A UAV formation control method based on improved artificial potential field and consistency

Abstract

The present invention relates to a UAV formation control method based on improved artificial potential field and consistency, belonging to the technical field of UAV formation control. The specific steps are described as follows: ① Improve the artificial potential field method, by setting the communication danger zone and the collision danger zone, define the dynamic adjustment parameters of the virtual force between machines to improve the traditional artificial potential field method, and solve the problems of internal collision avoidance and communication distance maintenance in the formation; ② Improve the consistency algorithm, introduce the time integral factor into the consistency algorithm, and give its definition, and use its cumulative effect to overcome the problem that the artificial potential field method is prone to fall into the local optimum, and improve the formation convergence speed; ③ Combine the above two improved methods to design a UAV formation control method based on improved artificial potential field and consistency.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle formation control, and in particular to a method and system for controlling an unmanned aerial vehicle formation based on improved artificial potential field and consistency. Background Art

[0002] With the continuous development of UAV technology, the execution of UAV formation collaborative tasks is a hot topic in current research. However, when a UAV formation performs a task, due to the large number of UAVs, especially in the critical stages of formation generation and formation transformation, collisions are likely to occur between them, resulting in interruption of the formation mission and degradation of overall performance. At the same time, the communication distance between UAVs is limited, which requires that when the formation flies, the UAVs must remain within a certain communication range to ensure the transmission of information. Therefore, how to better achieve internal collision avoidance and communication distance maintenance in the formation control process is a key issue in current formation control. The existing methods have poor collision avoidance and communication distance maintenance effects, poor parameter adaptability, and the trajectory is prone to unnecessary bending and jittering; in addition, there are also problems such as easy to fall into local optimality and slow convergence, which affect the performance and efficiency of the formation.

[0003] Based on this, the present invention improves the traditional artificial potential field method by setting communication danger zone and collision danger zone, defining dynamic adjustment parameters of virtual force between machines, and realizing internal collision avoidance and communication distance maintenance of UAVs during formation flight; at the same time, by introducing time integral factor into the consistency algorithm to improve the consistency algorithm, the problem that the artificial potential field method is prone to fall into local optimum is overcome, and the convergence speed and stability of the formation are improved; finally, the improved artificial potential field method is combined with the improved consistency algorithm to design a UAV formation control method based on improved artificial potential field and consistency. The present invention can improve the obstacle avoidance performance and overall collaboration ability of UAV formations, is applicable to a variety of complex mission scenarios, and provides a certain reference for the further development and application of UAV formation control technology.

[0004] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0005] The purpose of the present invention is to provide a UAV formation control method based on improved artificial potential field and consistency. Taking into account the problems of existing methods such as poor collision avoidance and communication distance maintenance, poor parameter adaptability, unnecessary bending and jittering of UAV tracks, and easy falling into local optimality, the traditional artificial potential field method and consistency algorithm are improved, and the two improved methods are combined to design a formation control method. In order to achieve the purpose of the above invention, the present invention provides a UAV formation control method based on improved artificial potential field and consistency. Figure 1 As shown, the method comprises the following steps:

[0006] (1) Improve the artificial potential field method by setting communication danger zone and collision danger zone and defining dynamic adjustment parameters of virtual force between machines to improve the traditional artificial potential field method;

[0007] (2) Improve the consistency algorithm, introduce the time integration factor into the consistency algorithm, and give its definition;

[0008] (3) Combining the above two improvement methods, a UAV formation control method based on improved artificial potential field and consistency is designed.

[0009] According to a preferred embodiment, the traditional artificial potential field method is improved by setting a communication danger zone and a collision danger zone and defining dynamic adjustment parameters of the virtual force between machines.

[0010] First, the collision danger zone and communication danger zone are established, and the improvement of the virtual force between drones is proposed. When entering the communication danger zone, the gravitational function starts to work, and the gravitational force reaches its maximum when the distance between drones is close to the maximum communication radius; when entering the collision danger zone, the repulsive function starts to work, and the repulsive force reaches its maximum when the distance between drones is close to the collision radius, thereby ensuring that the distance between drones is always kept at a safe distance. The potential field space is as follows Figure 2 shown.

[0011] Secondly, the dynamic adjustment parameter λ is introduced ij , which is associated with the relative speed of the UAVs to improve the parameter adaptability of the inter-machine virtual force.

[0012] The definition of improved inter-machine virtual force is as follows:

[0013]

[0014]

[0015] Among them, F att is gravity, F rep is the repulsive force, d ij Distance between machines, Represents the unit vector pointing from other drones to the current drone, Represents the unit vector pointing from the current drone to other drones. r is the collision radius, d0 is the communication danger radius, d1 is the collision danger radius, and d c is the communication radius. ij is a dynamic adjustment parameter, defined as follows:

[0016]

[0017] Among them, v i Indicates the current speed of the drone, v j Indicates the speed of the neighboring drone.

[0018] According to a preferred embodiment, the consistency algorithm is improved. The drone may fall into a local minimum during movement and cannot move to the specified position. To solve this problem, the time integration factor σ t Introducing the consensus algorithm:

[0019]

[0020] Among them, u i represents the control input of the current drone, u0 represents the control input of the navigator, and k i is the proportional coefficient of the error adjustment term, z i Represents the relative position deviation variable, σ t Represents the time integration factor of the formation.

[0021] Relative position deviation variable z i It indicates that the UAV can obtain the distance difference between the neighboring UAVs and the expected position difference with the leader, z i Defined as:

[0022]

[0023] Among them, N i Indicates the current drone, d j represents the expected position of the neighboring drone in the formation, a ij Represents the adjacency matrix element of the drone communication topology.

[0024] σ t The following situations should be considered in the selection:

[0025] ① Whether the formation falls into a local minimum. When the following drone does not reach the specified position (|p i -p0-d i |>Δ d ), and the speed difference with the pilot is less than a certain limit value (|v i -v0|<Δ v ), and after a period of time (t>Δt ), then the formation is judged to have fallen into a local minimum.

[0026] ②Define the time integration factor. σ t The value increases with time, and the force required for the drone to move to the desired position also accumulates and increases with time, thereby getting rid of the local minimum area and continuing to move toward the target position. t is defined as follows:

[0027]

[0028] Among them, ε is a positive real number, d i represents the expected position of the current UAV in the formation, Δ d Indicates distance tolerance, Δ v Indicates speed tolerance, Δ t By introducing the time integral factor, the problem of UAVs being trapped in local minima can be effectively solved, the formation control accuracy can be improved, and the reliability and mission execution efficiency of UAV formations in complex environments can be ensured.

[0029] According to a preferred embodiment, the improved artificial potential field method and the improved consistency algorithm are combined to design a UAV formation control method based on the improved artificial potential field and consistency. i =0, considering the system stability, in order to better combine the two methods, the method of the present invention is divided into two stages: initialization and formation control.

[0030] ① Initialization stage. First, constrain the speed of the leader within a certain range. As long as the speed difference and angular velocity difference between the leader and the follower are within a certain range, the speed of the follower is guaranteed to be greater than zero. Considering the influence of gravity and repulsion, in the angular velocity control law, in addition to the yaw angle difference θ between the drone and the leader, i In addition to -θ0, the improved artificial potential field constructed according to the present invention increases the adjustment term of gravity on the yaw angle Adjustment of repulsion force to yaw angle The drone is made to adjust its angular velocity in the direction of the resultant force. The control law for the follower state is designed as follows.

[0031]

[0032] Among them, the saturation function It is used to constrain the size of the control quantity within a certain range, ensuring that the angular velocity and speed of the follower converge to the leader at a certain speed; represents the first-order derivative of the leader UAV velocity with respect to time; sig(·) is defined as |·|sgn(·), where sgn is the sign function. represents the angle of gravity, Represents the angle of repulsion.

[0033] ② Formation control stage. After the initialization stage, the follower speed is limited to a certain range, and the UAV enters the formation control stage. The control method of single-machine collision avoidance and communication maintenance is borrowed and extended to the UAV formation. The consistency method is applied for formation flight, and the mutual influence between individual UAVs is considered. The control law of each UAV is as follows:

[0034]

[0035] Among them, α represents the weight coefficient of the repulsive force between drones, and β represents the weight coefficient of the attractive force between drones.

[0036] The beneficial effects of the present invention relative to the prior art are:

[0037] First, the artificial potential field method was improved; secondly, the consistency algorithm was improved; finally, the improved artificial potential field method and the improved consistency algorithm were combined to design a UAV formation control method based on improved artificial potential field and consistency.

[0038] The UAV formation control method based on improved artificial potential field and consistency proposed in this invention effectively solves the problems of internal collision avoidance and communication distance maintenance of the UAV formation, and improves the stability and reliability of the formation. It has a good guiding significance for the optimization of UAV formation control strategy, task execution in complex environment and improvement of overall system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of the method involved in this application;

[0040] Figure 2 It is a schematic diagram of the potential field of a single machine;

[0041] Figure 3 is the graph of the number of collisions changing with α;

[0042] Figure 4 is the graph of formation time changing with α;

[0043] Figure 5 is the graph of collision number changing with β;

[0044] Figure 6 is the graph of formation time changing with β;

[0045] Figure 7 It is the formation track diagram;

[0046] Figure 8 is the yaw angle variation curve;

[0047] Fig. 9 is the linear speed variation curve;

[0048] Fig.10 is the distance variation curve between machines;

[0049] Fig.11 The time when the formation falls into a local minimum;

[0050] Fig.12 This is the formation time comparison curve (original data);

[0051] Fig.13 This is the formation time comparison curve (smoothed). DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. For those skilled in the art, the specific meanings of the terms in the present invention can be understood in specific circumstances.

[0053] This embodiment provides a UAV formation control method based on improved artificial potential field and consistency. In order to verify the feasibility, applicability and effectiveness of the present invention, simulation analysis is designed from six aspects, including parameter selection, formation flight state analysis, distance between aircraft, collision probability analysis, local minimum value, and formation time.

[0054] The initial environment is set as a two-dimensional scene, with a total of 5 drones, of which 0 is the leader, and 1, 2, 3, and 4 are follower drones. The initial states of each drone are shown in Table 1. Under different tasks and application scenarios, drone clusters require different formations. Here, three formations, square, triangle, and linear, are considered, and their formation matrices are (1)(2)(3). The rows and columns of the formation matrix are defined as follows: The formation matrix has 3 rows, which represent the drone number, the distance between the drone and the formation leader in the X direction and the distance in the Y direction, respectively. The first column represents the position coordinates of the leader, and the other columns represent the coordinates of followers 1, 2, 3, and 4 relative to the leader.

[0055] Square initial matrix:

[0056]

[0057] V-shaped initial matrix:

[0058]

[0059] Rectilinear initial matrix:

[0060]

[0061] (1) Parameter selection

[0062] In order to select the best repulsion coefficient α, the gravity is set to 0, and the initial state that is easy to cause formation collision is randomly generated. 1000 formation experiments are carried out. The experimental results are as follows: Figure 3 , Figure 4 As shown in the figure, the number of collisions and the average formation time change with α. The experimental results show that:

[0063] ① When the α value is small, the repulsive force is insufficient, which makes it easy for drones to collide with each other;

[0064] ② As the α value increases, the number of collisions decreases sharply;

[0065] ③When α≥2, the number of collisions reaches a minimum and tends to be stable;

[0066] ④ As α increases, the repulsive force is too strong, resulting in excessive overshoot, and the time required to complete each formation gradually increases.

[0067] Based on the above analysis, in order to obtain better collision avoidance effect and consider the rationality of formation time, the α value is selected as 2.

[0068] Similarly, in order to select the best gravitational coefficient β, the repulsion is set to 0, and the initial state that is easy to cause the formation to lose contact is randomly generated. The formation experiment is carried out 1000 times. The number of formation loss and the average formation time are shown as follows: Figure 5 , Figure 6 shown.

[0069] ① As the β value increases, the number of lost connections gradually decreases;

[0070] ② When β≥5, the number of collisions reaches a minimum and tends to be stable;

[0071] ③As β increases, the time required to complete each formation also gradually increases.

[0072] Therefore, the repulsion parameter β is selected as 5. The main parameters involved in the experiment are shown in Table 2.

[0073] (2) Formation flight status analysis

[0074] In order to verify the effect of the UAV formation control method proposed in the present invention in actual flight, the formation flight trajectory, yaw angle and linear speed are analyzed. Figure 7 As shown in the figure, it can be seen that the initial position, speed and direction of the followers are different. When the formation movement begins, the followers adjust their own speed and yaw angle, move to the desired position relative to the leader, form a V-shaped formation, follow the leader, and transform into a straight line and square formation. The yaw angle and linear speed change curve is shown in Figure 8 , Fig. 9As shown in FIG. 1 , the yaw angle and linear velocity of the follower gradually tend to be consistent with the leader. The method of the present invention effectively realizes the convergence and maintenance of the UAV formation.

[0075] (3) Distance between machines

[0076] In order to verify the effect of the present invention in achieving internal collision avoidance and maintaining communication distance, the distance change curve between aircrafts during formation flying is analyzed. Fig.10 The colored solid lines in the figure represent the distance change curve between two drones. The part enclosed by the blue dotted line is the distance between drones during the formation process, during which it is easy to lose connection. The part enclosed by the red dotted line is the distance between drones during the formation change process, during which it is easy to collide. Fig.10 It can be seen that the UAVs in this paper always maintain a safe distance, and the system has strong internal collision avoidance and communication distance maintenance capabilities.

[0077] (4) Collision probability

[0078] To further verify the effectiveness of the present invention in achieving internal collision avoidance and maintaining communication distance, the initial state of the formation was randomly changed to generate a V-shaped formation, and the formation flew 1000 times. The experimental results are shown in Table 3. After the V-shaped formation was generated, the formation was changed to a straight line, and the formation flew again 1000 times. The experimental results are shown in Table 4. It can be seen from the data in the table that the present invention is effective in reducing collisions and loss of connection, which proves the effectiveness and reliability of the present invention.

[0079] (5) Local Minima

[0080] In order to verify the formation's ability to escape from the local minimum, the time it takes for the formation to fall into the local minimum is analyzed. The judgment condition for the formation's local minimum is: if the follower drone has not reached the designated position and the speed difference with the leader is less than a certain limit and lasts for a period of time, the formation is considered to have fallen into the local minimum area. Fig.11 As shown, the blue line indicates no time integration factor σ t , the red line indicates that there is a time integration factor σ t After the introduction of the time integration factor, the UAVs have the ability to escape from the local minimum area, and the time that the UAV formation falls into the local minimum is significantly reduced.

[0081] (6) Formation convergence speed

[0082] In order to verify the performance of the algorithm in terms of formation time, three methods are used for comparison: the traditional consistency algorithm method, the improved artificial potential field method proposed in this invention alone, and the improved artificial potential field method proposed in this invention and the time integration factor σ t, randomly change the initial state of the formation, fly the formation 1000 times, form a V-shaped formation and change the formation to a straight line. The formation time comparison is shown in the figure below. Fig.11 For the convenience of comparison, Fig.12 The data is filtered by moving average, and the comparison curve after smoothing is as follows Fig.12 As shown. Fig.13 It can be seen that the formation time (yellow line) of the improved artificial potential field method is increased by 30 seconds compared with the traditional consistency method (blue line). This is because the introduction of the potential field function increases the computational complexity and the complexity of the motion trajectory, and there is a local minimum problem, which leads to an extension of the overall formation time. The formation time of the present invention is nearly 15 seconds faster than the consistency method without considering the collision between aircraft. This is because the introduction of the time integral factor not only reduces the time for the formation to fall into the local minimum, but also improves the convergence speed of the formation.

[0083] In summary, the UAV formation control method based on improved artificial potential field and consistency proposed in the present invention improves the performance indicators such as control accuracy, distance between drones, collision probability, and formation time of UAV formation control, and effectively solves the problems of collision avoidance and communication distance maintenance within the UAV formation. This shows that the method of the present invention has certain advantages in improving the coordination efficiency and stability of UAV formations, and provides a certain reference for the application of UAV formations in complex environments.

[0084] Table 1 UAV initial state table

[0085] UAV Position / m Speed / (m / s) Yaw angle / θ UAV0 (0,0) 3 π / 6 UAV1 (-40,10) 4 π UAV2 (-20,-40) 8 π / 7 UAV3 (5,-40) 2.5 4π / 7 UAV4 (30,-20) 2 2π / 3

[0086] Table 2 Main parameters

[0087]

[0088]

[0089] Table 3 Formation

[0090] method Formation flying times Number of collisions Number of lost connections Traditional consistency approach 1000 705 172 Methods 1000 5 8

[0091] Table 4 Formation

[0092] method Formation flying times Number of collisions Number of lost connections Traditional consistency approach 1000 1000 155 Methods 1000 7 11

[0093] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "according to a preferred embodiment, it means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept. Throughout the text, the features guided by "preferably" are only an optional method and should not be understood as a must-have setting. Therefore, the applicant reserves the right to abandon or delete the relevant preferred features at any time.

Claims

1. A UAV formation control method based on improved artificial potential field and consistency, characterized in that: The steps include: (1) Improve the artificial potential field method. By setting the communication danger zone and collision danger zone, and defining the dynamic adjustment parameters of the virtual force between drones, the traditional artificial potential field method is improved. First, the collision danger zone and communication danger zone are established, and the improved virtual force between drones is proposed. When entering the communication danger zone, the gravity function starts to work, and the gravity reaches the maximum when the distance between drones approaches the maximum communication radius. When entering the collision danger zone, the repulsion function starts to work, and the repulsion reaches the maximum when the distance between drones approaches the collision radius, thereby ensuring that the distance between drones is always kept at a safe distance. Secondly, the dynamic adjustment parameter λ is introduced ij , which is associated with the relative speed of the UAVs to improve the parameter adaptability of the inter-machine virtual force; (2) The consistency algorithm is improved, a time integration factor is introduced into the consistency algorithm, and its definition is given. The improved consistency algorithm is as follows: Among them, u i represents the control input of the current drone, u0 represents the control input of the navigator, and v i represents the current speed of the drone, v0 represents the speed of the navigator, k i is the proportional coefficient of the error adjustment term, z i Represents the relative position deviation variable, σ t represents the time integration factor of the formation; (3) Combining the above two improvement methods, a UAV formation control method based on improved artificial potential field and consistency is designed; The definition of the improved inter-machine virtual force is as follows: Among them, F att is gravity, F rep is the repulsive force, d ij Distance between machines, Represents the unit vector pointing from other drones to the current drone, Represents the unit vector pointing from the current drone to other drones, d r is the collision radius, d0 is the communication danger radius, d1 is the collision danger radius, and d c is the communication radius; Dynamically adjust parameter λ ij The definition is as follows: Among them, v i Indicates the current speed of the drone, v j Indicates the speed of the neighboring drone.

2. The UAV formation control method according to claim 1, characterized in that: When the following drone does not reach the specified location (|p i -p0-d i |>Δ d ), and the speed difference with the pilot is less than a certain limit value (|v i -v0|<Δ v ), and after a period of time (t>Δ t ), then the formation is judged to have fallen into a local minimum.

3. The UAV formation control method according to claim 2, characterized in that: z i It indicates that the UAV can obtain the distance difference between neighboring UAVs and the expected position difference with the leader, z i Defined as: Among them, N i Indicates the current drone, d j represents the expected position of the neighboring drone in the formation, a ij Represents the adjacency matrix element of the drone communication topology.

4. The UAV formation control method according to claim 3, characterized in that: σ t The value increases with time, and the force of the drone moving to the desired position also accumulates and increases with time, thus getting rid of the local minimum area and continuing to move to the target position. t is defined as follows: Among them, ε is a positive real number, d i represents the expected position of the current UAV in the formation, Δ d Indicates distance tolerance, Δ v Indicates speed tolerance, Δ t is the set value; by introducing the time integral factor, the problem of UAVs being trapped in local minimal areas can be effectively solved, the formation control accuracy can be improved, and the reliability and mission execution efficiency of UAV formations in complex environments can be ensured.

5. The UAV formation control method according to claim 4, characterized in that: Combining the above two improvement methods, the UAV formation control method based on improved artificial potential field and consistency is designed as follows: ① Initialization phase; ②Formation control phase.

6. A UAV formation control system based on improved artificial potential field and consistency, characterized in that: The system is configured to: ① Improve the artificial potential field method. By setting the communication danger zone and collision danger zone, and defining the dynamic adjustment parameters of the virtual force between machines, the traditional artificial potential field method is improved. First, the collision danger zone and the communication danger zone are established, and the improved virtual force between machines is proposed. When entering the communication danger zone, the gravity function starts to work, and the gravity reaches the maximum when the distance between machines is close to the maximum communication radius; when entering the collision danger zone, the repulsion function starts to work, and the repulsion reaches the maximum when the distance between machines is close to the collision radius, thereby ensuring that the distance between drones is always kept at a safe distance; Secondly, the dynamic adjustment parameter λ is introduced ij , which is associated with the relative speed of the UAVs to improve the parameter adaptability of the inter-machine virtual force; ② Improve the consistency algorithm, introduce the time integration factor into the consistency algorithm, and give its definition. The improved consistency algorithm is as follows: Among them, u i represents the control input of the current drone, u0 represents the control input of the navigator, and v i represents the current speed of the drone, v0 represents the speed of the navigator, k i is the proportional coefficient of the error adjustment term, z i Represents the relative position deviation variable, σ t represents the time integration factor of the formation; ③ Combine the improved artificial potential field method with the improved consistency algorithm to design a UAV formation control method with improved artificial potential field and consistency; The definition of the improved inter-machine virtual force is as follows: Among them, F att is gravity, F rep is the repulsive force, d ij Distance between machines, Represents the unit vector pointing from other drones to the current drone, Represents the unit vector pointing from the current drone to other drones, d r is the collision radius, d0 is the communication danger radius, d1 is the collision danger radius, and d c is the communication radius; Dynamically adjust parameter λ ij The definition is as follows: Among them, v i Indicates the current speed of the drone, v j Indicates the speed of the neighboring drone.

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