Automatic control method and system for cooperative motion of unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle cooperative motion, and provides an automatic control method and system for unmanned aerial vehicle cooperative motion, and the method comprises the steps: obtaining a flight road section, a passing unmanned aerial vehicle of a position node and a road section predicted speed according to the flight data and task data of the unmanned aerial vehicle cooperative motion, and calculating the speed gain intensity; acquiring a collision risk rate according to the passing unmanned aerial vehicle of the position node, the length of the flight road section and the predicted speed of the road section; according to the speed gain intensity and the collision risk rate, heuristic factors of the unmanned aerial vehicles on the flight road section are obtained, the optimal path from each unmanned aerial vehicle to the to-be-executed task place is obtained through an ant colony algorithm, and automatic control over cooperative motion between the unmanned aerial vehicles is completed. The invention aims to solve the problem that the existing ant colony algorithm performs path planning on the unmanned aerial vehicles in different states by using the same parameter, resulting in collision during cooperative motion of the unmanned aerial vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of cooperative motion of unmanned aerial vehicles, and in particular to an automatic control method and system for cooperative motion of unmanned aerial vehicles. Background Art

[0002] UAV cooperative motion refers to the flight behavior of multiple UAVs to achieve a common goal through coordination and cooperation. Sudden failures of UAVs and the priority logic relationship between UAV tasks will affect how UAVs allocate tasks and execute tasks. Therefore, how to scientifically and reasonably dispatch and control the motion path of each UAV is the key to achieving efficient UAV collaboration.

[0003] As a commonly used path planning optimization algorithm, the ant colony algorithm has good global search capabilities and can make selections based on the guidance of pheromone concentration, thereby realizing the search for the global optimal solution. However, the algorithm performance and search efficiency of the ant colony algorithm depend on the setting of parameters in the algorithm. When drones in different states use the same parameters for path planning during cooperative motion, it is impossible to find the optimal path for each drone, which easily causes path intersections and collisions, thereby affecting the efficiency of cooperative motion of drones. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an automatic control method and system for cooperative motion of unmanned aerial vehicles. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides an automated control method for cooperative motion of unmanned aerial vehicles, the method comprising the following steps:

[0006] Obtain flight data and mission data of UAV cooperative motion;

[0007] According to the mission data of the cooperative motion of the UAVs, the flight section and the motion direction of the UAVs on the flight section are obtained; according to the flight data of the cooperative motion of the UAVs and the motion direction of the UAVs on the flight section, the length of the flight section and the estimated speed of the UAVs on the flight section are obtained;

[0008] According to the mission data of the UAV cooperative motion, the passing UAVs of the location node are obtained; according to the length of the flight section and the estimated speed of the UAV on the flight section, the estimated passage time of the UAV on the flight section is calculated; according to the passing UAVs of the location node and the estimated passage time of the UAV on the flight section, the collision risk rate of the UAV at the location node is obtained;

[0009] According to the estimated speed of the UAV on the flight section, the speed gain intensity of the UAV on the flight section is obtained;

[0010] According to the speed gain intensity of the UAV on the flight section and the collision risk rate of the UAV at the position node, the inspiration factor of the UAV on the flight section is obtained;

[0011] According to the inspiration factors of the UAVs on all flight sections, the ant colony algorithm is used to obtain the optimal path for each UAV to the mission site to be performed, and the automated control of the collaborative movement between UAVs is completed.

[0012] Preferably, the step of obtaining the flight segment and the movement direction of the UAV on the flight segment according to the mission data of the cooperative movement of the UAV includes:

[0013] The connection between the position node and the next adjacent position node is recorded as a flight segment;

[0014] The position node with the smallest number on the flight segment is recorded as the starting position node of the flight segment;

[0015] The position node with the largest number on the flight segment is recorded as the terminal position node of the flight segment;

[0016] When the horizontal coordinates of the starting position node and the ending position node of the flight segment are equal, half of pi is recorded as the third angle, and the radian value of the third angle is recorded as the movement direction of the UAV on the flight segment;

[0017] When the horizontal coordinates of the starting position node and the ending position node of the flight section are not equal, the difference between the vertical coordinates of the starting position node and the ending position node of the flight section is recorded as the first difference, the difference between the horizontal coordinates of the starting position node and the ending position node of the flight section is recorded as the second difference, the ratio of the first difference to the second difference is recorded as the first ratio, the arc tangent value of the first ratio is recorded as the first angle, and the radian value of the first angle is recorded as the movement direction of the UAV on the flight section.

[0018] Preferably, the step of obtaining the length of the flight section and the estimated speed of the drone on the flight section based on the flight data of the cooperative motion of the drone and the motion direction of the drone on the flight section includes:

[0019] The Euclidean distance between the starting position node and the ending position node of the flight segment is recorded as the length of the flight segment;

[0020] The angle between the movement direction of the UAV on the flight path and the wind direction in the mission area is recorded as the second angle;

[0021] The cosine of the second angle is recorded as the first cosine;

[0022] The product of the first cosine and the wind speed in the mission area is recorded as the first speed;

[0023] The sum of the first speed and the average speed of the UAV in a windless environment is recorded as the estimated speed of the UAV on the flight section.

[0024] Preferably, the step of obtaining the passing drones of the location node according to the mission data of the drone cooperative motion includes:

[0025] According to the coordinates of the starting position of the UAV, the coordinates of the mission location to be performed, and the coordinates of all location nodes, a greedy algorithm is used to obtain the shortest path from each UAV to the mission location to be performed;

[0026] The drones whose shortest paths pass through the location node are recorded as the path drones of the location node.

[0027] Preferably, the step of calculating the estimated travel time of the drone on the flight segment according to the length of the flight segment and the estimated speed of the drone on the flight segment includes:

[0028] The ratio of the length of the flight segment to the estimated speed of the UAV on the flight segment is recorded as the estimated travel time of the UAV on the flight segment.

[0029] Preferably, the specific calculation method for obtaining the collision risk rate of the drone at the location node based on the passing drones of the location node and the estimated travel time of the drone on the flight section is:

[0030]

[0031] Among them, C(W i , D Z ) is the i-th drone W i At location node D Z The collision risk rate, n Z is the location node D Z The number of passing drones, R Z，a is the location node D Z The ath UAV passes through the node D Z The total number of nodes on the shortest path, t(W Z，a , P r-1，r ) is the location node D Z The ath passing drone W Z，a In flight segment P r-1，r Estimated travel time for the road section above, Q Z，i is the i-th drone W i To location node D Z The total number of nodes on the shortest path, t(W i , P l-1，l ) is the i-th drone W iIn flight segment P l-1，l Estimated travel time for the above road sections.

[0032] Preferably, obtaining the speed gain intensity of the UAV on the flight section according to the estimated speed of the UAV on the flight section includes:

[0033] A triangulation algorithm is used for all position nodes to obtain the triangular mesh of the mission area;

[0034] The position nodes directly connected to the position node in the triangular mesh of the mission area are recorded as adjacent position nodes;

[0035] The maximum value of the estimated speed of the UAV from the location node to all adjacent location nodes is recorded as the first maximum speed;

[0036] The ratio of the predicted speed of the UAV on the flight section to the first maximum speed is recorded as the speed gain intensity of the UAV on the flight section.

[0037] Preferably, the step of obtaining the heuristic factor of the UAV on the flight section according to the speed gain intensity of the UAV on the flight section and the collision risk rate of the UAV at the position node includes:

[0038] The ratio of the weight of the preset collision risk rate to the collision risk rate of the UAV at the location node is recorded as the collision pheromone of the UAV on the flight section;

[0039] The ratio of the weight of the preset speed gain intensity to the speed gain intensity of the UAV on the flight section is recorded as the speed gain pheromone of the UAV on the flight section;

[0040] The sum of the collision pheromone and the speed gain pheromone of the UAV on the flight section is recorded as the pheromone dependence of the UAV on the flight section;

[0041] The inversely proportional normalized value of the UAV's pheromone dependence on the flight section is recorded as the UAV's inspiration factor on the flight section.

[0042] Preferably, the method of using an ant colony algorithm to obtain the optimal path for each drone to the task location to be performed based on the heuristic factors of the drones on all flight sections, and completing the automated control of the cooperative movement between the drones, includes:

[0043] According to the preset values ​​of the initial pheromone concentration, pheromone volatility coefficient and ant quantity of the drone on all flight sections, the heuristic factors of the drone on all flight sections are substituted into the ant colony algorithm for modeling to obtain the optimal path for each drone to reach the mission location to be performed;

[0044] The optimal path for each drone to reach the mission location is sent to the command center, and the command center issues instructions to each drone, and the drone moves according to the instructions issued by the command center.

[0045] In a second aspect, an embodiment of the present invention further provides an automated control system for cooperative motion of unmanned aerial vehicles, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0046] The present invention has at least the following beneficial effects: based on the mission data and flight data of the cooperative motion of unmanned aerial vehicles, the passing unmanned aerial vehicles of the flight segment and the position node, as well as the motion direction and the estimated speed of the unmanned aerial vehicle on the flight segment are obtained, and then the speed gain intensity of the unmanned aerial vehicle on the flight segment is calculated, and the accuracy of calculating the speed gain intensity on the flight segment is improved by comprehensively considering the motion attributes of the unmanned aerial vehicle itself and the characteristics of the mission area; the estimated passage time of the unmanned aerial vehicle on the flight segment is calculated according to the passing unmanned aerial vehicles of the position node, the length of the flight segment and the estimated speed of the unmanned aerial vehicle on the flight segment, and then the collision risk rate of the unmanned aerial vehicle at the position node is obtained, and the collision risk rate of the unmanned aerial vehicle at the position node is obtained by estimating the collision risk rate of all the unmanned aerial vehicles passing the position node. The time for the UAV at the location node to arrive at the location node is improved, which improves the reliability of calculating the collision risk rate of the UAV at the location node; according to the speed gain intensity of the UAV on the flight section and the collision risk rate of the UAV at the location node, the heuristic factor of the UAV on the flight section is obtained, and the ant colony algorithm is used to obtain the optimal path for each UAV to the mission to be performed, and the automatic control of the cooperative movement between UAVs is completed. The heuristic information such as environment, energy consumption and collision probability is comprehensively considered, which improves the reliability of the ant colony algorithm to determine the local optimal solution, and solves the problem that the existing ant colony algorithm uses the same parameters for path planning for UAVs in different states, which leads to collisions during the cooperative movement of UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A flowchart of a method for automatically controlling cooperative motion of unmanned aerial vehicles provided by one embodiment of the present invention;

[0049] Figure 2 Schematic diagram of cooperative motion of drones. DETAILED DESCRIPTION

[0050] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the automatic control method and system for cooperative motion of unmanned aerial vehicles proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0051] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0052] The specific scheme of the automatic control method and system for cooperative motion of unmanned aerial vehicles provided by the present invention is described in detail below with reference to the accompanying drawings.

[0053] See also Figure 1 , which shows a flowchart of a method for automatically controlling cooperative motion of unmanned aerial vehicles provided by an embodiment of the present invention, the method comprising the following steps:

[0054] Step S001, obtaining flight data and mission data of cooperative motion of UAVs.

[0055] The main purpose of the present invention is to analyze the different states of the UAVs and the characteristics of the mission to be performed, so as to realize the automatic control of the cooperative motion of the UAVs. Therefore, it is necessary to first obtain the flight data and mission data of the cooperative motion of the UAVs. The schematic diagram of the cooperative motion of the UAVs is shown in FIG. Figure 2 As shown in the figure, the flight data of the UAV cooperative motion includes the wind direction and wind speed in the mission area and the average speed of the UAV in a windless environment. The mission data of the UAV cooperative motion includes the coordinates of the starting position of the UAV, the coordinates of the mission site to be performed, and the coordinates of all position nodes. The number of UAVs is N, and the i-th UAV W i The coordinates of the starting position are W(x i ,y i ), the i-th drone W i The average speed in a windless environment is v i , the jth task to be executed is Q j The coordinates of Q(x j ,y j ), the wind direction in the mission area is θ, and the wind speed is v θ ,According to the coordinates of the drones, the drones are numbered in order from top to bottom and from left to right.

[0056] At this point, the flight data and mission data of the UAV cooperative movement have been obtained.

[0057] Step S002: Obtain the flight segment and the movement direction of the UAV on the flight segment based on the mission data of the UAV cooperative movement; obtain the length of the flight segment and the estimated speed of the UAV on the flight segment based on the flight data of the UAV cooperative movement and the movement direction of the UAV on the flight segment.

[0058] It should be noted that wind direction and wind speed are important factors affecting the flight speed of drones. Based on the principles of aerodynamics, air resistance is proportional to the square of wind speed. When the wind direction is opposite to the direction of movement of the drone, the greater the wind resistance to the drone, the greater the fuel consumption. Therefore, the environment of the mission area where the drone is to perform is an important factor affecting the flight speed of the drone.

[0059] Specifically, the line between each position node and the next adjacent position node is recorded as a flight segment, the position node with the smallest number on the flight segment is recorded as the starting position node of the flight segment, the position node with the largest number on the flight segment is recorded as the ending position node of the flight segment, and the Euclidean distance between the starting position node and the ending position node of the flight segment is recorded as the length of the flight segment. According to the coordinates of the starting position node and the ending position node of the flight segment, the estimated speed of the drone on the flight segment is expressed as follows:

[0060] v(W i , P Z-1，Z )=v i +v θ *cos(α Z-1，Z -θ)

[0061]

[0062] Among them, v(W i , P Z-1，Z ) is the i-th drone W i In flight segment P Z-1，Z The expected speed of the road segment, v i is the i-th drone W i Average speed in windless environment, v θ is the wind speed in the mission area, cos() is the cosine function, θ is the wind direction in the mission area, α Z-1，Z is the flight segment P Z-1，Z The angle with the vertical direction represents the direction of movement of the drone, π is pi, arctan() is the inverse tangent function, and y PZ is the flight segment P Z-1，Z The vertical coordinate of the end position node, is the flight segment P Z-1，Z The ordinate of the starting position node, is the flight segment P Z-1，Z The horizontal coordinate of the end position node, is the flight segment P Z-1，Z The horizontal coordinate of the starting position node.

[0063] It should be further explained that, when the wind speed is constant, the greater the average speed of the UAV and the smaller the angle between the UAV's movement direction and the wind direction, the more the UAV is pushed by the wind, the greater the expected speed of the UAV on the road section, and the less extra fuel consumption of the UAV on this road section; when the average speed of the UAV is smaller and the angle between the UAV's movement direction and the wind direction is larger, the more the UAV is hindered by the wind, the smaller the expected speed of the UAV on the road section, and the greater the extra fuel consumption of the UAV on this road section.

[0064] At this point, the length of the flight segment and the estimated speed of the drone on the flight segment are obtained.

[0065] Step S003, based on the mission data of the cooperative motion of UAVs, obtain the passing UAVs of the location node, calculate the estimated passage time of the UAV on the flight section according to the length of the flight section and the estimated speed of the UAV on the flight section, and obtain the collision risk rate of the UAV at the location node according to the passing UAVs of the location node and the estimated passage time of the UAV on the flight section.

[0066] It should be noted that when UAVs are moving in collaboration, if multiple UAVs fly to the same location node at the same time, the probability of collision will be greatly increased. Therefore, the path planning of all UAVs needs to be considered comprehensively to determine the optimal movement path for each UAV.

[0067] Specifically, according to the coordinates of the starting position of the drone, the coordinates of the task site to be performed, and the coordinates of all position nodes, a greedy algorithm is used to obtain the shortest path from each drone to the task site to be performed, and the drone whose shortest path passes through the position node is recorded as the path drone of the position node. It is a known technology to use a greedy algorithm to obtain the shortest path from each drone to the task site to be performed, and the details are not repeated here. The ratio of the length of the flight section to the estimated speed of the drone on the flight section is recorded as the estimated travel time of the drone on the flight section.

[0068] Furthermore, according to the passing drones of the location node and the estimated travel time of the drone on the flight section, the collision risk rate of the drone at the location node is expressed as follows:

[0069]

[0070] Among them, C(W i , D Z) is the i-th drone W i At location node D Z The collision risk rate, n Z is the location node D Z The number of passing drones, R Z,a is the location node D Z The ath UAV passes through the node D Z The total number of nodes on the shortest path, t(W Z,a , P r-1，r ) is the location node D Z The ath passing drone W Z,a In flight segment P r-1，r Estimated travel time for the road section above, Q Z,i is the i-th drone W i To location node D Z The total number of nodes on the shortest path, t(W i , P l-1，l ) is the i-th drone W i In flight segment P l-1，l Estimated travel time for the above road sections.

[0071] It should be further explained that when the position node D Z Each drone passing through to the location node D Z The time required is similar to that of drone W i To location node D Z The smaller the time difference is, the more likely they will arrive at node D at the same time. Z The more drones there are, the more drones W i At location node D Z The greater the collision risk rate, the more likely it is to collide with drones on other paths. The greater the collision risk rate, the more likely it is that the ants will collide with drones on other paths. Z-1 When choosing the next node, D is given Z Smaller probability weights.

[0072] At this point, the collision risk rate of the drone at the location node is obtained.

[0073] Step S004, obtaining the speed gain intensity of the UAV on the flight section according to the estimated speed of the UAV on the flight section.

[0074] It should be noted that due to air resistance, fuel consumption will increase. Z-1，Z When the fuel consumption on the flight path is lower than that on other flight paths, blindly changing the route may cause the fuel consumption of the drone to increase sharply. The greater the air resistance, the more work the drone does and the more fuel it consumes. Therefore, the present invention calculates the fuel consumption of the drone based on the position node D.Z-1 The estimated speed of the road segment to all adjacent location nodes obtains the degree of influence of air resistance on the speed of the drone.

[0075] Specifically, the Delaunay triangulation algorithm is used for all position nodes to obtain the triangular mesh of the mission area, where each vertex of a triangle is a position node, and all position nodes directly connected to the position node are recorded as the adjacent position nodes of the position node. Then the i-th UAV W i In flight segment P Z-1，Z The speed gain strength vup(W i , P Z-1，Z ) is calculated as follows:

[0076]

[0077] Among them, vup(W i , P Z-1，Z ) is the i-th drone W i In flight segment P Z-1，Z Upper velocity gain strength, v(W i , P Z-1，Z ) is the i-th drone W i In flight segment P Z-1，Z The expected speed of the road segment on B Z-1 is the location node D Z-1 The number of adjacent nodes, v(W i , P Z-1，b ) is the i-th drone W i From location node D Z-1 To location node D Z-1 The bth adjacent node D b The expected speed of the road section, b ranges from 1 to B Z-1 An integer between ; max{} is the maximum value function.

[0078] It should be noted that when the drone is in the flight section P Z-1，Z The estimated speed on the road segment relative to the drone from position node D Z-1 The larger the maximum value of the estimated speed of the route to all adjacent nodes, the faster the UAV is in the flight route P. Z-1，Z The greater the speed increase value, the greater the speed gain intensity, the smaller the air resistance, and the less fuel consumption of the drone.

[0079] At this point, the speed gain intensity of the drone on the flight section is obtained.

[0080] Step S005, obtaining a heuristic factor of the UAV on the flight section according to the speed gain strength of the UAV on the flight section and the collision risk rate of the UAV at the position node.

[0081] It should be noted that in the traditional ant colony algorithm, the heuristic factor determines the degree of dependence on the pheromone concentration in the process of selecting the location node. The larger the heuristic factor, the lower the degree of dependence on the pheromone concentration. At this time, the length of the path no longer occupies a major position in the selection of the location node. Comprehensive consideration of heuristic information such as the environment, energy consumption and collision probability improves the reliability of the local optimal solution, and a smaller heuristic factor can increase the degree of dependence on the pheromone concentration, so that the algorithm has a better global search capability. Therefore, the present invention obtains the pheromone dependence and heuristic factor of the drone from each location node to the next location node by setting different weights for the speed gain intensity and the collision risk probability.

[0082] Specifically, according to the speed gain intensity of the UAV on the flight section and the collision risk rate of the UAV at the position node, the heuristic factor of the UAV on the flight section is expressed as follows:

[0083]

[0084]

[0085] in, is the number of drones Wi on the flight path P Z-1，Z The inspiration factor on the exp() is an exponential function with a natural constant as the base, ρ(W i , P Z-1，Z ) is the i-th drone W i In flight segment P Z-1，Z The pheromone dependence on i , D Z ) is the i-th drone W i At location node D Z The collision risk rate, β1 is the weight of the collision risk rate, and the empirical value is 0.7, vup(W i , P Z-1，Z ) is the i-th drone W i In flight segment P Z-1，Z The speed gain strength on β2 is the weight of the speed gain strength, and its empirical value is 0.3.

[0086] It should be noted that when the drone is at position node D Z The greater the collision risk rate, the greater the Z-1，Z The smaller the speed gain intensity is, the closer the UAV is to position node D. Z The greater the probability of collision, the greater the resistance and fuel consumption, the greater the flight path P. Z-1，Z The smaller the pheromone dependence, the larger the inspiration factor, the more consideration is given to the inspiration information, and the less likely it is to choose the flight segment P.Z-1，Z .

[0087] At this point, the inspiration factors of the drone on the flight section are obtained.

[0088] Step S006, based on the heuristic factors of the drones on all flight sections, use the ant colony algorithm to obtain the optimal path for each drone to the mission location to be performed, and complete the automated control of the cooperative movement between the drones.

[0089] Firstly, the initial pheromone concentration of all UAVs on all flight sections is set to 20, the pheromone volatility coefficient is set to 0.5, and the number of ants is set to 20. Then, the heuristic factors of the UAVs on all flight sections are substituted into the ant colony algorithm for modeling, and the optimal path for each UAV to reach the mission site to be performed is obtained. The optimal path for each UAV to reach the mission site to be performed is sent to the command center, and the command center issues instructions to each UAV, and the UAV moves according to the instructions issued by the command center.

[0090] At this point, the automated control of the collaborative movement between drones is completed.

[0091] Based on the same inventive concept as the above method, an embodiment of the present invention also provides an automated control system for cooperative motion of unmanned aerial vehicles, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned automated control methods for cooperative motion of unmanned aerial vehicles are implemented.

[0092] In summary, the embodiments of the present invention solve the problem that the existing ant colony algorithm uses the same parameters to perform path planning for drones in different states, resulting in collisions when the drones move in a collaborative manner. The present invention analyzes the environmental conditions of the mission site to be performed, combines the wind direction, wind speed, the average speed of the drone and the direction of movement, obtains the flight section, the passing drones of the position node, and the movement direction and the expected speed of the drone on the flight section, and then calculates the speed gain intensity of the drone on the flight section; calculates the expected passage time of the drone on the flight section according to the passing drones of the position node, the length of the flight section and the expected speed of the drone on the flight section, and then obtains the collision risk rate of the drone at the position node; obtains the heuristic factor of the drone on the flight section according to the speed gain intensity of the drone on the flight section and the collision risk rate of the drone at the position node, uses the ant colony algorithm to obtain the optimal path for each drone to the mission site to be performed, and completes the automated control of the collaborative movement between drones.

[0093] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An automated control method for cooperative motion of unmanned aerial vehicles, characterized in that: The method comprises the following steps: Obtain flight data and mission data of UAV cooperative motion; According to the mission data of the cooperative motion of the UAVs, the flight section and the motion direction of the UAVs on the flight section are obtained; according to the flight data of the cooperative motion of the UAVs and the motion direction of the UAVs on the flight section, the length of the flight section and the estimated speed of the UAVs on the flight section are obtained; According to the mission data of the UAV cooperative motion, the passing UAVs of the location node are obtained; according to the length of the flight section and the estimated speed of the UAV on the flight section, the estimated passage time of the UAV on the flight section is calculated; according to the passing UAVs of the location node and the estimated passage time of the UAV on the flight section, the collision risk rate of the UAV at the location node is obtained; According to the estimated speed of the UAV on the flight section, the speed gain intensity of the UAV on the flight section is obtained; According to the speed gain intensity of the UAV on the flight section and the collision risk rate of the UAV at the position node, the inspiration factor of the UAV on the flight section is obtained; According to the inspiration factors of the UAVs on all flight sections, the ant colony algorithm is used to obtain the optimal path for each UAV to the mission site to be performed, and the automated control of the collaborative movement between UAVs is completed.

2. The automatic control method for cooperative motion of unmanned aerial vehicles according to claim 1, characterized in that: The step of obtaining the flight segment and the movement direction of the UAV on the flight segment according to the mission data of the UAV cooperative movement includes: The connection between the position node and the next adjacent position node is recorded as a flight segment; The position node with the smallest number on the flight segment is recorded as the starting position node of the flight segment; The position node with the largest number on the flight segment is recorded as the terminal position node of the flight segment; When the horizontal coordinates of the starting position node and the ending position node of the flight segment are equal, half of pi is recorded as the third angle, and the radian value of the third angle is recorded as the movement direction of the UAV on the flight segment; When the horizontal coordinates of the starting position node and the ending position node of the flight section are not equal, the difference between the vertical coordinates of the starting position node and the ending position node of the flight section is recorded as the first difference, the difference between the horizontal coordinates of the starting position node and the ending position node of the flight section is recorded as the second difference, the ratio of the first difference to the second difference is recorded as the first ratio, the arc tangent value of the first ratio is recorded as the first angle, and the radian value of the first angle is recorded as the movement direction of the UAV on the flight section.

3. The automatic control method for cooperative motion of unmanned aerial vehicles according to claim 1, characterized in that: The method of obtaining the length of the flight section and the estimated speed of the drone on the flight section according to the flight data of the cooperative motion of the drone and the motion direction of the drone on the flight section includes: The Euclidean distance between the starting position node and the ending position node of the flight segment is recorded as the length of the flight segment; The angle between the movement direction of the UAV on the flight path and the wind direction in the mission area is recorded as the second angle; The cosine of the second angle is recorded as the first cosine; The product of the first cosine and the wind speed in the mission area is recorded as the first speed; The sum of the first speed and the average speed of the UAV in a windless environment is recorded as the estimated speed of the UAV on the flight section.

4. The automatic control method for cooperative motion of unmanned aerial vehicles according to claim 1, characterized in that: The step of obtaining the passing drones of the location node according to the mission data of the drone cooperative motion includes: According to the coordinates of the starting position of the UAV, the coordinates of the mission location to be performed, and the coordinates of all location nodes, a greedy algorithm is used to obtain the shortest path from each UAV to the mission location to be performed; The drones whose shortest paths pass through the location node are recorded as the path drones of the location node.

5. The automatic control method for cooperative motion of unmanned aerial vehicles according to claim 1, characterized in that: The method of calculating the estimated travel time of the UAV on the flight segment according to the length of the flight segment and the estimated speed of the UAV on the flight segment includes: The ratio of the length of the flight segment to the estimated speed of the UAV on the flight segment is recorded as the estimated travel time of the UAV on the flight segment.

6. The automatic control method for cooperative motion of unmanned aerial vehicles according to claim 1, characterized in that: The specific calculation method for obtaining the collision risk rate of the drone at the location node according to the passing drones of the location node and the estimated travel time of the drone on the flight section is: Among them, C(W i ,D Z ) is the i-th drone W i At location node D Z The collision risk rate, n Z is the location node D Z The number of passing drones, R Z,a is the location node D Z The ath UAV passes through the node D Z The total number of nodes on the shortest path, t(W Z,a ,P r-1,r ) is the location node D Z The ath passing drone W Z,a In flight segment P r-1,r Estimated travel time for the road section above, Q Z,i is the i-th drone W i To location node D Z The total number of nodes on the shortest path, t(W i ,P l-1,l ) is the i-th drone W i In flight segment P l-1,l Estimated travel time for the above road sections.

7. The automatic control method for cooperative motion of unmanned aerial vehicles according to claim 1, characterized in that: The obtaining the speed gain strength of the UAV on the flight section according to the estimated speed of the UAV on the flight section includes: A triangulation algorithm is used for all position nodes to obtain the triangular mesh of the mission area; The position nodes directly connected to the position node in the triangular mesh of the mission area are recorded as adjacent position nodes; The maximum value of the estimated speed of the UAV from the location node to all adjacent location nodes is recorded as the first maximum speed; The ratio of the predicted speed of the UAV on the flight section to the first maximum speed is recorded as the speed gain intensity of the UAV on the flight section.

8. The automatic control method for cooperative motion of unmanned aerial vehicles according to claim 1, characterized in that: The step of obtaining the heuristic factor of the UAV on the flight section according to the speed gain intensity of the UAV on the flight section and the collision risk rate of the UAV at the position node includes: The ratio of the weight of the preset collision risk rate to the collision risk rate of the UAV at the location node is recorded as the collision pheromone of the UAV on the flight section; The ratio of the weight of the preset speed gain intensity to the speed gain intensity of the UAV on the flight section is recorded as the speed gain pheromone of the UAV on the flight section; The sum of the collision pheromone and the speed gain pheromone of the UAV on the flight section is recorded as the pheromone dependence of the UAV on the flight section; The inversely proportional normalized value of the UAV's pheromone dependence on the flight section is recorded as the UAV's inspiration factor on the flight section.

9. The automatic control method for cooperative motion of unmanned aerial vehicles according to claim 1, characterized in that: The method uses an ant colony algorithm to obtain the optimal path for each drone to the task location based on the heuristic factors of the drone on all flight sections, and completes the automated control of the cooperative movement between drones, including: According to the preset values ​​of the initial pheromone concentration, pheromone volatility coefficient and ant quantity of the drone on all flight sections, the heuristic factors of the drone on all flight sections are substituted into the ant colony algorithm for modeling to obtain the optimal path for each drone to reach the mission location to be performed; The optimal path for each drone to reach the mission location is sent to the command center, and the command center issues instructions to each drone, and the drone moves according to the instructions issued by the command center.

10. An automated control system for cooperative motion of unmanned aerial vehicles, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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