Unmanned aerial vehicle formation obstacle avoidance method, system and device and storage medium

Abstract

The invention discloses an unmanned aerial vehicle formation obstacle avoidance method, system and device and a storage medium, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance. The method comprises the following steps: acquiring obstacle data information in a target area and flight data information of each unmanned aerial vehicle in an unmanned aerial vehicle formation; constructing an unmanned aerial vehicle obstacle avoidance model based on the obstacle data information and the flight data information of each unmanned aerial vehicle in the unmanned aerial vehicle formation; a speed adaptive gain item and an angle factor are introduced into a traditional artificial potential field method, and an obstacle repulsive force field is constructed to obtain first obstacle repulsive force; introducing an auxiliary traction force on the basis of the first obstacle repulsive force to obtain a second obstacle repulsive force; and according to the second obstacle repulsive force, constructing a control law of each unmanned aerial vehicle to control the unmanned aerial vehicle formation to perform obstacle avoidance flight. Repulsive force generated by the obstacle is adjusted according to the speed of the obstacle, meanwhile, the relative angle between the unmanned aerial vehicle and the obstacle is considered, and the oscillation problem existing in a traditional artificial potential field method is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle obstacle avoidance, and in particular relates to a method, system, device and storage medium for unmanned aerial vehicle formation obstacle avoidance. Background Art

[0002] Unmanned aerial vehicle formation obstacle avoidance technology is a key technology in unmanned aerial vehicle formation flight, especially in flight missions in complex environments. In the field of unmanned aerial vehicle obstacle avoidance, the artificial potential field method is a classic path planning method that guides unmanned aerial vehicles to avoid obstacles by simulating the force field of the physical world. This method is simple and intuitive, but there is a local minimum problem, that is, the unmanned aerial vehicle is easily trapped in the virtual potential well around the obstacle and cannot escape. In order to overcome these problems, researchers have proposed a variety of improved methods and new obstacle avoidance strategies. Although the improved artificial potential field method has made significant progress in the field of unmanned aerial vehicle formation obstacle avoidance, it still has some shortcomings and deficiencies.

[0003] Existing formation obstacle avoidance methods may not be able to adjust obstacle avoidance strategies in a timely manner when faced with dynamically changing environments, resulting in obstacle avoidance failures. This is particularly evident when drones are performing complex tasks. Obstacles in dynamic environments may be moving, and this uncertainty may cause pre-planned obstacle avoidance paths to quickly become ineffective, causing the drone formation to be unable to fly as planned. At the same time, drones are prone to falling into local minima, that is, in some cases, drones will be trapped by the virtual force field around obstacles and unable to find a suitable obstacle avoidance path, which may cause the drone to stagnate or repeatedly detour and fail to effectively complete the mission. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method, system, device and storage medium for obstacle avoidance of a UAV formation, so as to adjust the repulsive force generated by the obstacle according to the speed of the obstacle itself, while taking into account the relative angle between the UAV and the obstacle, and solving the oscillation problem existing in the traditional artificial potential field method.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, a method for obstacle avoidance of a drone formation is provided, comprising: obtaining obstacle data information in a target area and flight data information of each drone in a drone formation; constructing a drone obstacle avoidance model based on the obstacle data information and the flight data information of each drone in the drone formation; based on the drone obstacle avoidance model, introducing a speed adaptive gain term and an angle factor into a traditional artificial potential field method, constructing an obstacle repulsion field to obtain a first obstacle repulsion; introducing an auxiliary traction force on the basis of the first obstacle repulsion to obtain a second obstacle repulsion; and constructing a control law for each drone based on the second obstacle repulsion to control the drone formation to perform obstacle avoidance flight.

[0007] As an optional technical solution of the present invention, the flight data information of each UAV in the UAV formation includes the initial position, flight speed, flight direction and target position of each UAV; the obstacle data information includes the number of obstacles, obstacle position, obstacle shape and obstacle moving speed.

[0008] As an optional technical solution of the present invention, the drone obstacle avoidance model is constructed based on the obstacle data information and the drone flight data information of each drone formation, including: in two-dimensional space, based on the initial position of each drone, the center of the circumscribed circle of each drone is used as the center position of the drone; in two-dimensional space, based on the shape and position of each obstacle, the center of the circumscribed circle of each obstacle is used as the center position of the obstacle.

[0009] As an optional technical solution of the present invention, based on the unmanned aerial vehicle obstacle avoidance model, a velocity adaptive gain term and an angle factor are introduced into the traditional artificial potential field method to construct an obstacle repulsion field to obtain a first obstacle repulsion, including:

[0010] After the velocity adaptive gain term is introduced into the traditional artificial potential field method, the repulsive field is expressed as:

[0011] ;

[0012] in, represents the repulsive field, represents the repulsion parameter, ; represents the speed adaptive gain term, , Indicates the obstacle moving speed. Indicates taking an integer. Express Take the integer, It represents the intersection point between the line connecting the center position of the drone and the center position of the obstacle and the obstacle repulsion field. is the distance between the center position of the i-th UAV and the intersection point; is the Euclidean distance between the center position of the i-th UAV and the center position of the b-th obstacle; is the radius of the obstacle repulsion field;

[0013] The repulsive force generated by the repulsive field is expressed as:

[0014] ;

[0015] in, represents the repulsive force generated by the repulsive field, represents the gradient operator;

[0016] On the basis of introducing the speed adaptive gain term, the angle factor is introduced to construct the obstacle repulsion field, which is expressed as:

[0017] ;

[0018] in, represents the obstacle repulsion field, represents the flight angle, represents the critical collision angle;

[0019] The first obstacle repulsion force generated by the obstacle repulsion field is expressed as:

[0020] ;

[0021] in, Represents the first obstacle repulsion.

[0022] As an optional technical solution of the present invention, the auxiliary traction force is introduced on the basis of the first obstacle repulsion force to obtain the second obstacle repulsion force, which is expressed as:

[0023] The auxiliary traction force is expressed as:

[0024] ;

[0025] in, Indicates auxiliary traction force, represents the auxiliary traction coefficient, ; represents the flight speed of the i-th UAV, represents the expected speed of the i-th UAV based on the critical collision angle;

[0026] The second obstacle repulsion force is expressed as:

[0027] ;

[0028] in, Represents the second obstacle repulsion.

[0029] As an optional technical solution of the present invention, the control law of each UAV is constructed according to the second obstacle repulsion force to control the UAV formation to perform obstacle avoidance flight, including:

[0030] The control law is expressed as:

[0031] ;

[0032] in, represents the control input of the ith UAV, represents the distance term parameter, represents the acceleration of the drone, represents the number of neighbors of the ith drone, N represents the total number of drones, , , , , and Respectively represent the flight speed, current position and target position of the i-th UAV, , and Respectively represent the flight speed, current position and target position of the jth UAV, Indicates the position of the leader in the UAV formation. represents the total force acting on the drone;

[0033] The resultant force on the drone It is expressed as:

[0034] ;

[0035] in, The gravitational force on the drone is expressed as:

[0036] ;

[0037] in, represents the gravitational factor, represents the Euclidean distance from the i-th UAV to the target location;

[0038] It represents the sum of the repulsive forces on the i-th UAV from other UAVs in the UAV formation, expressed as:

[0039] ;

[0040] in, represents the potential field force parameter, represents the adjacency matrix formed by the communication relationship between each drone in the drone formation, represents the Euclidean distance between the i-th UAV and the j-th UAV, represents the expected distance between the i-th UAV and the j-th UAV, represents the normalization function, , represents the judgment function;

[0041] It represents the sum of the repulsive forces of all obstacles on the i-th UAV, expressed as:

[0042] ;

[0043] in, represents the repulsion parameter, , represents the speed adaptive gain term, L represents the total number of obstacles, is the Euclidean distance between the center position of the ith UAV and the center position of the bth obstacle, is the radius of the obstacle repulsion field, represents the normalization function, represents the flight angle, represents the critical collision angle;

[0044] It represents the auxiliary traction force of the i-th UAV, expressed as:

[0045] ;

[0046] in, represents the auxiliary traction coefficient, ; represents the flight speed of the i-th UAV, represents the expected speed of the i-th UAV based on the critical collision angle.

[0047] In a second aspect, a UAV formation obstacle avoidance system is provided, comprising: an information acquisition module, for acquiring obstacle data information in a target area and flight data information of each UAV in the UAV formation;

[0048] A model building module, used to build a drone obstacle avoidance model based on the obstacle data information and the flight data information of each drone in the drone formation;

[0049] A potential field construction module is used to introduce a speed adaptive gain term and an angle factor into the traditional artificial potential field method based on the UAV obstacle avoidance model, construct an obstacle repulsion field to obtain a first obstacle repulsion; and introduce an auxiliary traction force on the basis of the first obstacle repulsion to obtain a second obstacle repulsion;

[0050] The control module is used to construct a control law for each UAV according to the second obstacle repulsion force to control the UAV formation to perform obstacle avoidance flight.

[0051] In a third aspect, a UAV formation obstacle avoidance device is provided, including a processor and a storage medium;

[0052] The storage medium is used to store instructions;

[0053] The processor is used to operate according to the instructions to execute the steps of the drone formation obstacle avoidance method described in the first aspect.

[0054] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the program is executed by a processor, the steps of the drone formation obstacle avoidance method described in the first aspect are implemented.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention provides a method for obstacle avoidance of unmanned aerial vehicle formations. The method introduces a speed adaptive gain term into a traditional artificial potential field method, so that the repulsive force generated by an obstacle can be adjusted according to the speed of the obstacle itself. The angle factor is introduced to take into account that the relative angle between the unmanned aerial vehicle and the obstacle will also affect the obstacle avoidance behavior of the unmanned aerial vehicle. In view of the situation that the resultant force acting on the unmanned aerial vehicle in the potential field force control space is zero and falls into a local minimum, an auxiliary traction force is added to the unmanned aerial vehicle according to the flight speed direction of the unmanned aerial vehicle, so that its speed is adjusted to a critical collision speed, thereby towing the unmanned aerial vehicle away from the local extreme point, thereby solving the oscillation problem existing in the traditional artificial potential field method. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of a method for avoiding obstacles in formation of unmanned aerial vehicles according to an embodiment of the present invention;

[0058] Figure 2 is a schematic diagram of an obstacle fitting in an embodiment of the present invention;

[0059] Figure 3 is a schematic diagram of static obstacle avoidance of a UAV in an embodiment of the present invention;

[0060] Figure 4 is a schematic diagram of dynamic obstacle avoidance of a UAV in an embodiment of the present invention;

[0061] Figure 5 is a top view of obstacle avoidance based on the traditional artificial potential field method in an embodiment of the present invention;

[0062] Figure 6 is a top view of obstacle avoidance based on an improved artificial potential field method in an embodiment of the present invention;

[0063] Figure 7 is a schematic diagram of the distance between a drone and an obstacle in an embodiment of the present invention;

[0064] Figure 8 is a simulation result diagram of dynamic obstacle avoidance flight in an embodiment of the present invention;

[0065] Fig. 9 Schematic diagram of the flight path and y-axis distance of the UAV in an embodiment of the present invention;

[0066] Fig.10 is a three-dimensional diagram of flight simulation of a UAV formation avoiding obstacles in an embodiment of the present invention;

[0067] Fig.11 is a top view of a flight simulation of a UAV formation avoiding obstacles in an embodiment of the present invention;

[0068] Fig.12 is a graph showing a speed change of a drone in an embodiment of the present invention;

[0069] Fig.13 is a graph of the minimum distance between drones in an embodiment of the present invention;

[0070] Fig.14 is a distance diagram between the drone and obstacles in an embodiment of the present invention;

[0071] Fig.15 It is a three-dimensional diagram of flight simulation of a UAV formation avoiding obstacles in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0073] Example 1

[0074] This embodiment provides a method for avoiding obstacles in a UAV formation. Figure 1 As shown, the method comprises the following steps:

[0075] Step 1: Obtain obstacle data information in the target area and flight data information of each drone in the drone formation.

[0076] Specifically, the flight data information of each drone in the drone formation includes the initial position, flight speed, flight direction and target position of each drone. The obstacle data information includes the number of obstacles, obstacle positions, obstacle shapes and obstacle moving speeds.

[0077] Step 2: Based on the obstacle data information and the flight data information of each drone in the drone formation, a drone obstacle avoidance model is constructed.

[0078] Specifically, in the two-dimensional space, based on the initial position of each drone, the center of the circumscribed circle of each drone is used as the center position of the drone. In the two-dimensional space, based on the shape and position of each obstacle, the center of the circumscribed circle of each obstacle is used as the center position of the obstacle.

[0079] like Figure 2As shown in the figure, if the obstacle is a plane obstacle, determine the smallest square that encloses the plane obstacle, use the diagonal of the smallest square as the diameter to make the circumscribed circle of the plane obstacle, and take the center of the circumscribed circle of the plane obstacle as the center position of the plane obstacle. If the obstacle is a three-dimensional obstacle, determine the smallest convex body that encloses the three-dimensional obstacle, and take the geometric center of the smallest convex body as the center position of the three-dimensional obstacle.

[0080] In this embodiment, the drone and the obstacle are abstracted into a circular model. To facilitate mathematical analysis, the following processing is performed: the radius of the drone's circumscribed circle is Added to the circumcircle of the obstacle, so that the drone can be regarded as a particle for mathematical analysis. In order to eliminate the influence of additional factors such as information interaction, aircraft control, and air resistance, the radius of the circumcircle of the obstacle is increased. value as a safety distance. Figure 3 and Figure 4 As shown, the obstacle radius is expressed as ,in, Indicates the radius of the obstacle model.

[0081] like Figure 3 As shown in the figure, it is a static obstacle avoidance judgment. The radius of the obstacle repulsion field is , is the center position of the obstacle, that is, the center position of the circumscribed circle of the obstacle, is the center position of the i-th UAV, and the flight speed of the UAV is , the distance to the obstacle is , the relative angle between the current speed and the obstacle line is , which is the flight angle. When the distance between the drone and the obstacle is greater than the radius of the obstacle repulsion field, it can be determined that the obstacle poses no threat to the drone, so there is no need to take obstacle avoidance maneuvers; when the distance between the two is less than or equal to the radius of the obstacle repulsion field, there is a risk of collision. When the drone is within the repulsion field of the obstacle, it is necessary to consider the critical situation: draw a tangent to circle O through point P, and the tangent points are and , these two tangent points are the critical collision points, and The angle between is the critical collision angle. When the relative angle between the speed direction of the drone and the obstacle line is Greater than the critical collision angle When the relative angle between the speed direction of the drone and the obstacle line is Less than or equal to the critical collision angle Obstacle avoidance measures are required.

[0082] like Figure 4 As shown in Figure 2, the dynamic obstacle avoidance maneuver analysis is performed based on the relative speed between the UAV and the dynamic obstacle. At the moment, the drone detects a dynamic obstacle and it is within the range of its own repulsive field, and then starts to avoid the dynamic obstacle. At this moment, the speed of the obstacle is , then the relative speed between the UAV and the obstacle is .Depend on Figure 4 It can be seen that when the relative speed is at the angle between the two tangents When the angle is outside the range, the drone will not collide with the obstacle, but when the angle is within the range, the drone will collide with the obstacle. Figure 4 As shown in the figure, the relative speed between the drone and the obstacle is decomposed to obtain Axis and The two components in the axial direction are and , then the linear equation of the UAV's motion relative to the obstacle can be obtained as During the flight of the drone, the shortest distance from the center of the obstacle to the drone's motion path is , then substituting the obstacle center position into the straight line equation we get , then the obstacle avoidance judgment condition of the UAV facing dynamic obstacles is: , there will be no collision between the drone and the dynamic obstacle; and when , the two will collide, and an obstacle avoidance strategy is needed.

[0083] Step 3: Based on the UAV obstacle avoidance model, a speed adaptive gain term and an angle factor are introduced into the traditional artificial potential field method to construct an obstacle repulsion field to obtain a first obstacle repulsion; an auxiliary traction force is introduced on the basis of the first obstacle repulsion to obtain a second obstacle repulsion.

[0084] 3.1. The repulsive term in the traditional artificial potential field method is improved by introducing a speed adaptive gain term in the repulsive term, so that the repulsive force generated by the obstacle can be adjusted according to the speed of the obstacle itself. In this way, the drone can be affected by different repulsive forces when facing static and dynamic obstacles, thereby achieving static / dynamic obstacle avoidance.

[0085] Specifically, after the velocity adaptive gain term is introduced into the traditional artificial potential field method, the repulsive field is expressed as:

[0086] ;

[0087] in, represents the repulsive field, represents the repulsion parameter, ; represents the speed adaptive gain term, , Indicates the obstacle moving speed. Indicates taking an integer. Express Take an integer. Figure 4 As shown, It represents the intersection point between the line connecting the center position of the drone and the center position of the obstacle and the obstacle repulsion field. is the distance between the center position of the i-th UAV and the intersection point; is the Euclidean distance between the center position of the i-th UAV and the center position of the b-th obstacle; is the radius of the obstacle repulsion field.

[0088] The repulsive force generated by the repulsive field is expressed as:

[0089] ;

[0090] in, represents the repulsive force generated by the repulsive field, Represents the gradient operator.

[0091] After the improvement, when the obstacle speed is 0, it is a static obstacle, and the adaptive speed gain term is 1, which has no effect on the original repulsion; when the obstacle speed is not 0, it is a dynamic obstacle. At this time, if the drone is within the influence range of the dynamic obstacle repulsion potential field, as the distance between the drone and the obstacle decreases, the adaptive speed gain term gradually increases. This leads to an increase in dynamic repulsion, which effectively repels the drone away from the obstacle. Otherwise, it will cause a decrease in dynamic repulsion, thereby achieving the purpose of dynamic obstacle avoidance.

[0092] 3.2. The traditional artificial potential field method only considers the distance between the drone and the target point and obstacles when designing the potential field force. The change of the potential field force is mainly realized according to the change of distance. The closer the distance, the greater the repulsion, and vice versa. Figure 3 It can be seen that during the flight, the relative angle between the drone and the obstacle will also affect the obstacle avoidance behavior of the drone. Obviously, when the drone flies toward the center of the obstacle, the risk of colliding with the obstacle is the greatest, and the obstacle avoidance maneuver required is greater; and when it flies further away from the center point, the risk of colliding with the obstacle is smaller, and the obstacle avoidance maneuver required is also smaller. The angle factor is introduced into the repulsion field to improve it, so that the repulsion field of the obstacle is not only affected by the distance between the drone and the obstacle, but also by the relative angle between the two.

[0093] Specifically, on the basis of introducing the speed adaptive gain term, the angle factor is introduced to construct the obstacle repulsion field, which is expressed as:

[0094] ;

[0095] in, represents the obstacle repulsion field, represents the flight angle, represents the critical collision angle;

[0096] The first obstacle repulsion force generated by the obstacle repulsion field is expressed as:

[0097] ;

[0098] in, Represents the first obstacle repulsion.

[0099] 3.3. One drawback of the traditional artificial potential field method is that when using the potential field force to control the flight path of the UAV in space, the UAV will fall into a local minimum because the resultant force it receives is zero. By adding auxiliary traction, the flight direction of the formation is changed by applying acceleration to the formation, and its speed is adjusted to the critical collision speed, thereby pulling the formation away from the local optimal point and completing the obstacle avoidance behavior.

[0100] Specifically, the auxiliary traction force is expressed as:

[0101] ;

[0102] in, Indicates auxiliary traction force, represents the auxiliary traction coefficient, ; represents the flight speed of the i-th UAV, represents the expected speed of the i-th UAV based on the critical collision angle;

[0103] The second obstacle repulsion force is expressed as:

[0104] ;

[0105] in, Represents the second obstacle repulsion.

[0106] Step 4: Based on the repulsive force of the second obstacle, construct the control law of each UAV to control the UAV formation to perform obstacle avoidance flight.

[0107] The control law is expressed as:

[0108] ;

[0109] in, represents the control input of the ith UAV, represents the distance term parameter, represents the acceleration of the drone, represents the number of neighbors of the ith drone, N represents the total number of drones, , , , , and Respectively represent the flight speed, current position and target position of the i-th UAV, , and Respectively represent the flight speed, current position and target position of the jth UAV, Indicates the position of the leader in a drone formation. A drone formation contains a leader that leads other drones. Represents the resultant force acting on the drone.

[0110] The resultant force on the drone It is expressed as:

[0111] ;

[0112] in, The gravitational force on the drone is expressed as:

[0113] ;

[0114] in, represents the gravitational factor, Represents the Euclidean distance from the i-th UAV to the target location.

[0115] It represents the sum of the repulsive forces on the i-th UAV from other UAVs in the UAV formation, expressed as:

[0116] ;

[0117] in, represents the potential field force parameter, represents the adjacency matrix formed by the communication relationship between each drone in the drone formation, represents the Euclidean distance between the i-th UAV and the j-th UAV, represents the expected distance between the i-th UAV and the j-th UAV, represents the normalization function, , Represents the judgment function.

[0118] It represents the repulsive force of all obstacles on the i-th UAV, expressed as:

[0119] ;

[0120] in, represents the repulsion parameter, , represents the speed adaptive gain term, L represents the total number of obstacles, is the Euclidean distance between the center position of the ith UAV and the center position of the bth obstacle, is the radius of the obstacle repulsion field, represents the normalization function, represents the flight angle, represents the critical collision angle.

[0121] It represents the auxiliary traction force of the i-th UAV, expressed as:

[0122] ;

[0123] in, represents the auxiliary traction coefficient, ; represents the flight speed of the i-th UAV, represents the expected speed of the i-th UAV based on the critical collision angle.

[0124] Example 2

[0125] This embodiment further illustrates the effect of the present invention based on the embodiment 1 and in combination with simulation experiments.

[0126] In this embodiment, the gravitational parameter , repulsion parameter , auxiliary traction coefficient .

[0127] First, we experimented with the problem of avoiding static obstacles by placing two static obstacles in the space. The safe distance between the obstacles is set to m, the initial position of the drone is , the target location is , the center coordinates of obstacle 1 are , the center coordinates of obstacle 2 are The radius of obstacles is m. The simulation results of avoiding static obstacles are as follows Figure 6 shown.

[0128] like Figure 5 As shown in the figure, under the traditional artificial potential field method, when the drone flies between two obstacles, the flight path swings back and forth, resulting in local oscillation problems. Figure 6As shown in the figure, the swinging phenomenon between the two obstacles disappears under the present invention, forming a smooth flight path. It can be seen that the artificial potential field method (APF) in the present invention has a significant improvement effect on the local oscillation problem existing in the traditional artificial potential field method.

[0129] Figure 7 The distance between the drone and the obstacle under the two methods is shown. The shortest distance between the traditional artificial potential field method and obstacle 1 is 80.65m, and the shortest distance between the traditional artificial potential field method and obstacle 2 is 94.32m. The shortest distance between the improved artificial potential field method and obstacle 1 is 23.16m, and the shortest distance between the improved artificial potential field method and obstacle 2 is 52.16m. The total flight length of the drone during obstacle avoidance is calculated. The total flight length of the traditional artificial potential field method is 6187.9m, and the total flight length of the improved artificial potential field method is 6055.1m. The above data show that under the action of the improved artificial potential field method, the drone is closer to the obstacle during obstacle avoidance, avoiding additional obstacle avoidance maneuvers. At the same flight speed, it can reach the destination faster, improving the obstacle avoidance efficiency of the drone.

[0130] like Figure 8 As shown in the figure, the performance of the test is tested in the case of avoiding dynamic obstacles. A dynamic obstacle is placed in the space, and the initial position coordinates of the obstacle are , the obstacle radius is 150m and the moving speed is 10m / s.

[0131] The simulation results of avoiding dynamic obstacles are as follows: Figure 8 As shown, when At s, the drone encounters a dynamic obstacle. The position coordinates of the obstacle are , the drone autonomously avoids obstacles based on their movement. Fig. 9 The distance between the drone's flight path and the y-axis is shown. When avoiding static obstacles, the farthest distance from the y-axis is m, and when avoiding dynamic obstacles, the maximum distance from the y axis reaches m. Because the radius of static obstacles and dynamic obstacles is the same, the repulsive force generated at a certain moment is also the same. Therefore, the difference in the distance value from the y-axis proves that when the drone faces a dynamic obstacle, it can identify the dynamic obstacle according to the speed of the obstacle and adjust the flight path in real time, thereby achieving successful avoidance of the dynamic obstacle.

[0132] In summary, the repulsive field changes according to the state of the obstacle and the distance and angle between the UAV and the obstacle. Under the guidance of the improved artificial potential field force, the UAV can take effective avoidance measures according to the state, and can effectively improve the oscillation problem caused by the traditional artificial potential field method when facing obstacles.

[0133] Example 3

[0134] This embodiment, based on Embodiment 1 and Embodiment 2, conducts a simulation experiment in combination with a drone formation.

[0135] In this embodiment, 5 drones are selected to form a formation for simulation experiment analysis. The various parameters in the experiment are shown in Table 1.

[0136] Table 1 Experimental parameters

[0137] parameter value parameter value parameter value #timg#(m) (0, -100) #timg#(m) (50, -50) #timg#(m) (50, 50) #timg#(m) (100,0) #timg#(m) (0,100) #timg#(m) 6 #timg#(m) 30 #timg# 5 #timg# 5 #timg#(m) 500 #timg#(m / s) [35,75] #timg#(m) 20

[0138] In Table 1, Respectively represent the initial position coordinates of UAV1~UAV5, represents the wingspan of the drone, represents the expected distance between UAVs, The hour distance term parameter, represents the force term parameter, Indicates the initial flight altitude of the drone. Indicates the speed range of the drone, Indicates a safe distance.

[0139] like Fig.10 As shown in the figure, when the center of the UAV formation encounters an obstacle, the UAV formation reconstruction algorithm in the prior art is divided into two alliances and enters the flight mission area separated by the obstacle respectively, thereby achieving obstacle avoidance behavior. When encountering the second obstacle, the formation chooses to fly over from one side according to the situation to achieve obstacle avoidance.

[0140] like Fig.11 As shown in the figure, the solid UAV is the leader that successfully competed, and the gray circles are two obstacles. When the UAV formation encounters obstacle 1, the formation splits into two alliances. The upper alliance is led by UAV3, and the lower alliance is led by UAV2. Under the influence of the potential field force between aircraft and the potential field force of obstacles, the upper alliance forms a horizontal formation, while the lower alliance chooses to fly in a vertical formation. When encountering obstacle 2, the UAV2 alliance can be regarded as a dynamic obstacle for the UAV3 alliance. At this time, the UAV3 alliance changes the vertical formation, accelerates through the narrow channel, and achieves obstacle avoidance of static and dynamic obstacles.

[0141] like Fig.12 As shown in the figure, when the drone encounters two obstacles, it changes its position by adjusting its speed to achieve the change of formation. Fig.13 As shown in the figure, the distance between drones in the drone formation changes due to obstacles, but it is always within a safe range, so there will be no collision between drones when the drone alliance is avoiding obstacles.

[0142] like Fig.14As shown in the figure, the closest distance to obstacle 1 is 48.54m, and the closest distance to obstacle 2 is 37.59m. Fig.15 As shown in the figure, before 29 seconds and after 90 seconds, the distance between UAV5 and the leader is 0, indicating that UAV5 is the leader at this time, and all drones follow UAV5 as the leader; between 29 seconds and 90 seconds, UAV2 and UAV3 become the new leaders, and the distance between them and the leader is zero, and the distance between the other three drones and the leader changes. Therefore, this method can also enable the drone formation to change its formation according to the change of potential field force, and ensure that the formation passes through the obstacle area within a safe range, realizing autonomous avoidance of static / dynamic obstacles.

[0143] Example 4

[0144] This embodiment provides a UAV formation obstacle avoidance system, including:

[0145] An information acquisition module is used to obtain obstacle data information in the target area and flight data information of each drone in the drone formation;

[0146] A model building module, used to build a drone obstacle avoidance model based on the obstacle data information and the flight data information of each drone in the drone formation;

[0147] A potential field construction module is used to introduce a speed adaptive gain term and an angle factor into the traditional artificial potential field method based on the UAV obstacle avoidance model, construct an obstacle repulsion field to obtain a first obstacle repulsion; and introduce an auxiliary traction force on the basis of the first obstacle repulsion to obtain a second obstacle repulsion;

[0148] The control module is used to construct a control law for each UAV according to the second obstacle repulsion force to control the UAV formation to perform obstacle avoidance flight.

[0149] Example 5

[0150] This embodiment provides a drone formation obstacle avoidance device, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the drone formation obstacle avoidance method described in Example 1.

[0151] Example 6

[0152] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the drone formation obstacle avoidance method described in Example 1 are implemented.

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0154] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for avoiding obstacles in a UAV formation, characterized in that: include: Obtain obstacle data information in the target area and flight data information of each drone in the drone formation; Based on the obstacle data information and the flight data information of each drone in the drone formation, a drone obstacle avoidance model is constructed; Based on the UAV obstacle avoidance model, a velocity adaptive gain term and an angle factor are introduced into the traditional artificial potential field method to construct an obstacle repulsion field to obtain a first obstacle repulsion; Introducing an auxiliary traction force on the basis of the first obstacle repulsion force to obtain a second obstacle repulsion force; According to the repulsive force of the second obstacle, the control law of each UAV is constructed to control the UAV formation to perform obstacle avoidance flight.

2. The obstacle avoidance method for UAV formation according to claim 1, characterized in that: The flight data information of each drone in the drone formation includes the initial position, flight speed, flight direction and target position of each drone; The obstacle data information includes the number of obstacles, obstacle positions, obstacle shapes, and obstacle moving speeds.

3. The obstacle avoidance method for UAV formation according to claim 2, characterized in that: The step of constructing a drone obstacle avoidance model based on the obstacle data information and the drone flight data information of each drone formation includes: In the two-dimensional space, based on the initial position of each drone, the center of the circumscribed circle of each drone is used as the center position of the drone; In two-dimensional space, based on the shape and position of each obstacle, the center of the circumscribed circle of each obstacle is taken as the center position of the obstacle.

4. The obstacle avoidance method for UAV formation according to claim 3, characterized in that: Based on the unmanned aerial vehicle obstacle avoidance model, the velocity adaptive gain term and the angle factor are introduced into the traditional artificial potential field method to construct an obstacle repulsion field to obtain the first obstacle repulsion, including: After the velocity adaptive gain term is introduced into the traditional artificial potential field method, the repulsive field is expressed as: ； in, represents the repulsive field, represents the repulsion parameter, ; represents the speed adaptive gain term, , Indicates the obstacle moving speed. Indicates taking an integer. Express Take the integer, It represents the intersection point between the line connecting the center position of the drone and the center position of the obstacle and the obstacle repulsion field. is the distance between the center position of the i-th UAV and the intersection point; is the Euclidean distance between the center position of the i-th UAV and the center position of the b-th obstacle; is the radius of the obstacle repulsion field; The repulsive force generated by the repulsive field is expressed as: ； in, represents the repulsive force generated by the repulsive field, represents the gradient operator; On the basis of introducing the speed adaptive gain term, the angle factor is introduced to construct the obstacle repulsion field, which is expressed as: ； in, represents the obstacle repulsion field, represents the flight angle, represents the critical collision angle; The first obstacle repulsion force generated by the obstacle repulsion field is expressed as: ； in, Represents the first obstacle repulsion.

5. The obstacle avoidance method for UAV formation according to claim 4, characterized in that: The auxiliary traction force is introduced on the basis of the first obstacle repulsion force to obtain the second obstacle repulsion force, which is expressed as: The auxiliary traction force is expressed as: ； in, Indicates auxiliary traction force, represents the auxiliary traction coefficient, ; represents the flight speed of the i-th UAV, represents the expected speed of the i-th UAV based on the critical collision angle; The second obstacle repulsion force is expressed as: ； in, Represents the second obstacle repulsion.

6. The obstacle avoidance method for UAV formation according to claim 1, characterized in that: The method of constructing a control law for each drone based on the second obstacle repulsion force to control the drone formation to perform obstacle avoidance flight includes: The control law is expressed as: ； in, represents the control input of the ith UAV, represents the distance term parameter, represents the acceleration of the drone, represents the number of neighbors of the ith drone, N represents the total number of drones, , , , , and Respectively represent the flight speed, current position and target position of the i-th UAV, , and Respectively represent the flight speed, current position and target position of the jth UAV, Indicates the position of the leader in the UAV formation. represents the resultant force acting on the drone; The resultant force on the drone It is expressed as: ； in, The gravitational force on the drone is expressed as: ； in, represents the gravitational factor, represents the Euclidean distance from the i-th UAV to the target location; It represents the sum of the repulsive forces on the i-th UAV from other UAVs in the UAV formation, expressed as: ； in, represents the potential field force parameter, represents the adjacency matrix formed by the communication relationship between each drone in the drone formation, represents the Euclidean distance between the i-th UAV and the j-th UAV, represents the expected distance between the i-th UAV and the j-th UAV, represents the normalization function, , represents the judgment function; It represents the repulsive force of all obstacles on the i-th UAV, expressed as: ； in, represents the repulsion parameter, , represents the speed adaptive gain term, L represents the total number of obstacles, is the Euclidean distance between the center position of the ith UAV and the center position of the bth obstacle, is the radius of the obstacle repulsion field, represents the normalization function, represents the flight angle, represents the critical collision angle; It represents the auxiliary traction force of the i-th UAV, expressed as: ； in, represents the auxiliary traction coefficient, ; represents the flight speed of the i-th UAV, represents the expected speed of the i-th UAV based on the critical collision angle.

7. A UAV formation obstacle avoidance system, characterized in that: include: An information acquisition module is used to obtain obstacle data information in the target area and flight data information of each drone in the drone formation; A model building module, used to build a drone obstacle avoidance model based on the obstacle data information and the flight data information of each drone in the drone formation; A potential field construction module is used to introduce a speed adaptive gain term and an angle factor into the traditional artificial potential field method based on the UAV obstacle avoidance model, construct an obstacle repulsion field to obtain a first obstacle repulsion; and introduce an auxiliary traction force on the basis of the first obstacle repulsion to obtain a second obstacle repulsion; The control module is used to construct a control law for each UAV according to the second obstacle repulsion force to control the UAV formation to perform obstacle avoidance flight.

8. An obstacle avoidance device for drone formation, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the drone formation obstacle avoidance method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the drone formation obstacle avoidance method described in any one of claims 1 to 6 are implemented.

Images