A flight trajectory planning method for an unmanned aerial vehicle data acquisition system

Abstract

The present invention discloses a flight trajectory planning method for an unmanned aerial vehicle (UAV) data collection system, which relates to the field of wireless communication technology. The method comprises a UAV and several data sources. The UAV serves as an aerial base station to collect data generated by a ground data source. Under the condition of limited energy of the UAV, the flight trajectory of the UAV is optimized to maximize the amount of data collected. The position coordinates of the ground data source are obtained, and the received signal-to-noise ratio at a reference distance of 1 meter is measured. The problem is then modeled as a Markov decision process to determine three elements: state, action, and reward. The reward function is a weighted sum of flight energy consumption per time slot and the amount of data collected. A weighting factor of the reward function is given, and the flight trajectory and the amount of data collected are calculated. The weighting factor is traversed from 0 to 1 according to a certain step size to find the maximum amount of data collected. The corresponding flight trajectory is the desired flight trajectory. Compared with a stationary solution and a greedy strategy under the same conditions, the method achieves a higher amount of data collection.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a flight trajectory planning method for an unmanned aerial vehicle (UAV) data acquisition system. Background Art

[0002] Drones, with their high mobility, flexible deployment, and low cost, can serve as aerial base stations, providing flexible and efficient communication solutions to meet communication needs in a variety of scenarios. In remote mountainous areas or during disasters, drones can act as mobile communication relay stations, rapidly establishing emergency communication networks. In remote areas such as forests and grasslands, drones can be flexibly deployed for data collection and monitoring. During large-scale events or in crowded areas, drones can provide additional communication capacity to meet peak communication needs. Furthermore, drones can be used in integrated satellite-ground networks, holographic systems, and edge computing networks.

[0003] When a UAV is performing a mission, reasonable trajectory planning can improve the communication quality between it and ground users, reduce mission execution time and energy consumption, and enhance its flight safety and flexibility, so it is very important. Based on content awareness, the Journal of Electronics and Information Technology (“Content-aware joint optimization method for UAV trajectory planning and resource allocation”, Journal of Electronics and Information Technology, 2023, 45(5): 1644-1650) jointly optimized UAV trajectory planning and wireless resource allocation to maximize the minimum user average service rate while meeting the content requirements of each user. For the UAV-assisted clustered non-orthogonal multiple access system, the American Institute of Electrical and Electronics Engineers Internet of Things Journal (“UAV-supported clustered NOMA for 6G-enabled internet of things: Trajectory planning and resource allocation”, IEEE Internet of Things, 2021, 8(20): 15041–15048) collaboratively planned the UAV trajectory and sub-time slot allocation strategy to maximize the uplink average reachability and rate of the IoT terminal under the condition that the UAV mobility is constrained.

[0004] At present, many studies assume that the UAV flight energy is unlimited, but in actual work, its energy is often limited. Obviously, considering the factor of UAV energy limitation is closer to practical scenarios, its flight trajectory planning still has a large room for research. For the energy-limited UAV-assisted data acquisition system, the present invention proposes a UAV flight trajectory planning method, so that the UAV can collect as much data as possible. Summary of the Invention

[0005] The purpose of the present invention is to provide a flight trajectory planning method for an unmanned aerial vehicle (UAV) data acquisition system, which can significantly improve the data acquisition capacity of the system by planning the flight trajectory of the UAV under the condition of limited UAV energy.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] This application provides a flight trajectory planning method for an unmanned aerial vehicle data acquisition system, comprising the following steps:

[0008] S1. The drone obtains the location coordinates of the ground data source and obtains the received signal-to-noise ratio at a reference distance of 1 meter by measuring; the drone's flight area is divided into several grids;

[0009] S2, initialize step size and weight;

[0010] S3. Calculate the throughput based on the sub-algorithm and store it in an array;

[0011] S4. Update the weight. If the weight is greater than 1, terminate the loop, otherwise return to S3.

[0012] S5. Find the maximum value and corresponding index of the array and find the optimal weight;

[0013] S6. Based on the optimal weight, call the sub-algorithm to find the optimal flight trajectory.

[0014] Preferably, the step S1 divides the drone flight area into several grids, and the rectangular area where the drone flies is represented as:

[0015] ; is divided into grids, each grid size is , , .

[0016] Preferably, the initialization operation of step S2 is initialized with a step length of , weight w=0.

[0017] Preferably, the sub-algorithm of step S3 includes:

[0018] S301: Initialize search strategy factors , search threshold , attenuation factor ; Reward discount rate , learning rate , action-value function , where S is the state space and A is the action space; construct the structure array Used to store flight trajectory; identifier ;

[0019] S302: Initialization time index n=0; UAV initial position , drone energy ; System throughput ;like , put z(0) into middle;

[0020] S303: According to The greedy strategy selects action a(n);

[0021] S304: The drone performs action a(n), the state changes to z(n+1), and gets rewarded ,like , put z(n+1) into middle;

[0022] S305: Update several parameters.

[0023] ;

[0024] Data collection volume ,,Drone Energy ,here is the amount of data collected and energy consumption in a time slot;

[0025] S306: If E>0, set n:=n+1 and go to S303; if E≤0 and , terminate the loop and go to the next step S307; if E≤0 and , output flight trajectory and data collection volume , terminate the entire program;

[0026] S307: ,like ,make , go to S302.

[0027] Preferably, the weight update in step S4 is expressed as: .

[0028] Preferably, the optimal weight of step S6 is to first find the index corresponding to the maximum value of the array, which is recorded as , then the optimal weight is .

[0029] Preferably, the state space S and action space A of step S301 are:

[0030] ,

[0031] in , , the superscript T is the transpose operation, , where 0~4 represent the five actions of hovering, east, south, west, and north respectively.

[0032] Preferably, the step S303 Greedy strategy, generating random numbers ;like , randomly selected ,otherwise .

[0033] Preferably, the reward function of step S304 is a weighted sum of the flight energy consumption per time slot and the amount of data collected, and a weighting factor of the reward function is given;

[0034]

[0035] Among them, 0≤w≤1 is the weight, is a flight slot duration, is the flight speed, They are respectively the UAV flight power and hovering power, and are two constants, is the communication rate at time n, expressed as:

[0036] ,

[0037] in, is the flight altitude of the UAV, K is the number of data sources, B is the bandwidth allocated to each data source, is the base station receiving signal-to-noise ratio for data source k at a reference distance of 1 meter, are the coordinates of the data source k, is the coordinate of the drone projected onto the ground at time n, Represents the magnitude of a vector.

[0038] Preferably, the step S305 and ,in; ; If the action at time n is hovering, ,otherwise .

[0039] The beneficial effects of the present invention are:

[0040] The present invention provides a flight trajectory planning method for an unmanned aerial vehicle (UAV) data acquisition system based on the feedback of coordinate position information from a ground data source. This method can effectively increase the system data acquisition volume m while satisfying the UAV's energy constraints. Compared with several baseline comparison schemes, such as the "stationary" and "greedy flight" schemes, this method achieves a higher data acquisition volume. The present invention also has the advantages of low feedback volume and low system overhead. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0042] Figure 1 A schematic diagram of a UAV flight trajectory calculation flow for a flight trajectory planning method of a UAV data acquisition system provided in Example 1 of the present application;

[0043] Figure 2 A schematic diagram of the sub-algorithm flow chart of the flight trajectory planning method of a drone data acquisition system provided in Example 1 of the present application;

[0044] Figure 3 A schematic diagram of the flight trajectory of a drone data acquisition system with six data sources according to a flight trajectory planning method for a drone data acquisition system provided in Example 1 of the present application;

[0045] Figure 4 This is a performance comparison diagram of a drone data acquisition system with six data sources and other methods for a flight trajectory planning method for a drone data acquisition system provided in Example 1 of the present application. DETAILED DESCRIPTION

[0046] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and systems consistent with certain aspects of the present application, as detailed in the appended claims.

[0047] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0048] The following describes in detail the specific implementation methods, features and effects of the present invention in conjunction with the accompanying drawings and preferred embodiments.

[0049] The present invention is applicable to energy-constrained UAV data acquisition systems, including a UAV and K data sources. The initial energy of the UAV is , it acts as an air base station to collect ground data information, and the data collection area is The data source is randomly distributed in the area. The drone has five actions: east, west, south, north and hovering. When the drone is flying, the flight speed is constant. Meters / second, the flight time is discretized into a length of time slot, then time , n is a non-negative integer, and it can be concluded that the flight distance of the UAV in a time slot is , accordingly, the rectangular area of ​​the ground is also discretized into a grid, and the size of each grid is .

[0050] The flight altitude of the drone is constant at , in the time slot n With data source k The channel gain is

[0051] ,

[0052] in, is the channel gain at a reference distance of 1 meter, Is the data source k The coordinates of is the coordinate of the drone projected onto the ground, with the superscript T represents transpose, represents the magnitude of a vector;

[0053] The data source is connected to the UAV base station using frequency division multiplexing, and the bandwidth allocated to each data source is B , in the time slot n At the initial moment, drones and data sources k The communication rate is

[0054] ,

[0055] in, Is the data source k The transmission power, is the noise power, For data sources k The base station receiving signal-to-noise ratio at a reference distance of 1 meter is the total communication rate of all data sources.

[0056] ,

[0057] Taking into account Very small, you can think If the change in a time slot is linear, then a time slot (time slot n ) is:

[0058] ,

[0059] The energy consumption of drones includes three aspects: flight energy consumption, hovering energy consumption and communication energy consumption. Communication energy consumption refers to the energy consumption generated by collecting (receiving) data. It is much smaller than the first two and can be ignored.

[0060] The energy consumption of the drone in one time slot is

[0061] (5),

[0062] in They are respectively the UAV flight power and hovering power; if n The action of the time slot drone is hovering, ; otherwise it is in flight state, represented by .

[0063] Assume that the initial energy of the UAV for data collection mission is , it starts from a certain initial point Set out and collect data according to a certain flight path K The present invention relates to the problem of how to plan the flight path to maximize the total data collection of the system under the condition of limited energy of the UAV.

[0064] The overall operation process of the system is as follows:

[0065] First, the drone base station obtains the location information of the data source Next, the base station receiving signal-to-noise ratio at a reference distance of 1 meter is measured using the pilot signal. ; Data source k sends a pilot signal to the base station, the base station estimates the signal-to-noise ratio and then multiplies it by the distance loss , which is In this way, the UAV base station can calculate the communication rate according to its own coordinates using formula (2). Then, the UAV base station calculates the flight trajectory using a certain algorithm based on the location information of the data source, the signal-to-noise ratio of the base station received at a reference distance of 1 meter, and the altitude information. Finally, the UAV flies according to the planned path and collects data. Example

[0066] In this embodiment, the initial position of the drone is the origin (0, 0), and the flight altitude is , the ground area of ​​aerial projection is 600m×600m. One time slot , UAV flight speed , then the length of one grid The ground area is divided into a 100×100 grid, and a small multi-rotor drone with hovering power Set to 12W, flight power The power consumption is 25W, and there are 6 randomly distributed data sources in the ground area. The bandwidth of each data source is 1MHz, and .

[0067] The UAV flight trajectory calculation process is as follows Figure 1 As shown, it mainly includes:

[0068] S1: The drone obtains the location coordinates of the ground data source , and obtain the receiving signal-to-noise ratio at a reference distance of 1 meter by measuring ; The area where the drone is flying , divided into 100×100 grids, each grid size is 6m×6m;

[0069] S2: Initialize step size and weight: step size , weight w =0;

[0070] S3: Calculate the throughput according to the sub-algorithm and store it in an array b;

[0071] The sub-algorithm process mentioned in this step is as follows Figure 2 As shown, including:

[0072] S301: Initialize search strategy factors , search threshold , attenuation factor ; Reward discount rate Y = 0.5, learning rate α = 0.8, initialize three-dimensional (11 × 11 × 5) action The element of the value array Q is 0: Q( i , j , a )=0, i =0~10, j =0~10, . Constructing a structure array Used to store flight trajectory; identifier .

[0073] It should be noted that Q is constructed based on the state space S and action space A: , 0~4 represent the five actions of hovering, east, south, west, and north respectively;

[0074] S302: Initialize time index n =0; UAV initial position , drone energy ; System throughput ;like , put z(0) into middle;

[0075] S303: According to Greedy strategy selects actions a ( n ): Generate a random number D∈[0,1]; if D<ε, randomly select a ( n )∈A, otherwise , where (x(n), y(n)) are the coordinates of the drone’s position at the beginning of time slot n;

[0076] S304: UAV performs action a ( n ), the status changes to , get rewarded ;like , put z( n +1) Put middle;

[0077]

[0078] According to the above formula It can be calculated according to formula (2)-(3);

[0079] S305: Update several parameters.

[0080]

[0081] In the above formula, , Calculated according to formula (2)-(3); if n The action of the time slot is hover, ,otherwise .

[0082] S306: If E >0, let n := n +1, go to S303; if E ≤0 and , terminate the loop and go to the next step S307; if E ≤0 and , output flight trajectory and data collection volume , terminate the entire program;

[0083] S307: ;

[0084] S308: If ,make , and go to S302.

[0085] S4: Update weights: .

[0086] S5: If the weight w >1, terminate the loop, otherwise return to S3.

[0087] S6: Find the maximum value and corresponding index of array b, and find the optimal weight. First find the index corresponding to the maximum value of array b, which is recorded as , then the optimal weight is .

[0088] S7: Based on the optimal weight, the optimal flight trajectory is obtained using the same sub-algorithm as S3.

[0089] Figure 3 This is a schematic diagram of the flight trajectory of the drone data acquisition system based on 6 data sources and the method of the present invention, where the initial energy of the drone is Focus. "Greedy flight" means that the drone always flies in the direction with the highest communication rate. For the method of the present invention, the drone starts from the starting point (0, 0) and remains in a hovering state and collects data after reaching (54, 156). For the greedy flight strategy, the flight trajectory from the starting point (0, 0) to (54, 156) coincides with the flight trajectory of the proposed algorithm; however, after (54, 156), the drone continues to fly and finally reaches (216, 342). It should be noted that the data volume under the method of the present invention is 1317Mbit, while the data volume under the greedy flight is 978Mbi; the method of the present invention balances the communication rate and flight energy consumption through weights, and has a higher data collection volume.

[0090] Figure 4 The performance comparison chart of the method of the present invention and other methods in the UAV data collection system with 6 data sources is shown in the figure, where "stationary" means that the UAV always hovers at the initial position (origin). As the number of When it is smaller, , the curve of the method of the present invention is close to that of the static strategy; As the value of When the value is small or medium, there is a certain gap between the performance of the method of the present invention and that of the method of the present invention; when When is larger, its performance gradually approaches that of the proposed algorithm. It is obvious that the performance of the method of the present invention is the best among all the schemes.

[0091] An efficient flight trajectory planning method for an unmanned aerial vehicle (UAV) data collection system optimizes the UAV's flight path to improve data collection efficiency and quality. The method first involves the UAV acquiring precise location coordinates and signal-to-noise ratio information from a ground data source and then dividing the flight area into a grid, providing a foundation for subsequent path planning and data collection. Next, by initializing the step size and weights, a sub-algorithm calculates the system throughput and updates the weights until the optimal weights are found. The method employs an ε-greedy strategy for action selection, ensuring a balance between exploring new paths and leveraging known information. A reward function comprehensively considers flight energy consumption and data collection volume to guide the UAV in executing the optimal action. Furthermore, the design of the state space and action space takes into account the UAV's various flight maneuvers, increasing the method's flexibility and adaptability. Finally, the sub-algorithm is invoked based on the optimal weights to determine the optimal flight trajectory, maximizing data collection under energy-constrained conditions. Compared to baseline solutions, the proposed method exhibits higher data collection efficiency while minimizing feedback and system overhead, providing an effective solution for UAV flight trajectory planning in practical applications.

[0092] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A flight trajectory planning method for an unmanned aerial vehicle data acquisition system, characterized by: The steps include: S1. The drone obtains the location coordinates of the ground data source and obtains the received signal-to-noise ratio at a reference distance of 1 meter by measuring; the drone's flight area is divided into several grids; S2, initialize step size and weight; S3. Calculate the throughput based on the sub-algorithm and store it in an array; S4. Update the weight. If the weight is greater than 1, terminate the loop, otherwise return to S3. S5. Find the maximum value and corresponding index of the array and find the optimal weight; S6. Based on the optimal weight, call the sub-algorithm to find the optimal flight trajectory; The sub-algorithm of step S3 includes: S301: Initialize search strategy factors , search threshold , attenuation factor ; Reward discount rate , learning rate , action-value function , where S is the state space and A is the action space; construct the structure array Used to store flight trajectory; identifier ; S302: Initialization time index n=0; UAV initial position , drone energy ; System throughput ;like , put z(0) into middle; S303: According to The greedy strategy selects action a(n); S304: The drone performs action a(n), the state changes to z(n+1), and gets rewarded ,like , put z(n+1) into middle; S305: Update several parameters. ； Data collection volume , drone energy ,here is the amount of data collected and energy consumption in a time slot; S306: If E>0, set n:=n+1 and go to S303; if E≤0 and , terminate the loop and go to the next step S307; if E≤0 and , output flight trajectory and data collection volume , terminate the entire program; S307: ,like ,make , go to S302.

2. The flight trajectory planning method of a UAV data acquisition system according to claim 1, characterized in that: In step S1, the UAV flight area is divided into several grids. The rectangular area where the UAV flies is represented as: ; is divided into grids, each grid size is , , .

3. The flight trajectory planning method of a UAV data acquisition system according to claim 1, characterized in that: The initialization operation of step S2, the initialization step length , weight w=0.

4. The flight trajectory planning method of a UAV data acquisition system according to claim 1, characterized in that: The weight update in step S4 is expressed as: .

5. The flight trajectory planning method of a UAV data acquisition system according to claim 1, characterized in that: The optimal weight of step S6 is to first find the index corresponding to the maximum value of the array, which is recorded as , then the optimal weight is .

6. The flight trajectory planning method of a UAV data acquisition system according to claim 1, characterized in that: The state space S and action space A of step S301, ， in , , the superscript T is the transpose operation, , where 0~4 represent the five actions of hovering, east, south, west, and north respectively.

7. The flight trajectory planning method of a UAV data acquisition system according to claim 1, characterized in that: The step S303 Greedy strategy, generate random number v∈[0,1]; If v < ε, randomly select ,otherwise .

8. The flight trajectory planning method of a UAV data acquisition system according to claim 1, characterized in that: The reward function of step S304 is a weighted sum of the flight energy consumption per time slot and the amount of data collected, and a weighting factor of the reward function is given; ， Among them, 0≤w≤1 is the weight, is a flight slot duration, is the flight speed, They are respectively the UAV flight power and hovering power, and are two constants, is the communication rate at time n, expressed as: ， in, is the flight altitude of the UAV, K is the number of data sources, B is the bandwidth allocated to each data source, is the base station receiving signal-to-noise ratio for data source k at a reference distance of 1 meter, are the coordinates of the data source k, is the coordinate of the drone projected onto the ground at time n, Represents the magnitude of a vector.

9. The flight trajectory planning method of a UAV data acquisition system according to claim 8, characterized in that: The step S305 and ,in ; If the action at time n is hovering, ,otherwise .

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