Method, device and equipment for dynamic tracking control of multiple drones to multiple unmanned vehicles

Abstract

The present invention discloses a method, device and equipment for dynamic tracking control of multiple unmanned vehicles by multiple unmanned aerial vehicles. The present invention relates to the field of coverage control technology and is used to solve the problem in the prior art that multiple unmanned vehicles cannot be dynamically tracked and covered. The method includes obtaining the location information of multiple unmanned vehicles, determining the distribution range, and obtaining a location information set of the unmanned vehicles; obtaining the initial positions of multiple unmanned aerial vehicles, using the initial positions as the origin, and using the k-means algorithm to determine the desired positions of the multiple unmanned aerial vehicles based on the location information set; determining the target number of unmanned aerial vehicles required to track all unmanned vehicles based on the tracking coverage range and the desired position of the multiple unmanned aerial vehicles; and driving the target number of unmanned aerial vehicles to the desired position to complete the tracking and coverage task of multiple unmanned vehicles. According to the position changes of the unmanned vehicles, a path is generated in real time, and the flight position and number of the unmanned aerial vehicles are determined according to the distribution of the unmanned vehicles to ensure flexibility and coverage effect, thereby achieving dynamic tracking and coverage.

Description

Technical Field

[0001] The present invention relates to the field of coverage control technology, and in particular to a method, device and equipment for dynamic tracking control of multiple unmanned aerial vehicles to multiple unmanned vehicles. Background Art

[0002] With the development of coverage control theory and the continuous advancement of drone technology, the application areas and scope of drone coverage missions are constantly expanding. After decades of development, drones have become relatively mature from a technical perspective. Their advantages include low cost, ease of operation, and high flexibility. They can carry important equipment and perform specialized missions from the air, such as aerial monitoring, surveillance, message relaying, public announcements, and emergency rescue. When performing these specialized missions, they generally do not cause casualties. Their strong survivability, excellent maneuverability, and ease of use make them crucial for responding to natural disasters, accidents, and social security incidents. Unmanned vehicles (UAVs) can be used in specific scenarios in real life. For example, in disaster-stricken areas, ground infrastructure is often easily damaged. Therefore, when using UAVs for tasks such as supply delivery, search and rescue, or environmental monitoring, UAVs are often required to monitor the vehicles.

[0003] Therefore, there is an urgent need to provide a more reliable multi-UAV to multi-UV dynamic tracking control solution. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device and equipment for dynamic tracking control of multiple unmanned vehicles by multiple unmanned aerial vehicles, so as to solve the problem that the existing technology cannot dynamically track and cover multiple unmanned vehicles.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for dynamic tracking and control of multiple unmanned vehicles by multiple unmanned aerial vehicles, the method comprising:

[0007] Obtain the location information of multiple unmanned vehicles, determine the distribution range of all unmanned vehicles, and obtain the location information set of multiple unmanned vehicles; multiple unmanned vehicles are in a dynamic operation process;

[0008] Get the initial positions of multiple drones;

[0009] Taking the initial position as the origin, using a k-means algorithm based on the position information set to determine the desired positions of the multiple drones;

[0010] Determine tracking coverage for multiple drones;

[0011] Determining a target number of drones required to track all unmanned vehicles based on the tracking coverage and the desired location;

[0012] Drive the target number of drones to the desired location to complete the tracking coverage task of multiple unmanned vehicles.

[0013] In a second aspect, the present invention provides a multi-UAV to multi-UAV dynamic tracking control device, the device comprising:

[0014] The unmanned vehicle location information set determination module is used to obtain the location information of multiple unmanned vehicles, determine the distribution range of all unmanned vehicles, and obtain the location information set of multiple unmanned vehicles; multiple unmanned vehicles are in a dynamic operation process;

[0015] UAV initial position acquisition module, used to obtain the initial positions of multiple UAVs;

[0016] A UAV expected position determination module is used to determine the expected positions of multiple UAVs based on the position information set using a k-means algorithm with the initial position as the origin;

[0017] A UAV tracking coverage determination module is used to determine the tracking coverage of multiple UAVs;

[0018] A UAV target quantity determination module is used to determine the target quantity of UAVs required to track all unmanned vehicles based on the tracking coverage and the expected position;

[0019] The UAV driving module is used to drive the target number of UAVs to the desired location to complete the tracking and coverage task of multiple unmanned vehicles.

[0020] In a third aspect, the present invention provides a multi-UAV to multi-UAV dynamic tracking control device, the device comprising:

[0021] Communication unit / communication interface, used to obtain the location information of multiple unmanned vehicles, determine the distribution range of all unmanned vehicles, and obtain the location information set of multiple unmanned vehicles; multiple unmanned vehicles are in a dynamic operation process;

[0022] Get the initial positions of multiple drones;

[0023] a processing unit / processor configured to determine the desired positions of the plurality of drones based on the position information set using a k-means algorithm with the initial position as an origin;

[0024] Determine tracking coverage for multiple drones;

[0025] Determining a target number of drones required to track all unmanned vehicles based on the tracking coverage and the desired location;

[0026] Drive the target number of drones to the desired location to complete the tracking coverage task of multiple unmanned vehicles.

[0027] In a fourth aspect, the present invention provides a computer storage medium, wherein instructions are stored in the computer storage medium. When the instructions are executed, the above-mentioned multi-UAV to multi-UAV dynamic tracking control method is implemented.

[0028] Compared to existing technologies, the present invention provides a dynamic tracking control scheme for multiple unmanned vehicles (UAVs). By acquiring the location information of multiple UAVs and determining the distribution range of all UAVs, a set of location information for multiple UAVs in dynamic operation is obtained. The initial positions of the multiple UAVs are obtained, and using the initial positions as the origin, a k-means algorithm is used to determine the desired locations of the multiple UAVs based on the location information set. The tracking coverage range of the multiple UAVs is then determined. Based on the tracking coverage range and the desired locations, the target number of UAVs required to track all UAVs is determined. The target number of UAVs is then driven to the desired locations, completing the tracking coverage task for the multiple UAVs. Using UAVs as control objects and ground-based UAVs as coverage targets, the k-means algorithm is used to determine the desired locations of the UAVs. Paths are generated in real time based on the changes in the UAVs' positions, ensuring flexibility and coverage. The flight positions and number of UAVs are determined based on the distribution of the UAVs, thereby achieving dynamic tracking coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0030] Figure 1 Schematic diagram of the dynamic tracking control process of multiple UAVs to multiple unmanned vehicles provided by the present invention;

[0031] Figure 2 This is a schematic diagram of the UAV flight control principle;

[0032] Figure 3 shows Figures 3A-3D The four figures are schematic diagrams of the tracking coverage effect when the number of drones remains unchanged;

[0033] Figure 4 shows Figures 4A-4D The four figures are schematic diagrams of the tracking coverage effect when additional drones are deployed;

[0034] Figure 5 shows Figures 5A-5D The four figures are schematic diagrams of the tracking coverage effect when withdrawing the drone;

[0035] Figure 6This is a schematic diagram of the structure of a multi-UAV to multi-UAV dynamic tracking control device;

[0036] Figure 7 This is a schematic diagram of the structure of the multi-UAV to multi-UAV dynamic tracking control device provided by the present invention. DETAILED DESCRIPTION

[0037] To facilitate a clear description of the technical solutions of the embodiments of the present invention, the words "first" and "second" are used in the embodiments of the present invention to distinguish between identical or similar items with substantially the same functions and effects. For example, the first threshold and the second threshold are merely used to distinguish between different thresholds and do not limit their order. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or execution order, and the words "first" and "second" do not necessarily mean different.

[0038] It should be noted that, in this disclosure, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this disclosure should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0039] In the present invention, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0040] In the prior art, existing coverage control is mostly about regional coverage issues. Currently, in the research on regional coverage control based on Voronoi diagram (Thyssen polygon or Dirichlet diagram), a preset coverage path is generally adopted. However, when this coverage control method performs tasks in a complex and changeable environment, when the coverage target leaves the coverage area, the coverage path cannot be flexibly adjusted, so that the target cannot be effectively covered. When there is no coverage target in a certain area, the coverage of the area will cause a certain degree of resource loss. The coverage path cannot be flexibly adjusted, lacks flexibility, and cannot achieve dynamic tracking coverage.

[0041] To this end, the present invention provides a dynamic tracking control solution for multiple unmanned vehicles to multiple unmanned vehicles.

[0042] Next, the solutions provided in the embodiments of this specification are described with reference to the accompanying drawings:

[0043] Figure 1 The schematic diagram of the dynamic tracking control process of multiple UAVs to multiple unmanned vehicles provided by the present invention is shown in FIG1 . The process may include the following steps:

[0044] Step 110: Acquire location information of multiple unmanned vehicles, determine the distribution range of all unmanned vehicles, and obtain a set of location information of multiple unmanned vehicles; multiple unmanned vehicles are in a dynamic operation process.

[0045] Unmanned vehicles are in dynamic operation on the ground, performing tasks in practical application scenarios, such as environmental monitoring, material transportation, and search and rescue. Their formations often change due to different missions and geographical environments. For example, during search and rescue operations, unmanned vehicles need to perform search and rescue tasks separately according to pre-demarcated areas. At this point, many unmanned vehicles may be dispersed for search and rescue operations. However, when passing through narrow sections of road, unmanned vehicles may gather together, forming a clustered distribution.

[0046] Therefore, when the unmanned vehicles are in dynamic operation, the distribution positions of the unmanned vehicles can be dynamically obtained to determine the distribution range of all unmanned vehicles.

[0047] The location information set in step 110 may include location information of all unmanned vehicles, wherein the location information may be location coordinates, distance values, and the like.

[0048] Step 120: Obtain the initial positions of multiple drones.

[0049] In practice, the drone and the unmanned vehicle do not depart simultaneously. Therefore, the initial position of the drone can only be determined by adjusting it based on the distribution of the unmanned vehicles. Furthermore, in practice, the drone needs to track and cover the unmanned vehicle, so theoretically, the drone's flight speed must match the vehicle's operating speed, and the speed difference between the two should not be too large.

[0050] Step 130: Taking the initial position as the origin, a k-means algorithm is used to determine the desired positions of the multiple drones based on the position information set.

[0051] The k-means algorithm is an indirect clustering method based on the similarity measurement between samples and is an unsupervised learning method. This algorithm uses k as a parameter to divide n objects into k clusters so that the similarity within the cluster is high and the similarity between clusters is low. The similarity is calculated based on the average value of the objects in a cluster (regarded as the center of gravity of the cluster). This algorithm first randomly selects k objects, each object represents the centroid of a cluster. For each of the remaining objects, it is assigned to the cluster that is most similar to it based on the distance between the object and the centroid of each cluster. Then, the new centroid of each cluster is calculated. The above process is repeated until the criterion function converges. The k-means algorithm is a typical point-by-point modification and iterative dynamic clustering algorithm. The key point is to use the sum of squared errors as the criterion function. Modify the class center point by point: after a pixel sample is classified into a certain group of classes according to a certain principle, the mean of this group of classes must be recalculated, and the new mean is used as the center of aggregation for the next clustering of the pixels; modify the class center batch by batch: after all pixel samples are classified according to the class center of a certain group, the mean of each class is calculated and modified as the center of aggregation for the next classification.

[0052] Taking the initial position of the UAV as the origin and the obtained position information of the unmanned vehicle as the basic data, the k-means algorithm can be used to calculate the expected positions of multiple UAVs, and then determine the tracking coverage of multiple UAVs.

[0053] Step 140: Determine the tracking coverage of the multiple drones.

[0054] During monitoring, the number of drones used depends on their coverage area. Drones used in specific applications all have the same coverage area. Therefore, the number of drones used and their flight paths can be adjusted in real time based on the real-time dynamic distribution of unmanned vehicles. In practical applications, the number of drones and their desired locations are determined based on the distribution area of unmanned vehicles. The time required to fly a drone to the desired location is also relatively short. To reduce server workload, the distribution area of unmanned vehicles is not collected in real time. Instead, the locations of unmanned vehicles are collected at intervals, for example, every 1 second. Therefore, if the number of drones or their flight locations need to be adjusted, they must be controlled immediately before the next collection to ensure comprehensive tracking and coverage of the unmanned vehicles.

[0055] Step 150: Based on the tracking coverage and the desired location, determine the target number of drones required to track all unmanned vehicles.

[0056] More specifically, when determining whether to withdraw drones or deploy additional drones, we can first compare the total tracking coverage of all currently used drones with the distribution range of all unmanned vehicles, and then further determine the specific number of drones that need to be deployed or withdrawn, the expected location of the drones, etc.

[0057] Step 160: Drive the target number of drones to the desired location to complete the tracking and coverage task for multiple unmanned vehicles. The subject that drives the drones to the desired location can be set according to the actual application scenario.

[0058] Figure 1 The method obtains the location information of multiple unmanned vehicles, determines the distribution range of all unmanned vehicles, and obtains the location information set of multiple unmanned vehicles in dynamic operation. The initial positions of multiple drones are obtained, and using the initial positions as the origin, the k-means algorithm is used to determine the desired positions of the multiple drones based on the location information set, and the tracking coverage range of the multiple drones is determined. Based on the tracking coverage range and the desired positions, the target number of drones required to track all the unmanned vehicles is determined. The target number of drones is driven to the desired positions to complete the tracking coverage task of multiple unmanned vehicles. With drones as the control objects and ground-based unmanned vehicles as the coverage targets, the k-means algorithm is used to determine the desired positions of the drones. Based on the position changes of the unmanned vehicles, paths are generated in real time to ensure flexibility and coverage. The flight positions and number of drones are determined based on the distribution of the unmanned vehicles, thereby achieving dynamic tracking coverage.

[0059] based on Figure 1The present specification also provides some specific implementation methods of the method, which are described below.

[0060] Optionally, taking the initial position as the origin and using a k-means algorithm based on the position information set to determine the desired positions of the multiple drones may specifically include:

[0061] Taking the initial position as the origin, clustering the position information of multiple unmanned vehicles in the position information set by using a k-means algorithm, and obtaining cluster centers;

[0062] The positions of the cluster centers are determined as the desired positions of the multiple UAVs.

[0063] Furthermore, the location information set contains location information corresponding to n unmanned vehicles, where n is a positive integer greater than 1;

[0064] Taking the initial position as the origin, clustering the position information of multiple unmanned vehicles in the position information set by using the k-means algorithm and obtaining cluster centers may specifically include:

[0065] Randomly select k vehicles from n unmanned vehicles and use their positions as the first cluster center. in is the initial position of the jth cluster center;

[0066] According to formula (1), the distance ρ from each unmanned vehicle to the k first cluster centers is calculated. ij :

[0067]

[0068] in, is the set of targets in the cluster where the J-th UAV is located obtained after λ times of iterative calculation;

[0069] All unmanned vehicles are divided into ij Assign it to the cluster represented by the nearest cluster center, and calculate the second cluster center according to formula (2):

[0070]

[0071] In formula (2): is the set of targets in the cluster where the J-th UAV is located obtained after λ times of iterative calculation; for The number of targets in the cluster; is the expected position of the J-th UAV after λ+1 iterations;

[0072] Determine whether the calculated second cluster center satisfies C λ+1 =C λ, if satisfied, then output the cluster center position C λ+1 ; If not satisfied, return to formula (1) to calculate the distance from the center.

[0073] Determine tracking coverage for multiple drones, including:

[0074] Calculate the coverage of each drone when it reaches the desired location, and finally obtain the total coverage of all drones;

[0075] The calculation of the coverage of each drone when it reaches the desired location, and finally obtaining the total coverage of all drones, specifically includes:

[0076] Using formula (3):

[0077]

[0078] Calculate the coverage area of each drone when it reaches the desired location;

[0079] in, is the coverage of the Jth UAV at the τth time, R is the coordinate of any point, is the expected position of the Jth UAV at the τth time, and r is the coverage radius of each UAV;

[0080] According to the coverage, formula (4) is used:

[0081]

[0082] The total coverage of all drones is calculated for the τth time, where k is the number of drones in the area to be covered.

[0083] When determining the coverage area of the unmanned vehicle, the following methods can be used:

[0084] According to formula (5), the distance from each unmanned vehicle to the desired position of the UAV is calculated

[0085]

[0086] Will Compare with the coverage radius r to determine whether formula (6) holds. If so, it means that some unmanned vehicles are not covered, and the number of drones is set to k = k + 1; otherwise, it means that all unmanned vehicles are covered.

[0087]

[0088] Determine whether formula (7) holds. If it does, it means that there is repeated coverage. Set the number of drones k = k-1 and set the expected position of the redundant drones as the origin:

[0089]

[0090] Among them, Q w(t) represents the set of all unmanned vehicle positions covered by the w-th drone.

[0091] When the current position of the UAV reaches the desired position, the UAV position must satisfy formula (8):

[0092]

[0093] Where P is the current position of the UAV; δ is the maximum allowable error.

[0094] In actual applications, when determining the number of drones and the expected location of drones, you can first determine whether the current drones can cover all the unmanned vehicles based on the total coverage of the current drones and the distribution range of all unmanned vehicles. If not, you can specifically determine the location information of the uncovered drones, determine the number of drones that need to be dispatched, and the expected location of the drones; if the current drones can fully cover the current unmanned vehicles, you can further determine whether there are redundant drones, that is, whether there are drones within the coverage area where there are no unmanned vehicles. If so, you can withdraw the drone to avoid wasting resources. That is, if there is incomplete coverage of an unmanned vehicle, the number of drones is increased by one, and the judgment is returned again; if there is repeated coverage of an unmanned vehicle, the number of drones is reduced by one, and the judgment is returned again until the unmanned vehicle is completely and non-repeatedly covered; the specific implementation steps may include:

[0095] Based on the total coverage of all drones and the distribution range of all drones, determine whether all unmanned vehicles are within the coverage of the current drone;

[0096] If all unmanned vehicles are not within the coverage of the current drone, the distribution locations of unmanned vehicles outside the coverage of the current drone are determined;

[0097] Based on the distribution of unmanned vehicles outside the current drone coverage area, the number of drones that need to be dispatched and the corresponding target locations are determined.

[0098] Optionally, after determining whether all unmanned vehicles are within the coverage of the current drone based on the total coverage of all drones and the distribution range of all drones, the following steps may also be performed:

[0099] If all unmanned vehicles are within the coverage of the current drone, it is determined whether there are redundant drones; the redundant drones refer to drones that have no unmanned vehicles within their coverage.

[0100] If there are redundant drones, the redundant drones will be withdrawn.

[0101] Through the above method, a path is generated in real time according to the position changes of the coverage target, ensuring flexibility and coverage effect. At the same time, the problem of resource loss is taken into account in the tracking and coverage process. When drone redundancy occurs, the excess drones can be withdrawn. If more drones are needed to complete the coverage task together, additional drones are dispatched, thus achieving dynamic tracking coverage.

[0102] After determining the target number of drones required for all unmanned vehicles, the target number of drones can be driven to the desired location to complete the tracking coverage task of multiple unmanned vehicles. Specifically, a quad-rotor drone control system can be constructed; the quad-rotor drone control system consists of a position control subsystem as an outer loop and an attitude control subsystem as an inner loop;

[0103] The quad-rotor UAV control system is based on sliding mode control to achieve motion control of the UAV, drive the target number of UAVs to the desired position, and complete the tracking coverage task of multiple unmanned vehicles.

[0104] More specifically, it can be combined with Figure 2 To explain, Figure 2 This is a schematic diagram of the UAV flight control principle. Figure 2 As shown, the position control subsystem controller is used to receive the position command (x d ,y d , z d ), the desired pitch angle θ is calculated using the coupling of the UAV d , roll angle ψ d and control input u1; pitch angle θ d 、 Roll angle ψ d There is also the angle φ issued by the command d The three angles are transmitted to the attitude subsystem controller, which tracks the three attitude angles, controlling the six outputs with four inputs. The controller then calculates three control inputs (u2, u3, and u4) and gives them to the attitude subsystem, which then calculates the attitude outputs (ψ, θ, and φ). The attitude outputs are then transmitted to the position subsystem, which calculates the position outputs x, y, and z based on the input u1 and the attitude outputs (ψ, θ, and φ). The command is then resolved to determine what inputs are required to implement the command.

[0105] Based on the obtained quadrotor UAV control system, design the control law and sliding mode function;

[0106] Based on the obtained control law, the attitude angle of the position control subsystem is calculated. At the same time, the attitude control subsystem calculates the control quantity according to the obtained attitude angle to realize the motion control of the quadrotor drone.

[0107] Using the interval sampling method, the UAV must reach the desired position within the interval time. To this end, a terminal sliding mode controller is designed based on the following assumptions:

[0108] Assumption 1: The quadrotor drone is a rigid body with a center of mass located at the geometric center and a symmetrical structure;

[0109] Assumption 2: The flight attitude of the quadrotor drone has no effect on the drag and gravity acting on the drone.

[0110] Assumption 3: The geometric center of the quadrotor drone is the origin of the body coordinate system;

[0111] Assumption 4: The geometric center of the quadrotor drone is the origin of the body coordinate system.

[0112] First, based on assumptions 1-4, the quadrotor UAV dynamics model is obtained as shown in formula (11):

[0113]

[0114] In formula (9), is the acceleration of the drone in the x-axis, y-axis and z-axis directions; is the angular acceleration of the roll angle, pitch angle and yaw angle; u i is the control input, k i is the air resistance coefficient in each direction; d i is interference and satisfies |d i |<D, D is a positive real number; m is the mass of the drone; [I x I y I z ] is the moment of inertia of the UAV in three directions; g is the acceleration due to gravity;

[0115] Secondly, the design is as shown in the attached Figure 2 The quadrotor UAV control system shown in the figure is composed of inner and outer loop control subsystems, with the position subsystem as the outer loop and the attitude subsystem as the inner loop; the position command (x d ,y d , z d ) and yaw angle command φ d After the position command (x d ,y d , z d ), calculate the desired pitch angle θ by solving the attitude angle d , roll angle ψ d and control input u1; the inner loop attitude control subsystem receives the yaw angle command φ d and the indirect desired attitude angle θ d , ψd , the control inputs u2, u3 and u4 are calculated to achieve motion control of the quadrotor drone;

[0116] Next, based on the above-mentioned quadrotor drone control system, the control law is designed. The specific steps include:

[0117] Assume the following virtual control quantity:

[0118]

[0119] Among them, u1 is the control input, φ is the yaw angle command, and ψ is the roll angle;

[0120] Step 2: Based on formula (10), transform formula (9) and finally obtain the dynamic model of the quadrotor UAV control system formula (11):

[0121]

[0122] Among them, x, t, z, ψ, θ, φ are control variables, x, t and z are associated with the control input u1, u 1x 、 u 1y 、u 1z is the virtual control quantity;

[0123] For the first equation in equation (11), the sliding mode function of the first control variable is designed as:

[0124]

[0125] Where α1, β1>0, a1, b1 are positive odd numbers and a1<b1, x e is the control error of x, x d is the expected value of x.

[0126] Taking the first-order derivative of formula (12), we can get:

[0127]

[0128] According to formula (13), the control law of the first control variable is designed as follows:

[0129]

[0130] in, μ1, γ1>0, σ1, ξ1 are positive odd numbers, and σ1<ξ1. The sliding mode functions of other control variables are designed as follows:

[0131]

[0132] Design the control laws for other controlled variables as follows:

[0133]

[0134] Again, the virtual attitude angle θ d The solution:

[0135] Since the quadrotor drone is underactuated, it is not possible to control all six degrees of freedom. Therefore, the reasonable strategy is to control [xyz] and φ while ensuring the stability of θ and ψ. Due to the high coupling characteristics, the expected values of θ and ψ can be solved;

[0136] From formula (10), we can get:

[0137]

[0138] because The above formula can be expressed as:

[0139]

[0140] because

[0141]

[0142] Then formula (18) can be expressed as:

[0143]

[0144] Formula (20) can be expressed separately as:

[0145]

[0146]

[0147] From formula (21), we can get:

[0148]

[0149] If the value on the left side of equation (22) exceeds [-1 1], it will cause θ d It is impossible to find a solution, so let:

[0150]

[0151] When χ≥1, take sinθ d =1, that is When χ≤-1, take sinθ d =-1, that is When -1<χ<1, θ d =arcsinχ;

[0152] Finally, in Equation (16), a finite-time convergent third-order differentiator is used to calculate the target attitude angle θ generated by the outer loop.d and ψ d Finding the first and second order derivatives, the expression of the third order differentiator is:

[0153]

[0154] Where: v(t) is the input signal; x1 is the input signal tracking value; x2 is the signal first-order derivative estimate; x3 is the signal second-order derivative estimate; where ε = 0.04, and the initial values of the differentiator are x1(0) = 0, x2(0) = 0, and x3(0) = 0.

[0155] Next, this solution is explained in combination with actual application scenarios:

[0156] For example, in a 40m×40m plane area, 50 unmanned vehicles are distributed as coverage target points. The unmanned vehicles will adjust their distribution during movement. A k-means-based multi-UAV to multi-UAV tracking and coverage control method is used to drive the UAVs to achieve tracking coverage of the unmanned vehicles. The UAV coverage radius r = 2.5m.

[0157] The specific steps include:

[0158] Step 1: Assume that the initial position P0 of k UAVs is the origin (0, 0). Set the maximum allowable error δ to 0.05m;

[0159] Step 2: Sample the position of the unmanned vehicle for the τth (τ=1,2,3,...) times, with the sampling interval set to 0.01 seconds, and obtain Q τ ={q1, q2, ..., q 50}(q i is the position of the i-th unmanned vehicle, and there are 50 unmanned vehicles in the coverage control system);

[0160] Step 3: Based on the k-means algorithm, according to Q τ Cluster multiple unmanned vehicles and obtain cluster centers. The specific operation process of the algorithm is as follows:

[0161] Step 3.1: Randomly select k vehicles from the 50 autonomous vehicles and use their positions as the first cluster center. ( is the initial position of the jth cluster center);

[0162] Step 3.2: Based on the formula Calculate the distance from each unmanned vehicle to the k cluster centers.

[0163] Step 3.3: Assign all unmanned vehicles to the cluster represented by the nearest cluster center according to their distance. According to the formula Calculate the second cluster centroid.

[0164] Step 3.4: Determine C λ+1 =C λ Is it true? If true, output C λ+1 , the algorithm stops; if not, return to step 3.2;

[0165] Step 4: As shown in the formula As shown, the cluster center position is set to the τth desired position of the UAV.

[0166] Step 5: According to the formula Calculate the coverage area of each drone when it reaches the desired location.

[0167] Step 6: Get the total coverage of all drones for the τth time as follows: shown.

[0168] Step 7: According to the formula Calculate the distance from each unmanned vehicle to the desired position of the drone

[0169] Step 8: Compare with the UAV coverage radius r to determine Is it true? If it is true, it means that there are unmanned vehicles not covered, and it is necessary to add drones. k=k+1, return to step 2; If it is not true, proceed to the next step;

[0170] Step 9: Judgement Is it true? If it is true, it means that there is repeated coverage, and the redundant drone needs to be withdrawn. k = k-1, set the expected position of the redundant drone as the origin; if it is not true, proceed to the next step;

[0171] Step 10: Use the designed controller to drive the drone to the desired position until the drone position meets

[0172] Step 11: Return to step 1 until the tracking coverage task is completed.

[0173] In specific application scenarios, the number and position change information of drones can be illustrated with reference to Figures 3-5. Figures 3A-3D The four figures are schematic diagrams of the tracking coverage effect when the number of drones remains unchanged, among which: Figure 3A This is a schematic diagram of the three-dimensional effect. Figure 3B It is a two-dimensional effect diagram. Figure 3C is the change curve of x, y, and z coordinate values, Figure 3D To track the coverage change curve; Figure 4 shows Figures 4A-4D The four figures are schematic diagrams of the tracking coverage effect when additional drones are deployed, among which: Figure 4AThe incomplete coverage result occurred at 4.5s in the experiment. Figure 4B The incomplete coverage result occurred during the 10s experiment. Figure 4C This is the coverage result after deploying two more drones. Figure 4D To track the coverage change curve; Figure 5 shows Figures 5A-5D The four figures are schematic diagrams of the tracking coverage effect when withdrawing the drone, among which: Figure 5A This is the coverage effect during the 12s experiment. Figure 5B To test the coverage effect of withdrawing a drone at 13.5s, Figure 5C To test the coverage effect of withdrawing the second drone at 14.5s, Figure 5D To track the coverage curve, black dots represent unmanned vehicles, five-pointed stars represent drones, and circles represent drone coverage.

[0174] like Figures 3A-3D As shown in the accompanying figure, the solid line represents the actual position, and the dashed line represents the calculated position. Regarding the tracking coverage effect when the number of drones remains constant and the distribution range of the unmanned vehicles remains constant during the operation of the drones, tracking is effective. As can be seen from the figure, 100% coverage is achieved in about 2 seconds. Initially, the drones and unmanned vehicles do not start from the same location, so the distribution range of the unmanned vehicles must be calculated to determine the drone's position, resulting in a time interval.

[0175] Figure 4 shows Figures 4A-4C In the four figures, when the distribution of unmanned vehicles is Figure 4A The aggregated distribution in the equation slowly changes to a dispersed distribution. Figure 4A Only one drone is needed to cover all drones. Figure 4B Two drones are needed to cover the unmanned vehicle. Figure 4C A second drone was added for tracking coverage. Figure 4D In the process, each time a drone is added, it takes time to drive the drone, and the coverage rate can reach 100% after a short time. Figure 5 shows Figures 5A-5C In the figure, the number of drones is reduced when the number of unmanned vehicles changes from dispersed to concentrated. For example, due to terrain changes, passing through narrow areas, and task distribution, the number of drones can be reduced by concentrating the unmanned vehicles. Figure 5D It takes about 2 seconds to reach 100% full coverage.

[0176] This solution has the following beneficial effects:

[0177] 1) The present invention applies the k-means algorithm to coverage control to cluster multiple unmanned vehicles and obtain the cluster center as the desired location of the UAV, thereby improving the efficiency of problem solving;

[0178] 2) To avoid the huge amount of calculation and information, the present invention adopts an interval sampling method to obtain the position information of the unmanned vehicle and proposes a terminal sliding mode controller, which can drive the UAV to the desired position within a limited time;

[0179] 3) The present invention also proposes an evaluation mechanism to determine whether there is incomplete coverage or repeated coverage, and then decide whether to add or withdraw drones, so as to achieve tracking coverage of multiple drones on multiple unmanned vehicles, and realize that the number of drones can be increased or decreased during the coverage process, thus laying the foundation for drones to perform tasks such as communication relay and command platform.

[0180] Based on the same idea, the present invention also provides a multi-UAV to multi-UAV dynamic tracking control device. Figure 6 The following is a schematic diagram of the structure of a multi-UAV to multi-UAV dynamic tracking control device. Figure 6 As shown, the device may include:

[0181] The unmanned vehicle location information set determination module 610 is used to obtain location information of multiple unmanned vehicles, determine the distribution range of all unmanned vehicles, and obtain a location information set of multiple unmanned vehicles; multiple unmanned vehicles are in a dynamic operation process;

[0182] The UAV initial position acquisition module 620 is used to obtain the initial positions of multiple UAVs;

[0183] The UAV expected position determination module 630 is configured to determine the expected positions of the multiple UAVs based on the position information set using a k-means algorithm with the initial position as the origin;

[0184] The UAV tracking coverage determination module 640 is used to determine the tracking coverage of multiple UAVs;

[0185] a UAV target quantity determination module 650 for determining the target quantity of UAVs required to track all unmanned vehicles based on the tracking coverage and the desired location;

[0186] The drone driving module 660 is used to drive a target number of drones to the desired location to complete the tracking and coverage task of multiple unmanned vehicles.

[0187] based on Figure 6 The device may further include some specific implementation units:

[0188] Optionally, the drone desired position determination module 630 may specifically include:

[0189] A cluster center calculation unit is used to cluster the position information of multiple unmanned vehicles in the position information set using the initial position as the origin and obtain cluster centers by using a k-means algorithm;

[0190] The expected position calculation unit is used to determine the positions of the cluster centers as the expected positions of the multiple UAVs.

[0191] Optionally, the location information set contains location information corresponding to n unmanned vehicles, where n is a positive integer greater than 1; the cluster center calculation unit can be specifically used to:

[0192] Randomly select k unmanned vehicles from n unmanned vehicles and use the location information of k unmanned vehicles as the first cluster center;

[0193] Calculate the distance between each unmanned vehicle and the k first cluster centers;

[0194] All unmanned vehicles are assigned to the cluster represented by the nearest cluster center according to the size of the distance value, and the second cluster center corresponding to the corresponding cluster is calculated;

[0195] If the second cluster center is consistent with the first cluster center, the second cluster center is determined as the cluster center corresponding to the position information set.

[0196] Optionally, the drone tracking coverage range determination module 640 may specifically include:

[0197] The coverage calculation unit is used to calculate the coverage of each drone when it reaches the desired location, and finally obtain the total coverage of all drones;

[0198] The coverage range calculation unit may specifically execute:

[0199] Using the formula:

[0200]

[0201] Calculate the coverage area of each drone when it reaches the desired location;

[0202] in, is the coverage of the Jth UAV at the τth time, R is the coordinate of any point, is the expected position of the Jth UAV at the τth time, and r is the coverage radius of each UAV;

[0203] Based on the stated coverage, the formula is:

[0204]

[0205] The total coverage of all drones is calculated for the τth time, where k is the number of drones in the area to be covered.

[0206] Optionally, the UAV target quantity determination module 650 may specifically include:

[0207] The first judgment unit is configured to judge whether all unmanned vehicles are within the coverage of the current drone based on the total coverage of all drones and the distribution range of all drones; if not, the distribution position of unmanned vehicles outside the coverage of the current drone is determined;

[0208] The drone dispatching unit is used to determine the number of drones that need to be dispatched and the corresponding target locations based on the distribution locations of unmanned vehicles outside the current drone coverage area.

[0209] Optionally, the UAV target quantity determination module 650 may further include:

[0210] The second judgment module is used to judge whether there are redundant drones if all unmanned vehicles are within the coverage area of the current drone; the redundant drones refer to drones that have no unmanned vehicles within the coverage area;

[0211] The drone withdrawal module is used to withdraw redundant drones if there are any.

[0212] Optionally, the drone driving module 660 can be used to:

[0213] Constructing a quadrotor drone control system; the quadrotor drone control system consists of a position control subsystem as an outer loop and an attitude control subsystem as an inner loop;

[0214] The quad-rotor UAV control system is based on sliding mode control to achieve motion control of the UAV, drive the target number of UAVs to the desired position, and complete the tracking coverage task of multiple unmanned vehicles.

[0215] Based on the same idea, the embodiments of this specification also provide a multi-UAV to multi-UAV dynamic tracking control device. Figure 7 This is a schematic diagram of the structure of the multi-UAV to multi-UAV dynamic tracking control device provided by the present invention. It may include:

[0216] Communication unit / communication interface, used to obtain the location information of multiple unmanned vehicles, determine the distribution range of all unmanned vehicles, and obtain the location information set of multiple unmanned vehicles; multiple unmanned vehicles are in a dynamic operation process;

[0217] Get the initial positions of multiple drones;

[0218] a processing unit / processor configured to determine the desired positions of the plurality of drones based on the position information set using a k-means algorithm with the initial position as an origin;

[0219] Determine tracking coverage for multiple drones;

[0220] Determining a target number of drones required to track all unmanned vehicles based on the tracking coverage and the desired location;

[0221] Drive the target number of drones to the desired location to complete the tracking coverage task of multiple unmanned vehicles.

[0222] like Figure 7 As shown, the terminal device may further include a communication line. The communication line may include a path for transmitting information between the components.

[0223] Optional, such as Figure 7 As shown, the terminal device may further include a memory. The memory is used to store computer-executable instructions for executing the solution of the present invention, and the execution is controlled by the processor. The processor is used to execute the computer-executable instructions stored in the memory, thereby implementing the method provided by the embodiment of the present invention.

[0224] like Figure 7 As shown, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via a communication line. The memory may also be integrated with the processor.

[0225] Optionally, the computer-executable instructions in the embodiment of the present invention may also be referred to as application program codes, which is not specifically limited in the embodiment of the present invention.

[0226] In a specific implementation, as an embodiment, Figure 7 As shown, the processor may include one or more CPUs, such as Figure 7 CPU0 and CPU1 in.

[0227] In a specific implementation, as an embodiment, Figure 7 As shown, the terminal device may include multiple processors, such as Figure 7 Each of these processors can be a single-core processor or a multi-core processor.

[0228] Based on the same idea, the embodiments of this specification also provide a computer storage medium corresponding to the above embodiments. The computer storage medium stores instructions. When the instructions are executed, the workpiece circular runout detection method in the above embodiments is implemented.

[0229] The above mainly introduces the solution provided by the embodiment of the present invention from the perspective of the interaction between the various modules. It can be understood that, in order to realize the above functions, each module includes a hardware structure and / or software unit corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0230] The embodiments of the present invention can be divided into functional modules according to the above-described method examples. For example, each functional module can be divided according to its function, or two or more functions can be integrated into a single processing module. The integrated modules can be implemented in the form of hardware or software functional modules. It should be noted that the module division in the embodiments of the present invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used.

[0231] While the present invention has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations thereof may be made without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention as defined by the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents thereof within the scope of the invention. It is apparent that various modifications and variations of the present invention may be made by those skilled in the art without departing from the spirit and scope of the invention. Thus, the present invention is intended to encompass such modifications and variations as fall within the scope of the claims and their equivalents.

Claims

1. A multi-UAV to multi-UAV dynamic tracking control method, characterized in that: include: Obtaining location information of multiple unmanned vehicles, determining the distribution range of all unmanned vehicles, and obtaining a location information set of multiple unmanned vehicles; the multiple unmanned vehicles are in a dynamic operation process; the location information set contains location information corresponding to n unmanned vehicles, where n is a positive integer greater than 1; Get the initial positions of multiple drones; Taking the initial position as the origin, using a k-means algorithm based on the position information set to determine the desired positions of the multiple drones; Determine tracking coverage for multiple drones; Determining a target number of drones required to track all unmanned vehicles based on the tracking coverage and the desired location; Drive the target number of drones to the desired location to complete the tracking and coverage task of multiple unmanned vehicles; The method of determining the desired positions of the plurality of drones using the k-means algorithm based on the position information set with the initial position as the origin includes: randomly selecting k drones from n drones and using the position information of the k drones as the first cluster center; Calculate the distance between each unmanned vehicle and the k first cluster centers; All unmanned vehicles are assigned to the cluster represented by the nearest cluster center according to the size of the distance value, and the second cluster center corresponding to the corresponding cluster is calculated; If the second cluster center is consistent with the first cluster center, determining the second cluster center as the cluster center corresponding to the position information set; The positions of the cluster centers are determined as the desired positions of the multiple UAVs.

2. The method according to claim 1, characterized in that Determine tracking coverage for multiple drones, including: Calculate the coverage of each drone when it reaches the desired location, and finally obtain the total coverage of all drones; Calculate the coverage of each drone when it reaches the desired location, and finally obtain the total coverage of all drones, including: Using the formula: Calculate the coverage area of each drone when it reaches the desired location; in, is the coverage of the jth UAV at the τth time, R is the coordinate of any point, is the expected position of the jth UAV at the τth time, and r is the coverage radius of each UAV; Based on the stated coverage, the formula is: The total coverage of all drones is calculated for the τth time, where k is the number of drones in the area to be covered.

3. The method according to claim 1, characterized in that Based on the tracking coverage and the desired location, determining the target number of drones required to track all unmanned vehicles includes: Based on the total coverage of all drones and the distribution range of all drones, determine whether all unmanned vehicles are within the coverage of the current drone; If all unmanned vehicles are not within the coverage of the current drone, the distribution locations of unmanned vehicles outside the coverage of the current drone are determined; Based on the distribution of unmanned vehicles outside the current drone coverage area, the number of drones that need to be dispatched and the corresponding target locations are determined.

4. The method according to claim 3, characterized in that Based on the total coverage of all drones and the distribution range of all drones, after determining whether all unmanned vehicles are within the coverage of the current drone, it also includes: If all unmanned vehicles are within the coverage of the current drone, it is determined whether there are redundant drones; the redundant drones refer to drones that have no unmanned vehicles within their coverage. If there are redundant drones, the redundant drones will be withdrawn.

5. The method according to claim 1, wherein Drive the target number of drones to the desired location and complete the tracking and coverage task of multiple unmanned vehicles, specifically including: Constructing a quadrotor drone control system; the quadrotor drone control system consists of a position control subsystem as an outer loop and an attitude control subsystem as an inner loop; The quad-rotor UAV control system is based on sliding mode control to achieve motion control of the UAV, drive the target number of UAVs to the desired position, and complete the tracking coverage task of multiple unmanned vehicles.

6. A multi-UAV to multi-UAV dynamic tracking control device, characterized in that: The device includes: An unmanned vehicle location information set determination module is used to obtain location information of multiple unmanned vehicles, determine the distribution range of all unmanned vehicles, and obtain a location information set of multiple unmanned vehicles; multiple unmanned vehicles are in a dynamic operation process; the location information set contains location information corresponding to n unmanned vehicles, where n is a positive integer greater than 1; UAV initial position acquisition module, used to obtain the initial positions of multiple UAVs; A UAV expected position determination module is used to determine the expected positions of multiple UAVs based on the position information set using a k-means algorithm with the initial position as the origin; The drone expected position determination module includes: A cluster center calculation unit is used to randomly select k unmanned vehicles from n unmanned vehicles and use the location information of the k unmanned vehicles as the first cluster center; Calculate the distance between each unmanned vehicle and the k first cluster centers; All unmanned vehicles are assigned to the cluster represented by the nearest cluster center according to the size of the distance value, and the second cluster center corresponding to the corresponding cluster is calculated; If the second cluster center is consistent with the first cluster center, determining the second cluster center as the cluster center corresponding to the position information set; an expected position calculation unit, configured to determine the positions of the cluster centers as expected positions of the plurality of UAVs; A UAV tracking coverage determination module is used to determine the tracking coverage of multiple UAVs; A UAV target quantity determination module is used to determine the target quantity of UAVs required to track all unmanned vehicles based on the tracking coverage and the expected position; The UAV driving module is used to drive the target number of UAVs to the desired location to complete the tracking and coverage task of multiple unmanned vehicles.

7. Multi-UAV to multi-UAV dynamic tracking control equipment, characterized in that the equipment include: A communication unit / communication interface is used to obtain the location information of multiple unmanned vehicles, determine the distribution range of all unmanned vehicles, and obtain a location information set of multiple unmanned vehicles; multiple unmanned vehicles are in a dynamic operation process; the location information set contains location information corresponding to n unmanned vehicles, where n is a positive integer greater than 1; Get the initial positions of multiple drones; a processing unit / processor configured to determine the desired positions of the plurality of drones based on the position information set using a k-means algorithm with the initial position as an origin; Determine tracking coverage for multiple drones; Determining a target number of drones required to track all unmanned vehicles based on the tracking coverage and the desired location; Drive the target number of drones to the desired location to complete the tracking and coverage task of multiple unmanned vehicles; The method of determining the desired positions of the plurality of drones using the k-means algorithm based on the position information set with the initial position as the origin includes: randomly selecting k drones from n drones and using the position information of the k drones as the first cluster center; Calculate the distance between each unmanned vehicle and the k first cluster centers; All unmanned vehicles are assigned to the cluster represented by the nearest cluster center according to the size of the distance value, and the second cluster center corresponding to the corresponding cluster is calculated; If the second cluster center is consistent with the first cluster center, determining the second cluster center as the cluster center corresponding to the position information set; The positions of the cluster centers are determined as the desired positions of the multiple UAVs.

8. A computer storage medium, characterized in that The computer storage medium stores instructions, and when the instructions are executed, the dynamic tracking control method of multiple unmanned aerial vehicles to multiple unmanned vehicles according to any one of claims 1 to 5 is implemented.

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