constraints, construct the problem of minimizing the age of information for the user to obtain data.

[0068] The present invention constructs an objective function, that is, the problem of minimizing the age of information for the user to obtain data, that is, minimizing the average age of information of the user, according to three key factors: the UAV hovering point, sensor selection, and the UAV trajectory, while considering the communication threshold between the UAV and the user, the coverage range and accuracy requirements of the sensor. The age of information reflects the timeliness and freshness of the data obtained by the user and is an important indicator for evaluating the performance of the data collection system. By constructing the problem of minimizing the age of information for the user to obtain data, it can be ensured that during the data collection process, not only the accuracy and integrity of the data are considered, but also the real-time nature and freshness of the data are emphasized, thereby providing higher-quality data services for users.

[0069] Step 2: Decompose the problem of minimizing the age of information for the user to obtain data into a UAV hovering point problem, a sensor selection problem, and a UAV trajectory optimization problem.

[0070] Since the problem of minimizing the age of information for the user to obtain data is a complex optimization problem, it is very difficult to solve directly. Therefore, decomposing the problem of minimizing the age of information for the user to obtain data into three sub-problems: the UAV hovering point problem, the sensor selection problem, and the UAV trajectory optimization problem, can simplify the solution process and improve the solution efficiency.

[0071] Through problem decomposition, the present invention can transform a complex optimization problem into multiple relatively simple sub-problems, solve them separately, and then combine the results to obtain the final optimal solution, which not only reduces the solution difficulty but also improves the accuracy and efficiency of the solution.

[0072] Step 3: Optimize the hovering position of the UAV in the user's area and the set of sensors participating in data collection respectively, solve the UAV hovering point problem and the sensor selection problem, and obtain the optimal solution of the UAV hovering point and the optimal solution of the set of sensors participating in data collection.

[0073] The UAV hovering point problem and the sensor selection problem are two independent sub-problems. Therefore, these two problems need to be optimized separately to obtain the optimal hovering point position and the set of sensors.

[0074] By optimizing the UAV hovering point position and sensor selection, the present invention can ensure that the UAV hovers at the best position and selects the most suitable sensors for data collection, thereby improving the efficiency and accuracy of data collection.

[0075] Step 4: Based on the optimal solution of the UAV hovering points and the optimal solution of the sensor set participating in data collection, optimize the UAV flight trajectory to solve the UAV trajectory optimization problem and obtain the UAV data collection obstacle avoidance trajectory.

[0076] Based on the optimal solutions of the UAV hovering points and the sensor set, the present invention further optimizes the flight trajectory of the UAV to ensure that the UAV can efficiently collect data and avoid obstacles. By optimizing the flight trajectory of the UAV, it can be ensured that the UAV can move efficiently during data collection while avoiding collisions with obstacles, thereby improving the safety and reliability of data collection.

[0077] In summary, the present invention discloses a method for optimizing the UAV data collection obstacle avoidance trajectory based on the age of information. In this embodiment, for the situation where neither the user nor the sensor can communicate with the base station, a problem of minimizing the age of information for the user to obtain data is constructed. Among them, the UAV obtains the user's requirements, flies to the sensor area to collect sensor data of corresponding categories, and then flies back to the user area to deliver the data. Based on the preset communication threshold between the UAV and the user and the coverage range and accuracy requirements of each type of sensor, the problem of minimizing the age of information for the user to obtain data is decomposed into a UAV hovering point problem, a sensor selection problem, and a UAV trajectory optimization problem. First, the hovering position of the UAV in the user area and the sensor set participating in data collection are solved, and the UAV flight trajectory is optimized according to the obtained optimal solutions to achieve obstacle avoidance and hovering-free data collection by the UAV in the sensor area, and finally the average age of information of the user is obtained.

[0078] Embodiment 2

[0079] Based on the same inventive concept as Embodiment 1, this embodiment introduces the specific implementation steps of a method for optimizing the UAV data collection obstacle avoidance trajectory based on the age of information, including:

[0080] Step 1: Based on users, one UAV, and types of sensors, construct a collection system.

[0081] In this embodiment, the problem of minimizing the age of information for the user to obtain data is constructed based on the said collection system. The system model is as Figure 2 shown. The said system model consists of users, one UAV, and types of sensors. The sensors are mixed and randomly distributed in a specific area, and the number of each type of sensor can completely cover the area.

[0082] Among them, A number of users are randomly distributed in a first specific area. Each user can only submit one demand at a time. Each demand can be obtained by collecting data generated by corresponding types of sensors, but the same type of data can be requested by multiple users. To reduce the data delivery time, users with the same demand are grouped together, and the drone sends the data to the users in the same group in a broadcast manner.

[0083] A number of types of sensors are randomly distributed in a second specific area, and the number of each type of sensor can fully cover the second specific area. To ensure the quality of the collected data and shorten the collection time, optimal sensors are selected from each type of sensor according to two constraints: coverage range and data accuracy for data collection. To improve the collection efficiency, the drone adopts a mode of collecting data while flying. The size of the data generated by the sensor is , the th sensor of the th type is denoted as with the position ; the users are randomly distributed in another area, and the position of user is . If user needs the data of the th type, then the group to which it belongs is denoted as the th group, denoted as .

[0084] Among them, the communication model between the drone and the sensor adopts a probabilistic line-of-sight / non-line-of-sight channel model. The sensing range of each sensor is a circle with a radius of , denoted as . When the drone collects the data at time, the transmission rate between the th sensor of the th type and the drone is: ;

[0085] ;

[0086] In the formula, represents the transmission rate between the th sensor of the th type and the drone at time, represents the bandwidth for uploading data, is the channel gain corresponding to the sensor area when the reference distance is 1m, represents the Gaussian white noise power, is the channel gain corresponding to the sensor area when the reference distance is 1m, represents the Gaussian white noise power, For the sensor Transmission power, For the UAV and Path loss, Represents the logarithmic function with base 2;

[0087] Among them, the path loss between the UAV and Path loss Is expressed as:

[0088] ;

[0089] In the formula, Represents At time Of the th sensor And the UAV The path loss of the line-of-sight link LOS between them, Represents At time Of the th sensor And the UAV The probability of line-of-sight link LOS communication between them; Represents At time Of the th sensor And the UAV The path loss of the non-line-of-sight link NLOS between them, Represents At time Of the th sensor And the UAV The probability of non-line-of-sight link NLOS communication between them; Represents the UAV At Time to the Of the th sensor Distance.

[0090] Among them, the probability of line-of-sight transmission between the UAV and Is expressed as:

[0091] ;

[0092] In the formula, Represents At time the probability of line-of-sight transmission between the UAV and the Of the th sensor Between them, Indicates the distance of the UAV from the th moment to the th type and the th sensor, and and represent the environmental constants used to calculate line-of-sight transmission and non-line-of-sight transmission, represents the exponential function with the natural constant as the base, is the elevation angle between the UAV and at the th moment, and the probability of non-line-of-sight transmission between the UAV and at the th moment is the total probability minus the line-of-sight probability, where

[0093] Among them, when the UAV collects data at the th moment, the path loss is expressed as:

[0094] ;

[0095] In the formula, represents the path loss when the UAV collects data from the th moment, the th type, and the th sensor, represents the logarithmic function, is the carrier frequency of the UAV, is the speed of light, and and are the additional path losses of the line-of-sight and non-line-of-sight links respectively, is the distance of the UAV from the th moment to the th type and the th sensor, where, when the transmission rate between the sensor and the UAV is greater than the transmission threshold , it is regarded as effective communication, and the effective communication radius is .

[0096] Among them, the UAV communicates with users in the same group in a broadcast form. The distance between the UAV and user at the th moment is , represents the coordinate position of the UAV on the axis at the th moment, represents the where user Axis coordinate position Indicates the drone At the moment of Axis coordinate position Indicates the user where Axis coordinate position Indicates the height of the drone. Among them, the drone is at At the moment and the user Channel gain where Is the channel gain between the drone and the user when the reference distance is 1m The transmission rate between the drone and the user is expressed as: ;

[0097] ;

[0098] In the formula, Indicates the transmission rate between the drone And the user Between, Indicates the drone Communication bandwidth of Indicates the drone At At the moment and the user Channel gain of Indicates the drone Transmission power of Indicates the Gaussian white noise power

[0099] This embodiment requires that the transmission rate Between the drone and the user Greater than the transmission threshold , so the minimum distance between the drone and the user is expressed as: ;

[0100] ;

[0101] In the formula, Indicates the minimum distance between the drone and the user Is the channel gain between the drone and the user when the reference distance is 1m Of Indicates the drone Transmission power of Indicates the Gaussian white noise power Represents the drone And the user Transmission rate threshold value between, Indicates the drone Communication bandwidth of

[0102] Based on the fact that the data volume uploaded by each sensor is , then the user receives the time taken for the th type of sensor data transmitted by the drone is expressed as:

[0103] ;

[0104] In the formula, represents the time taken for the user to receive the th type of sensor data transmitted by the drone, represents the sum of the data volumes uploaded by the selected best acquisition sensors, represents the drone and the user transmission rate therebetween.

[0105] Select best acquisition sensors from each type of sensor, use to represent the order of the sensors in data acquisition, represents the total number of acquisition orders participating in data acquisition.

[0106] To sum up, in this embodiment, when the th type of the th sensor and the drone the transmission rate between is greater than the transmission threshold , it is regarded as effective communication, represents the transmission rate threshold value between the sensor and the drone , identifies that this value is a threshold value.

[0107] Group users who need the same type of data as the same group of users, and use the drone to communicate with the same group of users in a broadcast form. Only considering the line-of-sight link, set the drone and the user the transmission rate between is greater than the transmission threshold .

[0108] Step 2: Based on the position of the drone hovering point, the selection set of each type of sensor, and the current data acquisition trajectory of the drone, with the goal of minimizing the average age of information of users, and with the preset communication threshold between the drone and users, as well as the coverage range and accuracy requirements of each type of sensor as constraints, construct a problem of minimizing the age of information for users to obtain data.

[0109] In this embodiment, the coverage range of each type of sensor is​ , where represents the formula for calculating the coverage range of the sensor, represents selecting the best acquisition combination of the -th type of sensor. If only the influence of the sensor noise variance on the data accuracy is concerned, the Fisher information can be used to represent the data accuracy.

[0110] In this embodiment, the accuracy requirement of each type of sensor is expressed as:

[0111] ;

[0112] In the formula, represents the average accuracy of the -th type of sensor, represents taking the average of the total data accuracy of the -th type of sensor, , represents the -th -th sensor data variance of the Fisher information, represents taking the expected value, represents the -th -th sensor data variance of the maximum likelihood function, represents taking the partial derivative.

[0113] In this embodiment, the method for obtaining the average information age of the user includes:

[0114] Using the information age of the data received by the user from the -th -th -th sensor of the -th type to construct the average information age of the data of the sensors of the types required by the user;

[0115] According to the average information age of the data of the sensors of the types required by the user and the optimal acquisition sensors, calculate the average information age of the user;

[0116] Among them, the information age of the data received by the user from the -th -th -th sensor of the -th type is expressed as:

[0117] ;

[0118] In the formula, represents the time taken by the UAV from the sensor with the acquisition sequence of to the sensor with the acquisition sequence of , where represents the total number of acquisition sequences participating in data acquisition, , represents the user needs the type of data, represents 's acquisition sequence; represents the time taken by the UAV from the sensor at the end of the acquisition sequence in the sensor area to the end point of the sensor area; represents the time taken by the sensor to fly from the end point of the second specific area where the sensor is located to the first specific area where the user is located ; represents the UAV arriving at the group of users, including the time taken for data transmission and flight; represents the time taken by the UAV to deliver data to the group of users, represents the UAV arriving at the group of users, the longest time taken for data transmission; represents the time taken by the UAV to fly from the hovering point of the group to the hovering point of the group; represents the time taken by the UAV to deliver to the nth user in the group;

[0119] The average age of information of the sensor data of the required category by the user , expressed as: , expressed as:

[0120] ;

[0121] The average age of information of the user , expressed as:

[0122] .

[0123] In this embodiment, the problem of minimizing the age of information for the user to obtain data is defined as , expressed as:

[0124] ;

[0125] In the formula, The optimization problem objective is to minimize the information age of the user's data acquisition, and the optimization variable is the position of the drone's hovering point. , each type of sensor selection set And drone tracks , the first constraint C1 is Moment Class Sensors With drones The transmission rate between Greater than the transmission threshold , Indicates that the constraint is established for any sensor; the second constraint C2 is Moment User With drones The transmission rate between Greater than the transmission threshold ; The third constraint C3 is the selected Coverage of sensor types Greater than coverage threshold , represents the coverage of each type of sensor, represents the formula for calculating sensor coverage, Indicates the selection of The fourth constraint C4 is the best acquisition combination of the selected Average data accuracy of sensor types Greater than the accuracy threshold ,in, , Indicates The total data accuracy of the sensor is averaged. , Indicates Class Sensors Variance of the data The Fisher information of It means to find the expected value. Indicates Class Sensors Variance of the data The maximum likelihood function of Indicates partial derivative; the fifth constraint C5 is that each user can and can only make one demand, Instruct users Do you need the Class sensor; the sixth constraint C6 is to ensure the variable indicating the user's needs The values are only 0 and 1.

[0126] Step 3: Decompose the problem of minimizing the age of information of the data obtained by the user into a UAV hovering point problem, a sensor selection problem, and a UAV trajectory optimization problem.

[0127] In this embodiment, with the preset communication threshold between the UAV and the user, and the coverage range and accuracy requirements of each type of sensor as constraints, the problem of minimizing the age of information of the data obtained by the user is decomposed into a UAV hovering point problem , a sensor selection problem and a UAV trajectory optimization problem .

[0128] In this embodiment, the UAV hovering point problem is expressed as:

[0129] ;

[0130] In the formula, it means that the objective of the optimization problem is to minimize the age of information of the data obtained by the user, and the optimization variable is the UAV hovering point position .

[0131] In this embodiment, the sensor selection problem is expressed as:

[0132] ;

[0133] In the formula, it means that the objective of the optimization problem is to minimize the age of information of the data obtained by the user, and the optimization variable is the sensor selection set .

[0134] In this embodiment, the UAV trajectory optimization problem is expressed as:

[0135] ;

[0136] In the formula, it means that the objective of the optimization problem is to minimize the age of information of the data obtained by the user, and the optimization variable is the UAV trajectory .

[0137] Step 4: Optimize the hovering position of the UAV in the user's area and the set of sensors participating in data collection respectively, solve the UAV hovering point problem and the sensor selection problem, and obtain the optimal solution of the UAV hovering point and the optimal solution of the set of sensors participating in data collection.

[0138] In this embodiment, the problem of the UAV hovering point is solved to obtain the optimal solution of the UAV hovering point, including:

[0139] Input the user coordinate point set with the same type of sensor data requirements, and use the Welzl algorithm to solve the UAV hovering point problem to obtain the minimum covering circle of each group of users. Among them, the center of the minimum covering circle is the horizontal coordinate of the UAV hovering position;

[0140] According to the UAV and the sensor transmission threshold , where identifier is a threshold value to obtain the UAV hovering position height;

[0141] In this embodiment, the calculation formula of the UAV hovering position height is expressed as:

[0142] ;

[0143] Among them, represents the UAV hovering position height, is the minimum distance between the UAV and the user to meet the communication threshold, is the radius of the minimum covering circle of the users with the type of requirements returned by the Welzl algorithm.

[0144] According to the UAV hovering position horizontal coordinate and the UAV hovering position height, the optimal solution of the UAV hovering point is obtained.

[0145] In this embodiment, the problem of sensor selection is solved to obtain the optimal solution of the sensor set participating in data collection, including:

[0146] Use the NSGA-II algorithm to obtain the Pareto solution set of the type of sensors required by the user;

[0147] Select the minimum number of sensors that meet the constraint conditions C3 and C4 in the Pareto solution set of the sensors required by the user as the best acquisition set for selecting the type of sensors .

[0148] In this embodiment, use the NSGA-II algorithm to obtain the Pareto solution set of the type of sensors required by the user, including:

[0149] Quantize the area where the sensor is located into pixel points, each pixel point has a size of 1. Among them, the probability that the point is covered by is expressed as:

[0150] ;

[0151] In the formula, represents the probability that point is covered by ; represents the position coordinates of the pixel point, identifies the position coordinates of the center, represents the perception radius of the

[0152] If sensors are selected from the th class of sensors, then the total coverage probability of selecting sensors from the th class of sensors is the total probability minus the probability of the uncovered points, expressed as:

[0153] ;

[0154] In the formula, represents that the total coverage probability of selecting sensors from the th class of sensors is the total probability minus the probability of the uncovered points, represents the probability that point is covered by ; represents the number of sensors selected from the th class of sensors, represents the product symbol.

[0155] Define the coverage rate of the sensor as the ratio of the covered area to the total area, expressed as:

[0156] ;

[0157] In the formula, represents that the coverage rate of the sensor is defined as the ratio of the covered area to the total area, represents the sensor covered area obtained by traversing all pixel points in the traversal area, represents the total area of the considered sensor area.

[0158] Adopt to represent the set of obstacles in the sensor area. The position of the th obstacle is . When not considering the observation noise, the sensor observation noise variance . Due to the existence of obstacles, the sensor observation noise variance is affected by the obstacle at The area within the sensing range and the distance from the obstacle center to the center have an impact, then the observation noise variance of can be expressed as:

[0159] ;

[0160] In the formula, represents the area of the obstacle within the sensing range, represents the distance from the obstacle center to the center, and the sum is taken to find the impact of all obstacles on , represents the influence factor of the distance on the variance and is a constant.

[0161] Step 5: Solve the UAV trajectory optimization problem to obtain the obstacle avoidance acquisition trajectory of the UAV.

[0162] In this embodiment, based on the optimal solution of the UAV hovering point and the optimal solution of the sensor set participating in data collection, a heuristic algorithm based on the greedy algorithm idea is used to optimize the UAV flight trajectory to solve the UAV trajectory optimization problem, and the UAV data collection obstacle avoidance trajectory is obtained, as Figure 3 shown, including:

[0163] Based on the optimal solution of the UAV hovering point and the optimal solution of the sensor set participating in data collection, the ant colony algorithm is used to solve the traveling salesman problem of the optimal solution of the UAV hovering point and the sensor set for data collection, and the acquisition order of the sensors for data collection is obtained;

[0164] The sensor area for data collection is gridded, and the grid units are dynamically quantified and scored according to the obstacle influence and transmission rate to obtain the initial shortest path;

[0165] The first sensor for data collection in the initial shortest path and the intersection point of the shortest path are regarded as the starting point, and the last sensor and the intersection point of the shortest path are regarded as the ending point;

[0166] The UAV starts from the starting point of the sensor area, searches in eight moving directions around the starting point, and selects the grid with the smallest score as the next moving point;

[0167] When the UAV enters the sensor area with the acquisition order of , the target point of the UAV is modified to the center of the circle of the sensor with the acquisition order of , and so on until the UAV completes the data collection of all sensors and reaches the ending point of the sensor area, and the UAV data collection obstacle avoidance trajectory is obtained.

[0168] In this embodiment, the area where the sensor is located is grid-divided into cells, and the cell in the th row and the th column is denoted as . The central coordinate of the cell in the th row and the th column is . The transmission rate of each cell's sensor is regarded as a fixed value .

[0169] Use to represent the risk from the th obstacle in the grid , which is defined as:

[0170] ;

[0171] In the formula, and are constants, represents the risk exposure degree factor, represents the distance from the center of the th obstacle to , represents the central coordinate of the cell in the th row and the th column, represents the central coordinate of the th obstacle, takes a value much larger than the value when , when represents the risk from the th obstacle in the grid exhibits an exponential distribution. Then the risk exposure probability of the grid from all obstacles can be expressed as:

[0172] ;

[0173] In the formula, represents the risk from the th obstacle in the grid , represents the total number of obstacles.

[0174] The score assignment rule for each cell in the sensor area is:

[0175] ;

[0176] In the formula, represents the minimum cost from the current point to the grid . The drone search is as followsFigure 4 When considering the grids numbered 1, 3, 5, and 7 in the surroundings shown Take 1. When searching for the grids numbered 2, 4, 6, and 8 Take 1.4142; Represents a square grid The estimated cost to the end point, represented by the Manhattan distance; Represents a square grid The additional cost, which is jointly affected by obstacles, transmission rate, and the amount of data collected, and is updated with the number of iterations changes.

[0177] Express the additional cost as:

[0178] ;

[0179] In the formula, Represents the additional cost, and Are weighting coefficients, respectively reflecting the tolerance to risk and the priority of data collection; Represents the risk exposure probability of the square grid ; Is the communication radius of the th type of sensor, Represents The distance from to the center, Is the amount of data uploaded by each sensor, Is the th step when the remaining data volume of sensor Can be expressed as

[0180] ;

[0181] In the formula, Represents whether the drone selects when collecting sensor at the th step, Represents the minimum cost from the current point to the square grid ; Represents The transmission rate in ; Represents the iteration number when the drone starts to enter the transmission range of ; Represents the total amount of data that the drone can collect at the th step.

[0182] To further optimize the flight path of the drone, it is stipulated that when the value of is greater than When. Here is the included angle of the original path in the sensor with the acquisition order as . When the included angle is less than 90°, the score difference caused by strengthening the distance difference from the sensor center is enhanced, guiding the UAV to be more inclined to fly towards the direction of the sensor center when the remaining data volume is large.

[0183] For sensors with overlapping transmission radii, it is stipulated that the UAV needs to complete the acquisition of all information of the current sensor before collecting the information of the next sensor. In a grid-like environment, the UAV can establish communication with only one sensor in a grid. In the overlapping area, the UAV follows the priority of the acquisition order, that is, it preferentially collects the information of the sensor with a higher position in the acquisition order.

[0184] Figure 4 The schematic diagram of the optimal acquisition sensors selected under different cases of the sensor area coverage rate threshold is given. Figure 4 gives all the -type sensors in the sensor area and the distribution diagrams of the -type sensors selected when the set coverage threshold values are 85%, 90% and 95%. It can be seen that the number of sensors after selection is significantly less than the total number of sensors. This optimization strategy greatly shortens the total time required for the UAV to collect information, thus effectively reducing the average AoI.

[0185] Figure 5 The complete path of the UAV from the acquisition requirement to delivering information to the user is given. This figure shows three types of user requirements and the flight path when the coverage threshold is taken as 90%. Among them, the pentagram mark represents the hovering point in the user area, the gray solid circle represents the obstacle, the hollow circle represents the effective communication range of the sensor, and different colors are used to distinguish the user's requirement type and the corresponding optimal acquisition sensor. It can be seen from the figure that compared with the shortest path trajectory, the path obtained by using the trajectory optimization algorithm in this paper does not need to pass through the sensor center, reducing the total path of the UAV flight; and the trajectory optimization algorithm in this paper effectively avoids the obstacles on the UAV flight path.

[0186] To verify the performance of the algorithm proposed in the present invention, the present invention considers comparing with the following several algorithms:

[0187] Comparison algorithm 1: Cluster the sensors, the UAV hovers at the center of each cluster to collect data, and use the dynamic programming algorithm to plan the path for the obtained hovering points, but the obstacle avoidance factor is not considered;

[0188] Comparison algorithm 2: After determining the optimal acquisition order of the sensors, use an estimation and solution method to determine the optimal path points of the sensors, and then further use the dynamic window method for obstacle avoidance when the obstacle is on the optimal path.

[0189] Figure 6 The average AoI of the algorithm of the present invention is compared with that of the shortest path hovering acquisition algorithm, the greedy hovering acquisition algorithm, and two comparison algorithms. The average AoI shows an upward trend as the amount of data uploaded by the sensor increases. This is because as the amount of uploaded data increases, the time for the UAV to collect and deliver data becomes longer, so the AoI also increases. When the amount of uploaded data increases, the performance of comparison algorithm 1 is the worst, because the sensor area in this scenario is large and the channel condition is poor, resulting in a very small transmission rate for sensors far from the cluster center, which greatly increases the time for uploading data. Although comparison algorithm 2 effectively shortens the flight path of the UAV and successfully achieves obstacle avoidance, the acquisition points it selects are located at the edge of the sensor communication range, with a low transmission rate, resulting in an increase in the data upload time.

[0190] Figure 7 The variation of the average AoI with the increase in the amount of data uploaded by the sensor is given when the number of user demand types is different. It can be seen from the figure that when the amount of uploaded data is the same, the average AoI with fewer user demand types is lower than that with a large number of user demand types. The main reasons for this phenomenon are twofold. On the one hand, the number of user demand types directly affects the number of sensors to be collected. Whenever a user adds a demand type, the UAV needs to collect data of a certain type of sensor additionally, which will cause the path of the UAV in the sensor area to become longer and the collection time to become longer. On the other hand, more demand types extend the total time spent on UAV delivery, so the average AoI increases with the increase in the number of demand types.

[0191] Figure 8 The variation of the average AoI with the increase in user demand is given when the sensor coverage threshold is different. It can be seen that when the user demand is the same, the average AoI decreases as the coverage rate decreases. This is because the coverage rate is directly related to the number of sensors to be selected. A smaller threshold means that the number of sensors to be selected is also smaller, and the collection time of the UAV also decreases accordingly. However, a higher coverage rate also means that the collected information is more comprehensive, so an appropriate threshold should be selected according to the actual situation.

[0192] In some embodiments, it further includes calculating the average age of information of the user according to the obstacle avoidance trajectory of the UAV data collection.

[0193] After obtaining the obstacle avoidance trajectory for UAV data collection, the present invention calculates the average information age of the user based on the obstacle avoidance trajectory for UAV data collection. As an important indicator for evaluating the performance of the entire data collection system, it can reflect the timeliness and freshness of the data obtained by the user, understand the performance of the entire data collection system, and further adjust and optimize the parameters and strategies of the system to improve the efficiency and accuracy of data collection. At the same time, this can also provide more reliable data services for users to meet their requirements for real-time and freshness.

[0194] Embodiment 3

[0195] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium with computer instructions stored thereon, characterized in that when the computer instructions are executed by a processor, the steps of the method in Embodiment 1 or 2 above are implemented.

[0196] Embodiment 4

[0197] Based on the same inventive concept as other embodiments, the present invention also provides a computer program product including computer instructions, characterized in that when the computer instructions are executed by a processor, the steps of the method in Embodiment 1 or 2 above are implemented.

[0198] In summary of the above embodiments, the present invention minimizes the average information age of the user while ensuring the data collection accuracy and communication effectiveness by comprehensively considering the UAV hovering point position, sensor selection, and UAV trajectory optimization. By decomposing the problem of minimizing the information age of the data obtained by the user into the UAV hovering point problem, sensor selection problem, and UAV trajectory optimization problem and solving them one by one, it not only ensures that the UAV can efficiently collect high-precision data required by the user, but also effectively avoids obstacles by optimizing the flight trajectory of the UAV, improving the continuity and stability of data collection. Based on the optimized obstacle avoidance collection trajectory, the present invention can significantly reduce the average information age of the user, ensuring that the user can obtain the latest and most accurate data information in a timely manner, thereby improving the timeliness and reliability of the entire data collection and transmission system.

[0199] The present invention randomly distributes users in the first specific area and randomly distributes a variety of sensors in the second specific area, ensuring that the sensors can completely cover the area where the sensors are located. At the same time, a probabilistic line-of-sight / non-line-of-sight channel model is used as the communication model between the UAV and the sensors, effectively distinguishing between effective communication and ineffective communication, improving the accuracy and efficiency of data transmission. In addition, by communicating with users in the same group in the form of UAV broadcasting and only considering the line-of-sight link, the stability and speed of communication are further improved, providing users with efficient and reliable data collection and transmission services.

[0200] The present invention realizes intelligent obstacle avoidance and trajectory optimization of an unmanned aerial vehicle (UAV) in a complex environment by using a heuristic algorithm based on the idea of a greedy algorithm, combined with a grid technology and a dynamic quantization and scoring strategy. The UAV can select an optimal path according to real-time situations, avoid obstacles, and ensure the continuity and stability of data transmission. This not only improves the safety and reliability of the UAV, but also provides strong technical support for the data collection task of the UAV in a complex environment.

[0201] Those skilled in the art should understand that the embodiments of the present invention may be provided as a method, a system, or a computer program product. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0202] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0203] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0204] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0205] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A method for optimizing obstacle avoidance trajectories for unmanned aerial vehicle data collection based on information age, characterized in that: include: According to the position of the drone's hovering point, the set of sensors selected for each type, and the drone's current data collection trajectory, the problem of minimizing the information age of the user's acquired data is constructed with the goal of minimizing the average information age of the user, the preset communication threshold between the drone and the user, and the coverage and accuracy requirements of each type of sensor as constraints; Decomposing the problem of minimizing the information age of the user-acquired data into a problem of drone hovering points, a problem of sensor selection, and a problem of drone trajectory optimization; Optimize the hovering position of the drone in the user's area and the set of sensors involved in data collection, solve the drone hovering point problem and sensor selection problem, and obtain the optimal solution of the drone hovering point and the optimal solution of the sensor set involved in data collection; Based on the optimal solution of the drone's hovering point and the optimal solution of the sensor set involved in data collection, the drone's flight trajectory is optimized to solve the drone trajectory optimization problem and obtain the drone's data collection obstacle avoidance trajectory.

2. The method for optimizing obstacle avoidance trajectory for drone data collection based on information age according to claim 1 is characterized in that: It also includes, before constructing the information age minimization problem of user acquisition data, based on Users, a drone and Building a collection system using sensors; The problem of minimizing the age of information of the user's acquired data is based on the acquisition of the acquisition system; In the acquisition system, The users are randomly distributed in the first specific area; The sensors of different types are randomly distributed in the second specific area, and the number of each type of sensors can completely cover the second specific area; The communication model between the drone and the sensor is a probabilistic line-of-sight / non-line-of-sight channel model. Class Sensors With drones The transmission rate between Greater than the transmission threshold The communication is considered as effective when the drone communicates with the users in the same group through broadcasting, and only the line-of-sight link is considered. With users Transfer rate Greater than the transmission threshold , wherein the users in the same group refer to users who collect the same type of data.

3. The method for optimizing obstacle avoidance trajectory for drone data collection based on information age according to claim 2 is characterized in that: The method for obtaining the average information age of users includes: Exploiting users Received Class Sensors The information age of the data and the number of sensors selected from each category The best acquisition sensor to build user The average information age of sensor data in the required categories; According to user The average information age of sensor data in the required categories and The best collection sensor is used to calculate the average information age of users; Among them, the user Received Class Sensors The information age of data It is expressed as: ； In the formula, Indicates that the drone is collected in the following order: The order of sensors to acquisition is The time spent by the sensor, where Indicates the total number of collection sequences involved in data collection, , Indicates user Need Class data, express The order of collection; Indicates that the drone is at the end of the acquisition sequence in the sensor area Sensor to sensor zone end point the time spent; Indicates that the sensor is from the sensor The second specific area end point Fly to the user Time spent in the first specific area; Indicates drone Arrive at The time the group users previously spent on transmitting data and flying; Indicates that the drone gives The time it takes for the group of users to deliver data, Indicates drone Arrive at The maximum time it takes to transfer data when grouping users. Indicates that the drone has The group hovers to the The time spent on the group hover point; Indicates that the drone gives The time it takes for the nth user in the group to deliver; The user Average information age of sensor data in the required categories , expressed as: ； The average information age of the users , expressed as: 。 4. The method for optimizing obstacle avoidance trajectory for drone data collection based on information age according to claim 3 is characterized in that: The problem of minimizing the information age of the user's acquired data is defined as , expressed as: ； In the formula, The optimization problem objective is to minimize the information age of the user's data acquisition, and the optimization variable is the position of the drone's hovering point. , each type of sensor selection set And drone tracks , the first constraint C1 is Moment Class Sensors With drones The transmission rate between Greater than the transmission threshold , Indicates that the constraint is established for any sensor; the second constraint C2 is Moment User With drones The transmission rate between Greater than the transmission threshold ; The third constraint C3 is the selected Coverage of sensor types Greater than coverage threshold , represents the coverage of each type of sensor, represents the formula for calculating sensor coverage, Indicates the selection of The fourth constraint C4 is the best acquisition combination of the selected Average data accuracy of sensor types Greater than the accuracy threshold ,in, , Expressing the The total data accuracy of the sensor is averaged. , Indicates Class Sensors Variance of the data The Fisher information of It means to find the expected value. Indicates Class Sensors Variance of the data The maximum likelihood function of Indicates partial derivative; the fifth constraint C5 is that each user can and can only make one demand, Instruct users Do you need the Class sensor; the sixth constraint C6 is to ensure the variable indicating the user's needs The values ​​of are only 0 and 1.

5. The method for optimizing obstacle avoidance trajectory for drone data collection based on information age according to claim 4 is characterized in that: The drone hovering point problem is defined as , expressed as: ； In the formula, The optimization problem objective is to minimize the information age of the user's data acquisition, and the optimization variable is the position of the drone's hovering point. .

6. The method for optimizing obstacle avoidance trajectory for drone data collection based on information age according to claim 4 is characterized in that: The sensor selection problem is defined as , expressed as: ； In the formula, The optimization problem objective is to minimize the information age of the user's data acquisition, and the optimization variable is the sensor selection set. .

7. The method for optimizing obstacle avoidance trajectory for drone data collection based on information age according to claim 4 is characterized in that: The UAV trajectory optimization problem is defined as , expressed as: ； In the formula, The optimization problem objective is to minimize the information age of the user's acquired data, and the optimization variable is the drone trajectory. .

8. The method for optimizing obstacle avoidance trajectory for drone data collection based on information age according to claim 5 is characterized in that: Solve the drone hovering point problem and get the optimal solution of the drone hovering point, including: Input the user coordinate point set with the same type of sensor data requirements, use the Welzl algorithm to solve the drone hovering point problem, and obtain the minimum coverage circle for each group of users, where the center of the minimum coverage circle is the horizontal coordinate of the drone hovering position; According to the drone With sensor The transmission threshold , Logo is a threshold value, and the hovering height of the drone is obtained; According to the horizontal coordinates of the drone's hovering position and the height of the drone's hovering position, the optimal solution for the drone's hovering point is obtained.

9. The method for optimizing obstacle avoidance trajectory for UAV data collection based on information age according to claim 6 is characterized in that: Solve the sensor selection problem and obtain the optimal solution for the sensor set involved in data collection, including: The NSGA-II algorithm is used to obtain the first Pareto solution set of class sensors; In the Pareto solution set of the user demand sensors, the minimum number of sensors that meet the constraints C3 and C4 are selected as the first The best collection of class sensors .

10. The method for optimizing obstacle avoidance trajectory for UAV data collection based on information age according to claim 7, characterized in that: Solve the drone trajectory optimization problem and obtain the obstacle avoidance trajectory for drone data collection, including: Based on the optimal solution of the drone hovering point and the optimal solution of the sensor set involved in data collection, the ant colony algorithm is used to solve the traveling salesman problem of the optimal solution of the drone hovering point and the sensor set for data collection, and the collection order of the sensors for data collection is obtained; The sensor area for data collection is gridded, and the grid units are dynamically quantified and scored according to the impact of obstacles and transmission rate to obtain the initial shortest path; The intersection point of the first sensor that collects data in the initial shortest path and the shortest path is regarded as the starting point, and the intersection point of the last sensor and the shortest path is regarded as the end point; Repeat the following steps until the drone completes data collection from all sensors and reaches the end of the sensor area, obtaining the drone data collection obstacle avoidance trajectory: The drone starts from the starting point of the sensor area, searches in eight moving directions around the starting point, and selects the grid with the smallest score as the next moving point; When the drone enters the collection sequence After the sensor area is set, modify the drone target point to the acquisition order The sensor center.

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