A method, device, equipment and storage medium for obstacle avoidance in UAV swarm formation

Abstract

The present invention discloses a method, device, equipment and storage medium for obstacle avoidance in UAV cluster formation. A new artificial potential field model is obtained by using a new potential field function. In this model, an influence factor of the distance from the current position to the final target is added to the repulsive force field potential function. The modified potential field function has no point where the resultant force is 0 except at the target point, and there will be no situations of being unreachable and local minimum. The repulsive force coefficient and the gravitational force coefficient are determined based on experience. If the repulsive force coefficient is small, the UAV is close to the obstacle and there is a risk of hitting the obstacle. If the repulsive force coefficient is large, the flight distance of the UAV is increased, resulting in waste of energy. Therefore, this method adopts a method of adaptively modifying the repulsive force coefficient, which can optimize the repulsive force coefficient according to the current motion state of the UAV, and minimize the flight distance of the UAV under the condition of ensuring safe flight.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV obstacle avoidance, and particularly to a method, device, equipment and storage medium for UAV swarm formation obstacle avoidance based on an adaptive artificial potential field model. Background Technique

[0002] A UAV swarm is a multi-agent system composed of multiple UAVs with certain autonomous decision-making capabilities. The advantages of UAV swarms, such as coordination, intelligence, autonomy, anti-loss, and payload diversity, make them have great development potential in civilian and military fields. Compared with a single UAV, a UAV swarm formation has many advantages, such as small overall flight resistance and high task execution success rate. During the flight of a UAV swarm formation, various obstacles may be encountered. Effectively avoiding obstacles while maintaining the stability of the swarm formation is a necessary condition for its successful task execution.

[0003] The existing UAV swarm formation control structures mainly include three types: fully centralized, centerless distributed, and limited centralized distributed. The UAV swarm formation control methods mainly include leader-follower method, virtual structure method, and behavior control method. In the leader-follower method, the leader flies according to a preset trajectory, and the followers fly following the leader and maintain a specific geometric distance from the leader. In the virtual structure method, the swarm regards the formation structure as a rigid body, and there is a virtual center point in the rigid body as the leader of the swarm. Each UAV forms and maintains the desired formation by maintaining a specific geometric position relationship with the virtual center. The behavior control method is a distributed control method that realizes the overall swarm behavior by individual UAVs following relatively simple rules. This method divides the UAV swarm formation control process into several basic control behaviors to achieve the cooperation or competition relationship between UAVs. The UAV swarm formation control method based on the consensus theory realizes the formation and transformation of the swarm by selecting appropriate state variables that can reach consensus.

[0004] UAV swarm formation obstacle avoidance needs to consider both external obstacles and inter-aircraft collisions. By changing the operating states of each UAV in the formation, an ideal trajectory to avoid conflicts is planned to ensure that all UAVs in the formation maintain a certain safety distance from the obstacles. The essence of UAV obstacle avoidance can be regarded as a problem of solving sequential decisions. The existing formation obstacle avoidance methods in the literature mainly include two categories: based on optimization algorithms and based on potential fields and navigation functions. The obstacle avoidance method based on optimization algorithms transforms the obstacle avoidance problem into an optimal control problem. The UAVs in the formation make a certain performance index reach the optimal under the constraint conditions of fuel consumption cost, range cost, and threat cost. The obstacle avoidance methods based on potential fields and navigation functions mainly include the artificial potential field method and the velocity obstacle method. Among them, the artificial potential field method is more widely used. This method artificially endows the complex environmental space with a potential field space. The UAV swarm formation avoids obstacles under the combined action of the repulsive potential field and the attractive potential field and flies to the target point.

[0005] The artificial potential field method is a commonly used method for obstacle avoidance in UAV swarms. By artificially constructing an appropriate potential field function, the minimum value is only obtained at the moving target point of the swarm under this potential field function. The potential field function controls the moving direction of the UAVs to achieve obstacle avoidance. As Figure 7 shown, the UAVs will move along the direction of the resultant force of gravity and repulsion, successfully avoiding obstacles and finally reaching the target point. The advantages of the artificial potential field method include strong real-time control ability, simple operation method, and good smoothness and reliability of the planned path, etc.

[0006] However, the disadvantages of the traditional artificial potential field method include:

[0007] There may be problems such as the target being unreachable, local minima, and the flight of UAV swarms in a fixed formation not being able to completely avoid dangerous areas. The problems of the target being unreachable and local minima are due to the fact that in some special cases, the selected potential field function obtains local minima, resulting in the UAVs oscillating near the local minima.

[0008] In addition, the traditional artificial potential field method only performs obstacle avoidance for a single UAV and generally can only ensure that a single UAV reaches the destination smoothly. But for UAV swarm formations, the traditional artificial potential field method cannot ensure that all UAVs can smoothly avoid dangerous areas and reach the destination. Summary of the Invention

[0009] In view of the above problems, the present invention provides a method, device, equipment and storage medium for obstacle avoidance of UAV swarm formations that overcome the above problems or at least partially solve the above problems.

[0010] The present invention provides the following solutions:

[0011] A method for obstacle avoidance of UAV swarm formations, including:

[0012] Obtain the current coordinates of the target UAV and the coordinates of the moving target point at the current moment, where the target UAV is any one in the UAV swarm;

[0013] Calculate the target gravity by using the current coordinates and the gravitational coefficient determined in combination with the coordinates of the moving target point;

[0014] A number of first repulsive forces and a number of second repulsive forces are respectively calculated using an improved potential field function. The first repulsive force is the repulsive force between the target UAV and each UAV in the UAV cluster, and the second repulsive force is the repulsive force between the target UAV and each obstacle. The improved potential field function includes an influence factor of the distance between the current coordinate and the coordinate of the moving target point or the edge of the obstacle. The sum of a number of the first repulsive forces is obtained as the first total repulsive force generated by the remaining all UAVs on the target UAV, and the sum of a number of the second repulsive forces is obtained as the second total repulsive force generated by all obstacles on the target UAV. The sum of the first total repulsive force and the second total repulsive force is obtained as the target repulsive force.

[0015] An optimized repulsive force coefficient is obtained according to the current motion state of the target UAV by combining an adaptive modification method of the repulsive force coefficient.

[0016] The target direction vector is solved using the target gravitational force, the target repulsive force, and the optimized repulsive force coefficient, and the target direction vector is used as the motion direction of the target UAV at the next moment.

[0017] The position of the target UAV at the next moment is calculated in combination with the target direction vector.

[0018] Preferably, the improved potential field function for calculating the first repulsive force is expressed by the following formula:

[0019]

[0020] In the formula: d rep1(i,j) is the repulsive force between the UAV numbered i and the UAV numbered j, X(i, t) = (x i (t), y i (t), z i (t)) is the position coordinate of the UAV numbered i at time t, juli_goal(i) is the distance between the current coordinate of the UAV numbered i and the coordinate of the moving target point, ρ O is the relative safety distance, j = 1, 2, 3, i - 1, i + 1,..., n, X(j) is the position coordinate of the UAV numbered j, and d(i, j) is the distance between the UAV numbered i and the UAV numbered j.

[0021] Preferably, the improved potential field function for calculating the second repulsive force is expressed by the following formula:

[0022]

[0023] In the formula: d rep2(i,m)is the repulsive force between the UAV numbered i and the m-th obstacle, d(1,m) is the distance between the leading UAV and the edge of the m-th obstacle, m = 1, 2,..., num_obs(1), num_obs(1) is the number of obstacles that can affect the leading UAV, and obs(m) is the central coordinate of the obstacle.

[0024] Preferably: the target UAV is the leading UAV, and the moving target point coordinates are the coordinates of the cluster formation target point; the UAV is a following UAV, and the moving target point coordinates are calculated by the following formula:

[0025] goal(i,t) = X(1, t + 1) + M i

[0026] In the formula: X(1, t + 1) is the position of the leading UAV at t + 1, and M i is the desired formation matrix.

[0027] Preferably: the method for adaptively modifying the repulsive force coefficient includes:

[0028] Calculate the vector temp = (goal(i, t) - X(i,t)) / norm(goal(i, t) - X(i,t)) + λ * F(i) / norm(F(i));

[0029] In the formula: norm(·) is the modulus of the vector ·;

[0030] Calculate the offset vector

[0031] In the formula: v(i,t) and a(i,t) are the current speed and acceleration of the UAV numbered i respectively, and Δt is the time interval;

[0032] Calculate the vector OB(i_obs) = [obs(i_obs,1), obs(i_obs,2), OH(i,3)];

[0033] In the formula: obs(i_obs,1) and obs(i_obs,2) are the abscissa and ordinate of the center of the i_obs-th obstacle respectively, and OH(i,3) is the height coordinate of the offset vector OH(i);

[0034] Calculate the vector HB(i_obs) = OH(i) - OB(i_obs). For each obstacle numbered i_obs, i_obs = 1, 2,..., M, where M is the number of obstacles. If the modulus of the vector HB(i_obs) is less than its radius, then λ = λ * 2, and the repulsive force coefficient k_pot(i) = λ * norm(goal - X(i,t)).

[0035] Preferably, the target direction vector is represented by the following formula:

[0036] D(i) = d att (i) + k_pot(i) * (d rep1 (i) + d rep2 (i))

[0037] where: d att (i) is the target gravitational force received by the UAV numbered i, d rep1 (i) is the repulsive force received by the UAV numbered i from all UAVs, d rep2 (i) is the repulsive force received by the UAV numbered i from all obstacles.

[0038] Preferably, the position of the target UAV at the next moment is represented by the following formula:

[0039]

[0040] A UAV swarm formation obstacle avoidance device, comprising:

[0041] A coordinate acquisition unit, configured to acquire the current coordinates of the target UAV and the coordinates of the moving target point at the current moment, where the target UAV is any one in the UAV swarm;

[0042] A target gravitational force calculation unit, configured to calculate the target gravitational force by using the gravitational coefficient determined by combining the current coordinates and the coordinates of the moving target point;

[0043] A target repulsive force calculation unit, configured to calculate a plurality of first repulsive forces and a plurality of second repulsive forces respectively by using an improved potential field function, where the first repulsive force is the repulsive force between the target UAV and each UAV in the UAV swarm, and the second repulsive force is the repulsive force between the target UAV and each obstacle; the improved potential field function includes an influence factor of the distance between the current coordinates and the coordinates of the moving target point or the obstacle edge; summing a plurality of the first repulsive forces to obtain the first total repulsive force generated by the remaining all UAVs received by the target UAV, and summing a plurality of the second repulsive forces to obtain the second total repulsive force generated by all obstacles received by the target UAV; summing the first total repulsive force and the second total repulsive force to obtain the target repulsive force;

[0044] A repulsive force coefficient calculation unit, configured to obtain an optimized repulsive force coefficient according to the current motion state of the target UAV in combination with an adaptive method for modifying the repulsive force coefficient;

[0045] A direction vector acquisition unit, configured to solve for a target direction vector by using the target gravitational force, the target repulsive force, and the optimized repulsive force coefficient, where the target direction vector is used as the motion direction of the target UAV at the next moment;

[0046] A position calculation unit, configured to calculate the position of the target UAV at the next moment in combination with the target direction vector.

[0047] An obstacle avoidance device for UAV cluster formation, the device includes a processor and a memory:

[0048] The memory is used to store program codes and transmit the program codes to the processor;

[0049] The processor is configured to execute the above-mentioned UAV cluster formation obstacle avoidance method according to the instructions in the program codes.

[0050] A computer-readable storage medium, the computer-readable storage medium is used to store program codes, and the program codes are used to execute the above-mentioned UAV cluster formation obstacle avoidance method.

[0051] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0052] A UAV cluster formation obstacle avoidance method, device, equipment and storage medium provided by an embodiment of the present application adopt a new potential field function to obtain a new artificial potential field model. In this model, an influence factor of the distance from the current position to the final target is added to the repulsive force field potential function. Except for the target point, there is no point where the resultant force is 0 in the modified potential function, and the situations of unreachability and local minimum will not occur. The repulsive force coefficient and the gravitational force coefficient are determined based on experience. If the repulsive force coefficient is small, the UAV is close to the obstacle and there is a risk of hitting the obstacle. If the repulsive force coefficient is large, the flight distance of the UAV is increased, resulting in waste of energy. Therefore, this method adopts a method of adaptively modifying the repulsive force coefficient, which can optimize the repulsive force coefficient according to the current motion state of the UAV, and minimize the flight distance of the UAV under the condition of ensuring safe flight.

[0053] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0055] Figure 1 is a flowchart of a UAV cluster formation obstacle avoidance method provided by an embodiment of the present invention;

[0056] Figure 2It is a flowchart of the obstacle avoidance process of the leading UAV and the following UAV provided by an embodiment of the present invention;

[0057] Figure 3 It is a flight route map of the UAV after setting obstacles and adopting the method provided by an embodiment of the present application;

[0058] Figure 4 It is provided by an embodiment of the present invention Figure 3 Top view of;

[0059] Figure 5 It is an obstacle avoidance flight route map using the traditional preset repulsion coefficient provided by an embodiment of the present invention;

[0060] Figure 6 It is an obstacle avoidance flight route map using the self-adaptive modified repulsion coefficient provided by an embodiment of the present application;

[0061] Figure 7 It is a schematic diagram of obstacle avoidance by the artificial potential field method;

[0062] Figure 8 It is a schematic diagram of an obstacle avoidance device for UAV cluster formation provided by an embodiment of the present invention;

[0063] Figure 9 It is a schematic diagram of an obstacle avoidance device for UAV cluster formation provided by an embodiment of the present invention. Detailed implementation manners

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present invention.

[0065] See Figure 1 , a method for obstacle avoidance of UAV cluster formation provided by an embodiment of the present invention, as Figure 1 shown, the method may include:

[0066] S101: Obtain the current coordinates of the target UAV and the coordinates of the moving target point at the current moment, where the target UAV is any one in the UAV cluster; it can be understood that the moving target point provided by the embodiments of the present application can determine the specific position according to the type of the UAV. For example, in one implementation manner, the embodiments of the present application may provide that the target UAV is a leading UAV, and the coordinates of the moving target point are the coordinates of the cluster formation target point; the UAV is a following UAV, and the coordinates of the moving target point are calculated by the following formula:

[0067] goal(i,t) = X(1, t + 1) + M i

[0068] Where: X(1, t + 1) is the position of the leading UAV at t + 1, and M i is the desired formation matrix.

[0069] S102: Calculate the target gravity using the gravitational coefficient determined by combining the current coordinates and the coordinates of the moving target point;

[0070] S103: Calculate a number of first repulsive forces and a number of second repulsive forces respectively using the improved potential field function. The first repulsive force is the repulsive force between the target UAV and each UAV in the UAV cluster, and the second repulsive force is the repulsive force between the target UAV and each obstacle; The improved potential field function includes an influence factor of the distance between the current coordinates and the coordinates of the moving target point or the edge of the obstacle; Sum a number of the first repulsive forces to obtain the first total repulsive force generated by the remaining all UAVs on the target UAV, and sum a number of the second repulsive forces to obtain the second total repulsive force generated by all obstacles on the target UAV; Sum the first total repulsive force and the second total repulsive force to obtain the target repulsive force;

[0071] Specifically, the improved potential field function for calculating the first repulsive force is expressed by the following formula:

[0072]

[0073] Where: d rep1(i,j) is the repulsive force between the UAV numbered i and the UAV numbered j, X(i, t) = (x i (t), y i (t), z i (t)) is the position coordinate of the UAV numbered i at time t, juli_goal(i) is the distance between the current coordinate of the UAV numbered i and the coordinates of the moving target point, ρ O is the relative safety distance, j = 1, 2, 3, i - 1, i + 1,..., n, X(j) is the position coordinate of the UAV numbered j, and d(i, j) is the distance between the UAV numbered i and the UAV numbered j.

[0074] The improved potential field function for calculating the second repulsive force is expressed by the following formula:

[0075]

[0076] Where: d rep2(i,m)$F_{rep}(i,m)$ is the repulsive force between the UAV numbered $i$ and the $m$-th obstacle, $d(1,m)$ is the distance between the leading UAV and the edge of the $m$-th obstacle, $m = 1,2,\cdots,num\_obs(1)$, $num\_obs(1)$ is the number of obstacles that can affect the leading UAV, and $obs(m)$ is the central coordinate of the obstacle.

[0077] S104: Obtain the optimized repulsive force coefficient according to the current motion state of the target UAV in combination with the method of adaptively modifying the repulsive force coefficient; specifically, when implemented, the method of adaptively modifying the repulsive force coefficient includes:

[0078] Calculate the vector $temp=(goal(i,t)-X(i,t)) / norm(goal(i,t)-X(i,t))+\lambda*F(i) / norm(F(i))$;

[0079] In the formula: $norm(\cdot)$ is the modulus of the vector $\cdot$;

[0080] Calculate the offset vector

[0081] In the formula: $v(i,t)$ and $a(i,t)$ are the current velocity and acceleration of the UAV numbered $i$ respectively, and $\Delta t$ is the time interval;

[0082] Calculate the vector $OB(i\_obs)=[obs(i\_obs,1),obs(i\_obs,2),OH(i,3)]$;

[0083] In the formula: $obs(i\_obs,1)$ and $obs(i\_obs,2)$ are the abscissa and ordinate of the center of the $i\_obs$-th obstacle respectively, and $OH(i,3)$ is the height coordinate of the offset vector $OH(i)$;

[0084] Calculate the vector $HB(i\_obs)=OH(i)-OB(i\_obs)$. For each obstacle numbered $i\_obs$, $i\_obs = 1,2,\cdots,M$, where $M$ is the number of obstacles. If the modulus of the vector $HB(i\_obs)$ is less than its radius, then $\lambda=\lambda*2$, and the repulsive force coefficient $k\_pot(i)=\lambda*norm(goal - X(i,t))$.

[0085] S105: Solve to obtain the target direction vector by using the target gravitational force, the target repulsive force, and the optimized repulsive force coefficient. The target direction vector is used as the motion direction of the target UAV at the next moment; specifically, when implemented, the target direction vector is represented by the following formula:

[0086] $D(i)=d$ att (i)+k\_pot(i)*(d rep1 (i)+d rep2 (i))

[0087] Where: d att (i) is the target gravitational force received by the UAV numbered i, d rep1 (i) is the repulsive force received by the UAV numbered i from all UAVs, d rep2 (i) is the repulsive force received by the UAV numbered i from all obstacles.

[0088] S106: Calculate and obtain the position of the target UAV at the next moment in combination with the target direction vector. Specifically, when implemented, the position of the target UAV at the next moment is represented by the following formula:

[0089]

[0090] The UAV swarm formation obstacle avoidance method provided by the embodiments of the present application adopts a new potential field function to obtain a new artificial potential field model. In this model, an influence factor of the distance from the current position to the final target is added to the repulsive force field potential function. Except for the target point, there is no point where the resultant force is 0 in the modified potential field function, and the situations of unreachability and local minimum will not occur. The repulsive force coefficient and the gravitational force coefficient are determined based on experience. If the repulsive force coefficient is small, the UAV is close to the obstacle and there is a risk of hitting the obstacle. If the repulsive force coefficient is large, the flight distance of the UAV increases, resulting in waste of energy. Therefore, this method adopts a method of adaptively modifying the repulsive force coefficient, which can optimize the repulsive force coefficient according to the current motion state of the UAV, and minimize the flight distance of the UAV under the condition of ensuring safe flight.

[0091] Next, taking the obstacle avoidance processes of the leader UAV and the follower UAV as examples respectively, the method provided by the embodiments of the present application will be described in detail, as Figure 2 shown.

[0092] (1) At time t, the current coordinates of the leader UAV are obtained as X(1, t) = (x1(t), y1(t), z1(t)), the coordinates of the swarm formation target point are goal(1, t) = (x0, y0, z0), and the gravitational force coefficient k att = 1, then the gravitational force vector is

[0093] d att (1) = (x0 - x1(t), y0 - y1(t), z0 - z1(t))

[0094] (2) The repulsive force vector consists of two parts: the repulsive force between UAVs and the repulsive force between UAVs and obstacles. In the patent, the improved artificial potential field method is used to solve the repulsive force vector, and the factor of the current UAV and the target end point is considered in the solution of the repulsive force.

[0095] Solve the repulsive force vector between the leader UAV and the j-th UAV, where j = 2, 3, ..., n. The repulsive force vector between the leader UAV and the j-th UAV is

[0096]

[0097] where juli_goal(1) is the distance between the leader UAV and the target point of the cluster formation, ρ O is the relative safety distance, X(j, t) is the current position coordinate of the UAV numbered j, and d(1, j) is the distance between the leader UAV and the j-th UAV.

[0098] The repulsive force received by the leader UAV from all the other UAVs is:

[0099]

[0100] (3) Solve the repulsive force vector between the leader UAV and the obstacle. The repulsive force vector received by the leader UAV from the m-th obstacle is:

[0101]

[0102] where d(1, m) is the distance between the leader UAV and the edge of the m-th obstacle, m = 1, 2, ..., num_obs(1), num_obs(1) is the number of obstacles that can affect the leader UAV, and obs(m) is the center coordinate of the obstacle.

[0103] The repulsive force received by the leader UAV from all the obstacles is:

[0104]

[0105] (4) The resultant repulsive force F(1) received by the leader UAV = d rep1 (1) + d rep2 (1).

[0106] (5) Let i = 1

[0107] (6) Run the optimization module to select the optimal repulsive force coefficient

[0108] The specific implementation process of the optimization module is as follows:

[0109] 6.1 Let λ = 1

[0110] 6.2 Calculate the vector

[0111] temp = (goal(i, t) - X(i, t)) / norm(goal(i, t) - X(i, t)) + λ * F(i) / norm(F(i)), where norm(·) is the modulus of the vector ·.

[0112] 6.3 Finding the offset vector

[0113]

[0114] where \(v(i,t)\) and \(a(i,t)\) are the current velocity and acceleration of the UAV numbered \(i\) (the leading UAV (\(i = 1\))), respectively, and \(\Delta t\) is the time interval.

[0115] 6.4 The vector \(OB(i_{obs})=[obs(i_{obs},1),obs(i_{obs},2),OH(i,3)]\).

[0116] where \(obs(i_{obs},1)\) and \(obs(i_{obs},2)\) are the abscissa and ordinate of the center of the \(i_{obs}\)-th obstacle, respectively. \(OH(i,3)\) is the height coordinate of the offset vector \(OH(i)\).

[0117] 6.5 The vector \(HB(i_{obs}) = OH(i)-OB(i_{obs})\).

[0118] For each obstacle numbered \(i_{obs}\), \(i_{obs}=1,2,\cdots,M\), where \(M\) is the number of obstacles, if the magnitude of the vector \(HB(i_{obs})\) is less than its radius, then \(\lambda=\lambda\times2\).

[0119] The repulsive force coefficient \(k_{pot}(i)=\lambda\times norm(goal - X(i,t))\), and the loop ends.

[0120] 6.6 \(\lambda=\lambda\times0.5\), jump to step 6.2.

[0121] (7) Solving the vector

[0122] \(D(1)=d\) att +k_{pot}(1)\times(d rep1 (1)+d rep2 (1))

[0123] The direction of the vector \(D(1)\) is used as the motion direction of the UAV at the next moment.

[0124] (8) The position of the leading UAV at time \(t + 1\) is:

[0125]

[0126] (9) Set the number \(i = 2\)

[0127] (10) According to the expected formation and the position of the leading UAV at time \(t + 1\), solve the coordinate of the motion target point of the following UAV numbered \(i\): \(goal(i,t)=X(1,t + 1)+M\) i . Where, \(M\) iis the desired formation matrix.

[0128] (11) At time t, the position coordinates of the following UAV numbered i are X(i, t) = (x i (t), y i (t), z i (t)), and its moving target point coordinates are goal(i, t) = (gx i , gy i , gz i ).

[0129] Take the gravitational coefficient k att = 1, then the gravitational vector is:

[0130] d att (i) = (gx i - x i (t), gy i - y i (t), gz i - z i (t))

[0131] (12) Solve the repulsive force vector between the UAV numbered i and the UAV numbered j. The repulsive force vector on the UAV numbered i by the UAV numbered j is:

[0132]

[0133] Among them, juli_goal(i) is the distance between the UAV numbered i and the target, ρ O is the relative safety distance, j = 1, 2, 3, i - 1, i + 1,..., n, X(j) is the position coordinates of the UAV numbered j, and d(i, j) is the distance between the UAV numbered i and the UAV numbered j.

[0134] The repulsive force on the UAV numbered i by all UAVs is:

[0135]

[0136] (13) Solve the repulsive force between the UAV numbered i and the obstacle. The repulsive force between the UAV numbered i and the m-th obstacle is:

[0137]

[0138] Among them, d(i, m) is the distance between the following UAV numbered i and the edge of the m-th obstacle, m = 1, 2,..., num_obs(1), num_obs(i) is the number of obstacles that can affect the UAV numbered i, and obs(m) is the center coordinates of the obstacle.

[0139] The repulsive force exerted on the UAV numbered i by all obstacles is:

[0140]

[0141] (14) The total repulsive force F(i) received by the following UAV numbered i = d rep1 (i) + d rep2 (i)

[0142] (15) The operation optimization module, that is, step (6), to obtain the optimized repulsive force coefficient k_pot(i)

[0143] (16) Solve the offset:

[0144] D(i) = d att (i) + k_pot(i) * (d rep1 (i) + d rep2 (i))

[0145] (17) The position of the UAV numbered i at time t + 1 is:

[0146]

[0147] (18) Whether the number i is less than n. If satisfied, then i + 1 → i and jump to step (10).

[0148] (19) Whether the time t has ended. If satisfied, then t + 1 → t and jump to step (1).

[0149] The embodiment of the present application adopts a new potential field function to obtain a new artificial potential field model, which will not have the situations of unreachability and local minimum that may occur in the traditional artificial potential field method. In addition, the method provided in the embodiment of the present application adopts a method of adaptively modifying the repulsive force coefficient to obtain a new artificial potential field model. Under this artificial potential field model, not only can safe flight be guaranteed, but also the flight distance of the UAV can be minimized.

[0150] Verification description of the invention effect:

[0151] Figure 3 If the set obstacles adopt the traditional artificial potential field method, there will be problems of target unreachability and local minimum. The results show that by using the method provided in the embodiment of the present application, obstacle avoidance for UAV swarms can be achieved, and there will be no problems of target unreachability, local minimum, and the inability of the UAV swarm flying in a fixed formation to completely avoid dangerous areas that may exist in the traditional artificial potential field method. Figure 3 and Figure 4 The results show that the new artificial potential field model will not have the situations of unreachability and local minimum that may occur in the traditional artificial potential field method.

[0152] Figure 5 Using the traditional preset repulsive force coefficient, Figure 6 Using the method for adaptively modifying the repulsive force coefficient provided by the embodiment of the present application, under the condition that other conditions are the same, Figure 5 The movement time is 76.4 seconds, Figure 6 The movement time is 73.25 seconds. The results show that the flight distance of the UAV is the smallest under the condition of ensuring safe flight by using the method provided by the embodiment of the present application. Figure 5 and Figure 6 The results show that by using the method for adaptively modifying the repulsive force coefficient, not only can safe flight be ensured, but also the flight distance of the UAV can be minimized under this artificial potential field model.

[0153] See Figure 8 , the embodiment of the present application can also provide a UAV cluster formation obstacle avoidance device, as Figure 8 shown, the device may include:

[0154] A coordinate acquisition unit 801, configured to acquire the current coordinate of the target UAV and the coordinate of the moving target point at the current moment, where the target UAV is any one of the UAV cluster;

[0155] A target gravitational force calculation unit 802, configured to calculate the target gravitational force by using the gravitational force coefficient determined by combining the current coordinate and the coordinate of the moving target point;

[0156] A target repulsive force calculation unit 803, configured to calculate a plurality of first repulsive forces and a plurality of second repulsive forces respectively by using an improved potential field function, where the first repulsive force is the repulsive force between the target UAV and each UAV in the UAV cluster, and the second repulsive force is the repulsive force between the target UAV and each obstacle; the improved potential field function includes an influence factor of the distance between the current coordinate and the coordinate of the moving target point or the edge of the obstacle; summing a plurality of the first repulsive forces to obtain the first total repulsive force generated by the remaining all UAVs on the target UAV, and summing a plurality of the second repulsive forces to obtain the second total repulsive force generated by all obstacles on the target UAV; summing the first total repulsive force and the second total repulsive force to obtain the target repulsive force;

[0157] A repulsive force coefficient calculation unit 804, configured to obtain an optimized repulsive force coefficient according to the current motion state of the target UAV in combination with the method for adaptively modifying the repulsive force coefficient;

[0158] A direction vector acquisition unit 805, configured to solve and obtain a target direction vector by using the target gravitational force, the target repulsive force, and the optimized repulsive force coefficient, where the target direction vector is used as the motion direction of the target UAV at the next moment;

[0159] A position calculation unit 806, configured to calculate the position of the target UAV at the next moment in combination with the target direction vector.

[0160] An embodiment of the present application may further provide a UAV cluster formation obstacle avoidance device, where the device includes a processor and a memory:

[0161] The memory is used to store program codes and transmit the program codes to the processor;

[0162] The processor is configured to execute the steps of the above-mentioned UAV cluster formation obstacle avoidance method according to the instructions in the program codes.

[0163] As Figure 9 shown, a UAV cluster formation obstacle avoidance device provided by an embodiment of the present application may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all complete mutual communication through the communication bus 13.

[0164] In an embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices, etc.

[0165] The processor 10 may call the program stored in the memory 11. Specifically, the processor 10 may execute the operations in the embodiment of the UAV cluster formation obstacle avoidance method.

[0166] The memory 11 is used to store one or more programs. The programs may include program codes, and the program codes include computer operation instructions. In an embodiment of the present application, the memory 11 stores at least programs for implementing the following functions:

[0167] Obtain the current coordinates of the target UAV and the coordinates of the moving target point at the current moment, where the target UAV is any one in the UAV cluster;

[0168] Calculate the target gravity by combining the current coordinates and the determined gravitational coefficient using the coordinates of the moving target point;

[0169] A number of first repulsive forces and a number of second repulsive forces are respectively calculated by using an improved potential field function. The first repulsive force is the repulsive force between the target UAV and each UAV in the UAV cluster, and the second repulsive force is the repulsive force between the target UAV and each obstacle. The improved potential field function includes an influence factor of the distance between the current coordinate and the moving target point coordinate or the obstacle edge. The sum of a number of the first repulsive forces is obtained to get the first total repulsive force generated by all the other UAVs on the target UAV, and the sum of a number of the second repulsive forces is obtained to get the second total repulsive force generated by all the obstacles on the target UAV. The sum of the first total repulsive force and the second total repulsive force is obtained to get the target repulsive force.

[0170] According to the current motion state of the target UAV, an optimized repulsive force coefficient is obtained by combining an adaptive method for modifying the repulsive force coefficient.

[0171] The target direction vector is obtained by solving using the target gravitational force, the target repulsive force, and the optimized repulsive force coefficient. The target direction vector is used as the motion direction of the target UAV at the next moment.

[0172] The position of the target UAV at the next moment is calculated by combining the target direction vector.

[0173] In a possible implementation manner, the memory 11 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function (such as a file creation function, a data reading and writing function), etc.; the data storage area may store the data created during use, such as initialization data, etc.

[0174] In addition, the memory 11 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one magnetic disk storage device or other volatile solid-state storage devices.

[0175] The communication interface 12 may be an interface of a communication module for connecting to other devices or systems.

[0176] Of course, it should be noted that Figure 9 the structure shown does not constitute a limitation on the UAV cluster formation obstacle avoidance device in the embodiments of the present application. In practical applications, the UAV cluster formation obstacle avoidance device may include more or fewer components than Figure 9 those shown, or combine some components.

[0177] The embodiments of the present application may also provide a computer-readable storage medium for storing program codes for executing the steps of the above-mentioned UAV cluster formation obstacle avoidance method.

[0178] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0179] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0180] Each embodiment in this specification is described in a progressive manner, and the same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for a system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment. The systems and system embodiments described above are only illustrative, where the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0181] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for avoiding obstacles in a drone swarm formation, characterized in that: include: Obtain the current coordinates of the target drone and the coordinates of the moving target point at the current moment, wherein the target drone is any one of the drone clusters; Calculating the target gravity using the current coordinates and the moving target point coordinates combined with the determined gravity coefficient; A plurality of first repulsive forces and a plurality of second repulsive forces are respectively calculated using the improved potential field function, wherein the first repulsive force is the repulsive force between the target UAV and each UAV in the UAV cluster, and the second repulsive force is the repulsive force between the target UAV and each obstacle; The improved potential field function includes an influencing factor of the distance between the current coordinate and the coordinate of the moving target point or the edge of the obstacle; a plurality of the first repulsive forces are summed to obtain a first total repulsive force generated by all other drones on the target drone, and a plurality of the second repulsive forces are summed to obtain a second total repulsive force generated by all obstacles on the target drone; summing the first total repulsive force and the second total repulsive force to obtain a target repulsive force; According to the current motion state of the target UAV, an optimal repulsion coefficient is obtained by combining an adaptive modification method of the repulsion coefficient; A target direction vector is obtained by using the target gravity, the target repulsion and the optimized repulsion coefficient, and the target direction vector is used as the movement direction of the target UAV at the next moment; The position of the target UAV at the next moment is obtained by calculating the target direction vector.

2. The obstacle avoidance method for drone swarm formation according to claim 1, characterized in that: The improved potential field function used to calculate the first repulsive force is expressed by the following formula: Where: d rep1(i,j) is the repulsive force between the UAV numbered i and the UAV numbered j, X(i, t) = (x i (t),y i (t),z i (t)) is the position coordinate of the UAV numbered i at time t, juli_goal(i) is the distance between the current coordinate of the UAV numbered i and the coordinate of the moving target point, ρ O is the relative safety distance, j = 1, 2, 3, i-1, i+1, ..., n, X(j) is the position coordinate of the UAV numbered j, and d(i, j) is the distance between the UAV numbered i and the UAV numbered j.

3. The obstacle avoidance method for drone swarm formation according to claim 1, characterized in that: The improved potential field function used to calculate the second repulsive force is expressed by the following formula: Where: d rep2(i,m) is the repulsive force between the UAV numbered i and the mth obstacle, d(1,m) is the distance between the pilot UAV and the edge of the mth obstacle, m=1,2,...,num_obs(1), num_obs(1) is the number of obstacles that can affect the pilot UAV, and obs(m) is the center coordinate of the obstacle.

4. The obstacle avoidance method for drone swarm formation according to claim 1, characterized in that: The target UAV is a pilot UAV, and the moving target point coordinates are the cluster formation target point coordinates; the UAV is a follower UAV, and the moving target point coordinates are calculated by the following formula: goal(i,t)=X(1,t+1)+M i Where: X(1, t+1) is the position of the pilot drone at t+1, M i is the desired formation matrix.

5. The obstacle avoidance method for drone swarm formation according to claim 1, characterized in that: The method for adaptively modifying the repulsion coefficient comprises: Find the vector temp=(goal(i,t)-X(i,t)) / norm(goal(i,t)-X(i,t))+λ*F(i) / norm(F(i)); Where: norm(·) is the norm of vector ·; Find the offset vector Where: v(i,t), a(i,t) are the current speed and acceleration of the UAV numbered i, respectively, and Δt is the time interval; Find the vector OB(i_obs)=[obs(i_obs,1),obs(i_obs,2),OH(i,3)]; Where: obs(i_obs,1) and obs(i_obs,2) are the horizontal and vertical coordinates of the center of the i_obsth obstacle respectively, and OH(i,3) is the height coordinate of the offset vector OH(i); Find the vector HB(i_obs)=OH(i)-OB(i_obs). For each obstacle numbered i_obs, i_obs=1,2,...,M, where M is the number of obstacles, if the modulus of the vector HB(i_obs) is less than its radius, then λ=λ*2, and the repulsion coefficient k_pot(i)=λ*norm(goal-X(i,t)).

6. The obstacle avoidance method for drone swarm formation according to claim 5, characterized in that: The target direction vector is expressed by the following formula: D(i)=d att (i)+k_pot(i)*(d rep1 (i)+d rep2 (i)) Where: d att (i) is the target gravity exerted on the UAV numbered i, d rep1 (i) The drone numbered i is repelled by all drones, d rep2 (i) The drone numbered i is subject to repulsion from all obstacles.

7. The obstacle avoidance method for drone swarm formation according to claim 6, characterized in that: The position of the target drone at the next moment is expressed by the following formula:

8. An obstacle avoidance device for drone cluster formation, characterized in that: include: A coordinate acquisition unit, used to acquire the current coordinates of the target UAV at the current moment and the coordinates of the moving target point, wherein the target UAV is any one of the UAV clusters; A target gravity calculation unit, used to calculate the target gravity by using the current coordinates and the coordinates of the moving target point in combination with the determined gravity coefficient; a target repulsion calculation unit, used to calculate and obtain a plurality of first repulsions and a plurality of second repulsions by using an improved potential field function, wherein the first repulsions are the repulsions between the target UAV and each UAV in the UAV cluster, and the second repulsions are the repulsions between the target UAV and each obstacle; The improved potential field function includes an influencing factor of the distance between the current coordinate and the coordinate of the moving target point or the edge of the obstacle; a plurality of the first repulsive forces are summed to obtain a first total repulsive force generated by all other drones on the target drone, and a plurality of the second repulsive forces are summed to obtain a second total repulsive force generated by all obstacles on the target drone; summing the first total repulsive force and the second total repulsive force to obtain a target repulsive force; A repulsion coefficient calculation unit, used to obtain an optimized repulsion coefficient according to the current motion state of the target UAV in combination with an adaptive repulsion coefficient modification method; a direction vector acquisition unit, used to obtain a target direction vector by using the target gravity, the target repulsion and the optimized repulsion coefficient, wherein the target direction vector is used as the movement direction of the target UAV at the next moment; The position calculation unit is used to calculate the position of the target UAV at the next moment in combination with the target direction vector.

9. An obstacle avoidance device for drone cluster formation, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the drone cluster formation obstacle avoidance method described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the drone cluster formation obstacle avoidance method described in any one of claims 1-7.

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