Unmanned aerial vehicle task allocation and trajectory planning method with minimum energy consumption

Abstract

The invention discloses an unmanned aerial vehicle task allocation and trajectory planning method with minimized energy consumption, which comprises the following steps: step 1, dividing all monitoring areas into K clusters according to parameter information of an unmanned aerial vehicle assisted Internet of Things system, each monitoring area having only one data collection point; 2, assigning one unmanned aerial vehicle for each cluster to collect data of all sensor nodes in the corresponding cluster, determining an access sequence of each unmanned aerial vehicle at data collection points in the corresponding cluster based on a tabu search algorithm, and taking the access sequence of the data collection points with the shortest access distance as a trajectory planning result of the cluster; 3, the task completion time of each unmanned aerial vehicle is calculated, if the collected data can be delivered to the data center within a preset delay, the current clustering result and the access sequence of the data collection points in each cluster are determined as final results, otherwise, K is set to be equal to K + 1, and the step 1 is returned; and 4, according to a final result, enabling each unmanned aerial vehicle to carry out data collection and then deliver the data to a data center.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) assisted wireless communication, and in particular relates to a UAV task allocation and trajectory planning method with minimized energy consumption. Background Art

[0002] With the rapid development of the Internet of Things, sensor devices are widely deployed in various IoT scenarios. Since the computing power of most sensor nodes is limited, the generated sensor data needs to be transmitted to the data center for further analysis. For example, the sensor node can choose to transmit data directly to the data center, or establish a route for multi-hop transmission. Direct transmission or multi-hop transmission dramatically increases the energy consumption of sensor nodes, and the corresponding data transmission duration will also increase. For delay-sensitive IoT scenarios, it is a challenge for sensor nodes to transmit data to the data center within a predetermined delay.

[0003] In order to transmit the data of sensor nodes to the data center within a predetermined delay, the application of drones in the Internet of Things has attracted widespread attention in recent years. Specifically, the drone starts from the data center, flies to the corresponding sensor node and receives the data sent by the sensor node, then flies back to the data center and delivers the collected data to the data center. The main advantages of drones are their high mobility, adaptability and low cost.

[0004] However, since the performance and operation time of UAVs are restricted by limited onboard energy, in UAV-assisted IoT, in order to minimize the total energy consumption of all UAVs while satisfying the constraint of transmitting data from sensor nodes to the data center within a predetermined delay, it is crucial to reasonably assign data collection tasks to each UAV and optimize its flight trajectory. Summary of the invention

[0005] The purpose of the present invention is to propose a drone task allocation and trajectory planning method with minimized energy consumption in response to the above problems, so as to minimize the total energy consumption of all drones in the drone-assisted Internet of Things system while transmitting the data of sensor nodes to the data center within a predetermined delay.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] The present invention proposes a method for allocating and trajectories of unmanned aerial vehicles (UAVs) with minimized energy consumption, which is applied to a UAV-assisted Internet of Things system. The UAV-assisted Internet of Things system includes a data center, N sensor nodes, and K UAVs. The N sensor nodes are divided into M non-overlapping monitoring areas, wherein N, K, and M are all positive integers, and K is a variable with an initial value of 1. The method for allocating and trajectories of unmanned aerial vehicles (UAVs) with minimized energy consumption includes the following steps:

[0008] Step 1: Divide all monitoring areas into K clusters according to the parameter information of the UAV-assisted IoT system. The clustering result is the task allocation result. Each monitoring area has only one data collection point. The parameter information of the UAV-assisted IoT system includes the geographical location of the data center, the geographical location of the data collection point in each monitoring area, and the current number of drones K. The data collection point is the hovering point of the drone when collecting data from the sensor nodes in the corresponding monitoring area.

[0009] Step 2: Assign one UAV to each cluster to collect data from all sensor nodes in each monitoring area in the corresponding cluster, and determine the access order of each UAV to the data collection points in the corresponding cluster based on the taboo search algorithm. The access order of the data collection points with the shortest access distance is used as the trajectory planning result of the cluster.

[0010] Step 3: Calculate the task completion time of each drone and determine whether each drone can deliver the collected data to the data center within the predetermined delay, that is, the task completion time of each drone is less than or equal to the predetermined delay. If so, determine the current clustering result and the access order of the data collection points in each cluster as the final result. Otherwise, set K = K + 1 and return to step 1.

[0011] Step 4: According to the final result, each drone visits the data collection points in the corresponding monitoring area in the corresponding cluster in turn to collect data and then deliver it to the data center.

[0012] Preferably, all monitoring areas are divided into K clusters according to the parameter information of the UAV-assisted Internet of Things system, and the obtained clustering result is the task allocation result, which is as follows:

[0013] Step 1.1: Randomly select one data collection point from all the data collection points in the monitoring area as the cluster center, and the data collection points in the unselected monitoring area are non-cluster centers. The cluster center set and non-cluster center set are represented by the center point set and non-center point set respectively;

[0014] Step 1.2: Calculate the distance between all data collection points in the non-central point set and all data collection points in the central point set, and use the roulette wheel selection method to select one data collection point from the non-central point set and add it to the central point set. The probability of a data collection point in the non-central point set being selected is positively correlated with the minimum distance from the data collection point to all data collection points in the central point set, that is, the farther the data collection point in the non-central point set is from the data collection point in the central point set, the higher the probability of the data collection point being selected;

[0015] Step 1.3: Repeat step 1.2 until there are K data collection points in the center point set;

[0016] Step 1.4: Execute the i-th iteration, calculate the distance between each data collection point in the non-center point set and each data collection point in the center point set, and assign the data collection points in the non-center point set to the clusters corresponding to the data collection points in the center point set according to the proximity principle to obtain the clustering results. The proximity principle satisfies the following conditions:

[0017] With binary variable λ m,k represents the association between the data collection points in the non-central point set and the data collection points in the central point set. If the data collection point of the mth monitoring area is in the non-central point set and is assigned to the cluster corresponding to the kth data collection point in the central point set, then λ m,k =1, otherwise, λ m,k = 0, that is, in the i-th iteration, the data collection point of the m-th monitoring area in the non-central point set is assigned to the clustering result λ of the k-th data collection point in the central point set m,k (i) The formula is as follows:

[0018]

[0019] in, N m (i) and R k (i) the distance between N m (i) and R z (i) The distance between m (i) represents the set of non-central points at the i-th iteration The mth data collection point in R k (i) represents the center point set at the i-th iteration The kth data collection point in R z (i) represents the center point set at the i-th iteration The zth data collection point in, i = 1 ~ I P , I P is the first preset number of iterations, m=1~M, k=1~K, z=1~K;

[0020] Step 1.5: From the non-central point collection Randomly select a data collection point and calculate the data collection point and the center point set The distance of each data collection point in is selected from the central point using the roulette wheel selection method. Select 1 data collection point, for the data collection from the center point The data collection points selected in The closer the randomly selected data collection points are, the higher the probability of being selected;

[0021] Step 1.6: Exchange the two data collection points selected in step 1.5, that is, the data collection point selected as the cluster center is regarded as a cluster member, and the data collection point selected as a cluster member is regarded as the cluster center, and update λ m,k (i) If the quality of the newly formed clustering result is better, keep the update, otherwise restore to the clustering result before the update. The quality of the newly formed clustering result is calculated by the cost function D(λ m,k (i)) is evaluated, and the cost function D(λ m,k (i)) is smaller than the clustering result before updating, which means the quality of the newly formed clustering result is better. m,k The formula for (i) is as follows:

[0022]

[0023] in, Indicates the data centers C0 and R at the i-th iteration k (i) the distance between them;

[0024] Step 1.7: Set i = i + 1, and return to step 1.4 until I P After iterations, the final clustering result is obtained.

[0025] Preferably, the access order of each drone to the data collection points in the corresponding cluster is determined based on the taboo search algorithm, as follows:

[0026] Step 2.1: For any data collection point in a cluster, a greedy algorithm is used to generate the initial access order;

[0027] Step 2.2: Take the initial access order as the initial solution and use the 2-opt operation to generate a set of neighborhood solutions of the current solution, that is, select any two data collection points in the corresponding access order, express them as exchange pairs, flip the access order of the data collection points between the exchange pairs, and construct a neighborhood solution of the current solution;

[0028] Step 2.3: Select the optimal solution from the set of neighborhood solutions of the current solution, that is, the solution with the shortest flight distance. The flight distance d of the kth UAV is f,k , the formula is as follows:

[0029]

[0030] Among them, V k,l represents the data collection point visited by the k-th drone at the l-th point, represents the data center C0 and the first data collection point V visited by the kth drone k,1 The distance between represents the data collection point V visited by the kth drone k,land the data collection point V visited by the kth drone at the l+1th location k,l+1 The distance between represents the number of data collection points that the kth drone needs to visit, Indicates the data collection point last visited by the kth drone The distance between the data center and the data center is C0.

[0031] Step 2.4: If the optimal solution is the historical optimal solution, it is used as the current solution for the next iteration and the taboo table is updated. Otherwise, the suboptimal solution that is not in the taboo table is selected as the current solution for the next iteration and the taboo table is updated. The update of the taboo table means that if the optimal solution or suboptimal solution is not in the taboo table, it is added. Otherwise, the position of the optimal solution or suboptimal solution is updated to the end of the taboo table.

[0032] Preferably, the length of the taboo table of the taboo search algorithm satisfies the following formula:

[0033]

[0034] Among them, L T (j) represents the length of the taboo table at the jth iteration, j = 1~I T Represents the iterative index of the tabu search algorithm, I T represents the second preset number of iterations, L T,max Indicates the maximum allowed length of the taboo table, L T,min Indicates the minimum allowed length of the taboo table, Indicates rounding down.

[0035] Preferably, the mission completion time of each drone is calculated as follows:

[0036]

[0037] Among them, t k represents the task completion time of the kth UAV, C m represents the data collection point of the mth monitoring area, represents the set of data collection points that the kth drone needs to visit, t h,m represents the hovering time of the UAV in the mth monitoring area to collect data, t f,k represents the flight time of the kth UAV, H represents the flight altitude of the UAV, v h Indicates the speed of the drone when ascending or descending.

[0038] Preferably, when the sensor node sends data to the drone, a time division multiplexing protocol is used, and the data is sent in order from small to large channel gain.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The clustering algorithm adopted in this method can greatly reduce the computational complexity of task allocation, as low as one-third of the existing technology. By improving the length of the taboo table in the taboo search algorithm, the length of the taboo table can be changed with the search process, and there is no need to manually set the length of the taboo table, which greatly improves the trajectory planning performance and accuracy. According to the predetermined delay constraints of the drone data collection and the minimum drone energy consumption constraints, the clustering results of the monitoring area and the access order of the data collection points in each cluster can be determined, and the data collection task can be completed in hovering mode. By reducing the flight distance of each drone, the total energy consumption of all drones can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flow chart of the method for assigning and trajectories of unmanned aerial vehicle tasks with minimized energy consumption according to the present invention;

[0042] Figure 2 It is a schematic diagram of an application scenario of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the technical field of this application. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0045] like Figure 1-2 As shown, a method for minimizing energy consumption of unmanned aerial vehicle task allocation and trajectory planning is applied to a unmanned aerial vehicle assisted Internet of Things system. The unmanned aerial vehicle assisted Internet of Things system includes a data center, N sensor nodes and K unmanned aerial vehicles. The N sensor nodes are divided into M non-overlapping monitoring areas, wherein N, K and M are all positive integers, and K is a variable with an initial value of 1. The method for minimizing energy consumption of unmanned aerial vehicle task allocation and trajectory planning includes the following steps:

[0046] Step 1: Divide all monitoring areas into K clusters according to the parameter information of the UAV-assisted Internet of Things system. The clustering result is the task allocation result. Each monitoring area has only one data collection point. The parameter information of the UAV-assisted Internet of Things system includes the geographical location of the data center, the geographical location of the data collection point in each monitoring area, and the current number of drones K. The data collection point is the hovering point of the drone when collecting data from the sensor nodes in the corresponding monitoring area.

[0047] The application scenarios of this embodiment are as follows Figure 2 As shown in Figure 1, the UAV-assisted IoT system includes: N sensor nodes, K drones and 1 data center. The N sensor nodes are distributed in M ​​non-overlapping monitoring areas in the UAV-assisted IoT system. Figure 2 In the figure, all monitoring areas in the UAV-assisted IoT system are divided into K clusters. K UAVs are dispatched to collect data from sensor nodes. One UAV is associated with one cluster to access all monitoring areas in the corresponding cluster. Each UAV starts from the data center and visits all monitoring areas in turn within a predetermined delay according to the access order of the monitoring areas in the corresponding cluster. It then flies back to the data center and delivers the collected data to the data center. When the UAV collects data in one monitoring area, it needs to hover over the data collection point corresponding to the monitoring area. The sensor nodes collect data from the environment and send the collected data to the UAV.

[0048] This method focuses on clustering the monitoring area and optimizing the access order of the drone data collection points in the cluster. In a specific embodiment, a clustering algorithm based on partitioning around the center point is proposed to cluster the data collection points in the monitoring area according to their geographical locations. The clustering algorithm has better robustness to isolated points and has lower computational complexity than the partitioning algorithm around the center point. The clustering algorithm based on partitioning around the center point is as follows:

[0049] Input: The locations of data collection points in all monitoring areas, the number of clusters K and the first preset number of iterations I P ;

[0050] Output: A set of monitoring areas of K clusters.

[0051] In one embodiment, all monitoring areas are divided into K clusters according to the parameter information of the UAV-assisted Internet of Things system, and the obtained clustering result is the task allocation result, which is as follows:

[0052] Step 1.1: Randomly select one data collection point from all the data collection points in the monitoring area as the cluster center, and the data collection points in the unselected monitoring area are non-cluster centers. The cluster center set and non-cluster center set are represented by the center point set and non-center point set respectively;

[0053] Step 1.2: Calculate the distance between all data collection points in the non-central point set and all data collection points in the central point set, and use the roulette wheel selection method to select one data collection point from the non-central point set and add it to the central point set. The probability of a data collection point in the non-central point set being selected is positively correlated with the minimum distance from the data collection point to all data collection points in the central point set, that is, the farther the data collection point in the non-central point set is from the data collection point in the central point set, the higher the probability of the data collection point being selected;

[0054] Step 1.3: Repeat step 1.2 until there are K data collection points in the center point set;

[0055] Step 1.4: Execute the i-th iteration, calculate the distance between each data collection point in the non-center point set and each data collection point in the center point set, and assign the data collection points in the non-center point set to the clusters corresponding to the data collection points in the center point set according to the proximity principle to obtain the clustering results. The proximity principle satisfies the following conditions:

[0056] With binary variable λ m,k represents the association between the data collection points in the non-central point set and the data collection points in the central point set. If the data collection point of the mth monitoring area is in the non-central point set and is assigned to the cluster corresponding to the kth data collection point in the central point set, then λ m,k =1, otherwise, λ m,k = 0, that is, in the i-th iteration, the data collection point of the m-th monitoring area in the non-central point set is assigned to the clustering result λ of the k-th data collection point in the central point set m,k (i) The formula is as follows:

[0057]

[0058] in, N m (i) and R k (i) the distance between N m (i) and R z (i) The distance between m (i) represents the set of non-central points at the i-th iteration The mth data collection point in R k (i) represents the center point set at the i-th iteration The kth data collection point in R z (i) represents the center point set at the i-th iteration The zth data collection point in, i = 1 ~ I P , I Pis the first preset number of iterations, m=1~M, k=1~K, z=1~K;

[0059] Step 1.5: From the non-central point collection Randomly select a data collection point and calculate the data collection point and the center point set The distance of each data collection point in is selected from the central point set using the roulette wheel selection method. Select 1 data collection point, for the collection from the center point The data collection points selected in The closer the randomly selected data collection points are, the higher the probability of being selected;

[0060] Step 1.6: Exchange the two data collection points selected in step 1.5, that is, the data collection point selected as the cluster center is regarded as a cluster member, and the data collection point selected as a cluster member is regarded as the cluster center, and update λ m,k (i) If the quality of the newly formed clustering result is better, keep the update, otherwise restore to the clustering result before the update. The quality of the newly formed clustering result is calculated by the cost function D(λ m,k (i)) is evaluated, and the cost function D(λ m,k (i)) is smaller than the clustering result before updating, which means the quality of the newly formed clustering result is better. mk The formula for (i) is as follows:

[0061]

[0062] in, Indicates the data centers C0 and R at the i-th iteration k (i) the distance between them;

[0063] Step 1.7: Set i = i + 1, and return to step 1.4 until I P After iterations, the final clustering result is obtained, that is, the M monitoring areas are finally divided into K clusters.

[0064] In one embodiment, when the sensor node sends data to the drone, a time division multiplexing protocol is used, and the data is sent in order from small to large channel gain.

[0065] In order to ensure that the UAV can receive all the data generated by all sensor nodes in the monitoring area corresponding to the data collection point during each hovering time, considering that the channel gain obeys the large-scale fading model, the hovering time of the UAV at the data collection point depends on the data transmission time of the sensor node with the worst channel quality. In the process of the sensor node sending data to the UAV, the sensor node uses the time division multiplexing protocol to send data to the UAV in the order of channel gain from small to large.

[0066] Step 2: Assign one UAV to each cluster to collect data from all sensor nodes in each monitoring area in the corresponding cluster, and determine the access order of each UAV to the data collection points in the corresponding cluster based on the taboo search algorithm. The access order of the data collection points with the shortest access distance is used as the trajectory planning result of the cluster.

[0067] In one embodiment, the access order of each drone to the data collection points in the corresponding cluster is determined based on the taboo search algorithm, as follows:

[0068] Step 2.1: For any data collection point in a cluster, a greedy algorithm is used to generate the initial access order;

[0069] Step 2.2: Take the initial access order as the initial solution and use the 2-opt operation to generate a set of neighborhood solutions of the current solution, that is, select any two data collection points in the corresponding access order, express them as exchange pairs, flip the access order of the data collection points between the exchange pairs, and construct a neighborhood solution of the current solution;

[0070] Step 2.3: Select the optimal solution from the set of neighborhood solutions of the current solution, that is, the solution with the shortest flight distance. The flight distance d of the kth UAV is f,k , the formula is as follows:

[0071]

[0072] Among them, V k,l represents the data collection point visited by the k-th drone at the l-th point, represents the data center C0 and the first data collection point V visited by the kth drone k,1 The distance between represents the data collection point V visited by the kth drone k,l and the data collection point V visited by the kth drone at the l+1th location k,l+1 The distance between represents the number of data collection points that the kth drone needs to visit, Indicates the data collection point last visited by the kth drone The distance between the data center and the data center is C0.

[0073] Step 2.4: If the optimal solution is the historical optimal solution, it is used as the current solution for the next iteration and the taboo table is updated. Otherwise, the suboptimal solution that is not in the taboo table is selected as the current solution for the next iteration and the taboo table is updated. The update of the taboo table means that if the optimal solution or suboptimal solution is not in the taboo table, it is added. Otherwise, the position of the optimal solution or suboptimal solution is updated to the end of the taboo table.

[0074] Specifically, for any UAV k, trajectory planning is performed based on the taboo search algorithm, and its trajectory planning performs the following operations:

[0075] Input: The set of monitoring areas associated with the kth drone and the second preset number of iterations I T ;

[0076] Output: The order in which the kth UAV visits the data collection points in its corresponding cluster;

[0077] The greedy algorithm is used to generate the initial access sequence. Specifically, the drone corresponding to the cluster starts from the data center and visits the data collection point closest to the current location each time. After visiting all data collection points in the cluster, it returns to the data center. The taboo table is a container used to store taboo objects in the taboo search algorithm. Taboo objects refer to solutions that are temporarily prohibited from being searched again during the search process. In the taboo search algorithm of this embodiment, an adaptive taboo table length L T (j) is proposed, so that the length of the taboo table can be changed during the search process, without manually setting the length of the taboo table, which greatly improves the search performance and accuracy.

[0078] In one embodiment, the length of the taboo table of the taboo search algorithm satisfies the following formula:

[0079]

[0080] Among them, L T (j) represents the length of the taboo table at the jth iteration, j = 1~I T Represents the iterative index of the tabu search algorithm, I T represents the second preset number of iterations, L T,max Indicates the maximum allowed length of the taboo table, L T,min Indicates the minimum allowed length of the taboo table, Indicates rounding down.

[0081] Step 3: Calculate the task completion time of each drone and determine whether each drone can deliver the collected data to the data center within the predetermined delay, that is, the task completion time of each drone is less than or equal to the predetermined delay. If so, determine the current clustering result and the access order of the data collection points in each cluster as the final result. Otherwise, set K=K+1 and return to step 1.

[0082] In one embodiment, the mission completion time of each drone is calculated as follows:

[0083]

[0084] Among them, t k represents the task completion time of the kth UAV, C mrepresents the data collection point of the mth monitoring area, represents the set of data collection points that the kth drone needs to visit, t h,m represents the hovering time of the UAV in the mth monitoring area to collect data, t f,k represents the flight time of the kth UAV, H represents the flight altitude of the UAV, v h Indicates the speed of the drone when it ascends or descends. The drone's mission completion time includes data collection time (hover time) and flight time. The scheduled delay is the preset time threshold, which can be adjusted according to actual needs.

[0085] Step 4: According to the final result, each drone visits the data collection points in the corresponding monitoring area in the corresponding cluster in turn to collect data and then deliver it to the data center.

[0086] The clustering algorithm adopted in this method can greatly reduce the computational complexity of task allocation, as low as one-third of the existing technology. By improving the length of the taboo table in the taboo search algorithm, the length of the taboo table can be changed with the search process, and there is no need to manually set the length of the taboo table, which greatly improves the trajectory planning performance and accuracy. The clustering results of the monitoring area and the access order of the data collection points in each cluster can be determined according to the predetermined delay constraints of the drone data collection and the minimum drone energy consumption constraints, and the data collection task can be completed in a hovering mode. By reducing the flight distance of each drone, the total energy consumption of all drones can be reduced. Finally, after the clustering results and the access order of the data collection points in each cluster are determined, each drone visits the data collection points in the corresponding monitoring area in the corresponding cluster in turn to collect data and then delivers it to the data center.

[0087] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] The above-described embodiments only express the more specific and detailed embodiments described in this application, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of this application, which all belong to the protection scope of this application. Therefore, the protection scope of the patent application shall be based on the attached claims.

Claims

1. A method for UAV task allocation and trajectory planning with minimized energy consumption, applied to a UAV-assisted Internet of Things system, characterized by: The UAV-assisted Internet of Things system includes a data center, N sensor nodes and K UAVs, wherein the N sensor nodes are divided into M non-overlapping monitoring areas, wherein N, K and M are all positive integers, and K is a variable with an initial value of 1. The UAV task allocation and trajectory planning method with minimized energy consumption includes the following steps: Step 1: Divide all monitoring areas into K clusters according to the parameter information of the UAV-assisted Internet of Things system. The clustering result is the task allocation result. Each monitoring area has only one data collection point. The parameter information of the UAV-assisted Internet of Things system includes the geographical location of the data center, the geographical location of the data collection point in each monitoring area, and the number of current drones K. The data collection point is the hovering point of the drone when collecting data from the sensor nodes in the corresponding monitoring area. Step 2: Assign one UAV to each cluster to collect data from all sensor nodes in each monitoring area in the corresponding cluster, and determine the access order of each UAV to the data collection points in the corresponding cluster based on the taboo search algorithm. The access order of the data collection points with the shortest access distance is used as the trajectory planning result of the cluster. Step 3: Calculate the task completion time of each drone and determine whether each drone can deliver the collected data to the data center within the predetermined delay, that is, the task completion time of each drone is less than or equal to the predetermined delay. If so, determine the current clustering result and the access order of the data collection points in each cluster as the final result. Otherwise, set K = K + 1 and return to step 1. Step 4: According to the final result, each drone is ordered to visit the data collection points in the corresponding monitoring area in the corresponding cluster in turn to collect data and then deliver it to the data center.

2. The method for assigning and trajectories of unmanned aerial vehicles with minimized energy consumption as claimed in claim 1, characterized in that: According to the parameter information of the UAV-assisted IoT system, all monitoring areas are divided into K clusters, and the obtained clustering result is the task allocation result, which is as follows: Step 1.1: Randomly select one data collection point from all the data collection points in the monitoring area as the cluster center, and the data collection points in the unselected monitoring area are non-cluster centers. The cluster center set and non-cluster center set are represented by the center point set and non-center point set respectively; Step 1.2: Calculate the distance between all data collection points in the non-central point set and all data collection points in the central point set, and use the roulette wheel selection method to select one data collection point from the non-central point set and add it to the central point set. The probability of a data collection point in the non-central point set being selected is positively correlated with the minimum distance from the data collection point to all data collection points in the central point set, that is, the farther the data collection point in the non-central point set is from the data collection point in the central point set, the higher the probability of the data collection point being selected; Step 1.3: Repeat step 1.2 until there are K data collection points in the center point set; Step 1.4: Execute the i-th iteration, calculate the distance between each data collection point in the non-center point set and each data collection point in the center point set, and assign the data collection points in the non-center point set to the clusters corresponding to the data collection points in the center point set according to the proximity principle to obtain the clustering result. The proximity principle satisfies the following conditions: With binary variable λ m,k represents the association between the data collection points in the non-central point set and the data collection points in the central point set. If the data collection point of the mth monitoring area is in the non-central point set and is assigned to the cluster corresponding to the kth data collection point in the central point set, then λ m,k =1, otherwise, λ m,k = 0, that is, in the i-th iteration, the data collection point of the m-th monitoring area in the non-central point set is assigned to the clustering result λ of the k-th data collection point in the central point set m,k (i) The formula is as follows: in, N m (i) and R k (i) the distance between N m (i) and R z (i) The distance between m (i) represents the set of non-central points at the i-th iteration The mth data collection point in R k (i) represents the center point set at the i-th iteration The kth data collection point in R z (i) represents the center point set at the i-th iteration The zth data collection point in, i = 1 ~ I P , I P is the first preset number of iterations, m=1~M, k=1~K, z=1~K; Step 1.5: From the non-central point collection Randomly select a data collection point and calculate the data collection point and the center point set The distance of each data collection point in is selected from the central point using the roulette wheel selection method. Select 1 data collection point, for the data collection from the center point The data collection points selected in The closer the randomly selected data collection points are, the higher the probability of being selected; Step 1.6: Exchange the two data collection points selected in step 1.5, that is, the data collection point selected as the cluster center is regarded as a cluster member, and the data collection point selected as a cluster member is regarded as the cluster center, and update λ m,k (i) If the quality of the newly formed clustering result is better, keep updating, otherwise restore to the clustering result before updating. The quality of the newly formed clustering result is calculated by the cost function D(λ m,k (i)) is evaluated, the cost function D(λ m,k (i)) is smaller than the clustering result before updating, which means the quality of the newly formed clustering result is better. m,k The formula for (i) is as follows: in, Indicates the data centers C0 and R at the i-th iteration k (i) the distance between them; Step 1.7: Set i = i + 1, and return to step 1.4 until I P After iterations, the final clustering result is obtained.

3. The method for assigning and trajectories of unmanned aerial vehicle tasks with minimized energy consumption as claimed in claim 1, characterized in that: The taboo search algorithm is used to determine the access order of each drone to the data collection points in the corresponding cluster, as follows: Step 2.1: For any data collection point in a cluster, a greedy algorithm is used to generate the initial access order; Step 2.2: Take the initial access order as the initial solution and use the 2-opt operation to generate a set of neighborhood solutions of the current solution, that is, select any two data collection points in the corresponding access order, express them as exchange pairs, flip the access order of the data collection points between the exchange pairs, and construct a neighborhood solution of the current solution; Step 2.3: Select the optimal solution from the set of neighborhood solutions of the current solution, that is, the solution with the shortest flight distance. The flight distance d of the kth UAV is f,k , the formula is as follows: Among them, V k,l represents the data collection point visited by the k-th drone at the l-th point, represents the data center C0 and the first data collection point V visited by the kth drone k,1 The distance between represents the data collection point V visited by the kth drone k,l and the data collection point V visited by the kth drone at the l+1th location k,l+1 The distance between represents the number of data collection points that the kth drone needs to visit, Indicates the data collection point last visited by the kth drone The distance between the data center and the data center is C0. Step 2.4: If the optimal solution is the historical optimal solution, it is used as the current solution for the next iteration and the taboo table is updated. Otherwise, the suboptimal solution that is not in the taboo table is selected as the current solution for the next iteration and the taboo table is updated. The update of the taboo table is to add the optimal solution or the suboptimal solution to the taboo table if it is not in the taboo table, otherwise update the position of the optimal solution or the suboptimal solution to the end of the taboo table.

4. The method for assigning and trajectories of unmanned aerial vehicle tasks with minimized energy consumption as claimed in claim 1 or 3, characterized in that: The length of the taboo table of the taboo search algorithm satisfies the following formula: Among them, L T (j) represents the length of the taboo table at the jth iteration, j = 1~I T Represents the iterative index of the tabu search algorithm, I T represents the second preset number of iterations, L T,max Indicates the maximum allowed length of the taboo table, L T,min Indicates the minimum allowed length of the taboo table, Indicates rounding down.

5. The method for assigning and trajectories of unmanned aerial vehicle tasks with minimized energy consumption as claimed in claim 1, characterized in that: The mission completion time of each drone is calculated as follows: Among them, t k represents the task completion time of the kth UAV, C m represents the data collection point of the mth monitoring area, represents the set of data collection points that the kth drone needs to visit, t h,m represents the hovering time of the UAV in the mth monitoring area to collect data, t f,k represents the flight time of the kth UAV, H represents the flight altitude of the UAV, v h Indicates the speed of the drone when ascending or descending.

6. The method for assigning and trajectories of unmanned aerial vehicle tasks with minimized energy consumption as claimed in claim 1, characterized in that: When the sensor node sends data to the UAV, a time division multiplexing protocol is used, and the data is sent in sequence from small to large channel gain.

Images