Unmanned aerial vehicle grouping formation tracking control method and system with anti-collision mechanism

Abstract

The application discloses a UAV grouping formation tracking control method and system with anti-collision mechanism, and relates to the technical field of UAV formation control. The method comprises the following steps: determining the topological relationship of a UAV grouping formation; the UAV grouping formation comprises at least one group; each group comprises at least one leader and at least one follower; the followers in each group are in communication connection with the leaders; based on the topological relationship, an artificial potential field method is used to construct an obstacle avoidance controller of the leaders and an obstacle avoidance controller of the followers; the obstacle avoidance controller of the leaders is used to control the movement of the leaders, and the obstacle avoidance controller of the followers is used to control the movement of the followers. The application realizes the cooperative obstacle avoidance of multiple UAVs while realizing the time-varying formation tracking.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle formation control, in particular to a method and system for tracking control of unmanned aerial vehicle grouping formation with anti-collision mechanism. BACKGROUND

[0002] Cluster is the future trend of work and military development, and research institutes of various countries are making in-depth research on cluster control. Formation control is the key to cluster control research and has become a top priority.

[0003] Grouping formation control plays a very important role in unmanned aerial vehicle combat and multi-missile guidance. For example, in unmanned aerial vehicle combat, when multiple targets need to be surrounded and captured, cluster combat is too cumbersome and it is difficult to capture each target, and if single combat is used, the combat effectiveness is too weak to deal a heavy blow to the target. Grouping cooperative combat is the best solution. Multi-missile guidance also has the same effect, which can ensure that multiple targets are attacked while improving the damage rate.

[0004] Time-varying systems have better universality than time-invariant systems and can adapt to most sudden situations, such as sudden environmental changes during patrol and surrounding. Traditional time-invariant systems cannot handle such changes. By studying time-varying formation, it is more flexible and better adapted to various task environments, and can better complete various types of tasks.

[0005] With the domestication of unmanned aerial vehicles, unmanned aerial vehicles are no longer in high altitudes, and performing tasks in low altitudes has become the mainstream. Unmanned aerial vehicles are seriously affected by the complex airspace of low-altitude dynamics, and autonomous obstacle avoidance mechanism has become an important mechanism to ensure the safety of unmanned aerial vehicle formation. An excellent and effective autonomous obstacle avoidance mechanism plays an important role in ensuring the safe operation of unmanned aerial vehicle formation and improving operational efficiency. Simple obstacle avoidance system actions include ascending, descending, decelerating, accelerating, and horizontal movement. In complex airspace, the dynamic changes of high and low make it difficult for simple obstacle avoidance systems to successfully complete the target task. Therefore, autonomous obstacle avoidance systems that can handle complex conflict environments and enable unmanned aerial vehicles to perform multiple obstacle avoidance actions sequentially or simultaneously have become the main research direction. It is necessary to ensure the safety of the aircraft, while also ensuring the efficiency and optimal solution of the obstacle avoidance system, which is the main goal of the obstacle avoidance system. Therefore, it is of practical significance to study the unmanned aerial vehicle grouping formation tracking control problem with anti-collision mechanism.

[0006] Now the obstacle avoidance of single machine is very mature, but the multi-machine cooperative obstacle avoidance is still a problem to be solved. SUMMARY

[0007] The application aims to provide a UAV grouping formation tracking control method and system with a collision avoidance mechanism, to realize time-varying formation tracking and cooperative obstacle avoidance of multiple UAVs.

[0008] To achieve the above-mentioned purpose, the application provides the following solutions.

[0009] A UAV grouping formation tracking control method with a collision avoidance mechanism comprises the following steps:

[0010] Determining a topological relationship of a UAV grouping formation; the UAV grouping formation comprises at least one group, and each group comprises at least one leader and at least one follower, and the followers in each group are in communication connection with the leaders;

[0011] Based on the topological relationship, an artificial potential field method is used to construct an obstacle avoidance controller of the leaders and an obstacle avoidance controller of the followers;

[0012] The obstacle avoidance controller of the leaders is used to control the movement of the leaders, and the obstacle avoidance controller of the followers is used to control the movement of the followers.

[0013] The application further discloses a UAV grouping formation tracking control system with a collision avoidance mechanism, comprising:

[0014] A topological relationship determination module is configured to determine a topological relationship of a UAV grouping formation; the UAV grouping formation comprises at least one group, and each group comprises at least one leader and at least one follower, and the followers in each group are in communication connection with the leaders;

[0015] An obstacle avoidance controller determination module is configured to, based on the topological relationship, use an artificial potential field method to construct an obstacle avoidance controller of the leaders and an obstacle avoidance controller of the followers;

[0016] A movement control module is configured to use the obstacle avoidance controller of the leaders to control the movement of the corresponding leaders and use the obstacle avoidance controller of the followers to control the movement of the followers.

[0017] According to the specific embodiments provided by the application, the following technical effects are disclosed:

[0018] Based on the topological relationship of the UAV grouping formation, the artificial potential field method is used to construct the obstacle avoidance controller of the leaders and the obstacle avoidance controller of the followers, to realize time-varying formation tracking and cooperative obstacle avoidance of multiple UAVs. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0020] Figure 1 A flow chart of a UAV grouping formation tracking control method with anti-collision mechanism provided by the embodiment of the present application is shown in the figure.

[0021] Figure 2 A topological relationship diagram of communication function of a multi-UAV system provided by the embodiment of the present application is shown in the figure.

[0022] Figure 3 A grouping formation communication topology diagram provided by the embodiment of the present application is shown in the figure.

[0023] Figure 4 A quad-rotor UAV model diagram provided by the embodiment of the present application is shown in the figure.

[0024] Figure 5 A force diagram of a UAV provided by the embodiment of the present application is shown in the figure.

[0025] Figure 6 A simulation comparison diagram of a compensation function provided by the embodiment of the present application is shown in the figure.

[0026] Figure 7 A search and rescue formation simulation diagram provided by the embodiment of the present application is shown in the figure.

[0027] Figure 8 A system structure diagram of a UAV grouping formation tracking control system with anti-collision mechanism provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, and all other embodiments obtained by those skilled in the art without creative labor based on the embodiments in the present application also belong to the scope of protection of the present application.

[0029] The purpose of the present application is to provide a UAV grouping formation tracking control method and system with anti-collision mechanism, to realize the formation of time-varying formation tracking and cooperative obstacle avoidance of multiple UAVs.

[0030] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0031] Example 1

[0032] like Figure 1 As shown in the figure, this embodiment provides a UAV group formation tracking control method with an anti-collision mechanism, which includes the following steps.

[0033] Step 101: Determine the topology of the drone group formation; the drone group formation includes at least one group, and each group includes at least one leader and at least one follower drone, and each follower in each group is communicatively connected to the leader.

[0034] If there are multiple groups in a drone formation, there is a general leader among the leaders of each group.

[0035] Step 102: Based on the aforementioned topological relationship, construct the obstacle avoidance controller for the leader and the obstacle avoidance controller for the follower using the artificial potential field method.

[0036] Step 103: Use the leader's obstacle avoidance controller to control the movement of the leader, and use the follower's obstacle avoidance controller to control the movement of the follower.

[0037] Specifically, step 101 includes: the topology relationship includes the communication topology relationship diagram and adjacency matrix of the UAV group formation.

[0038] Both the leader and the follower are quadcopter drones.

[0039] The communication topology diagram is the topology model. First, a model of a single UAV is established, and then combined with the topology model, the topology model of a multi-UAV system is obtained.

[0040] For effective drone formation control, information transfer between drones is a crucial element. Generally, the information transfer mechanism between drones in a drone formation system can be described using a directed or undirected graph; this graph used to describe the information transfer mechanism is called the interaction topology.

[0041] The topological graph G = {V, E, W} consists of a node set V, a boundary set E, and a weighted adjacency matrix W, used to describe the information exchange relationships between individuals in the formation. Where V = {v1, v2, ..., v...} N}, Represent N nodes and node v respectively. i to node v j The edge e ij The set, W = [w ij ]∈R N×N satisfy e ijweight of edge e, i.e. the cost of communication between node i and node j. In this invention, V represents the set of N quadrotor UAVs. If for any e ij ji ji ji is called undirected graph, otherwise, it is called directed graph. If the communication form is undirected graph, it will cause more communication cost, therefore, this invention is all directed graph. In directed graph, if UAV v j ji ij

[0042] The UAV system constructed in this invention is composed of N quadrotor UAVs, which are divided into N-M leaders and M followers, the set of followers is represented by F = {1, 2, …, M}, and the set of leaders is represented by E = {M+1, M+2, …, N}.

[0043] A directed graph G is called to have a spanning tree if at least one node can reach all other nodes through a directed path. The mathematical form of directed graph G is Laplacian matrix L, L = D-W, where D matrix is the in-degree matrix of each node.

[0044] Simple single group UAV system is shown in Figure 2 (a) and (c), the leader UAV is numbered 1, and the four follower UAVs are numbered 2, 3, 4 and 5, which communicate with each other and exchange information with the leader 1.

[0045] The communication function topology relationship of multi-UAV system is shown in Figure 3 .

[0046] Figure 2 The adjacency matrix, degree matrix and Laplacian matrix of the communication topology graph shown in (a), (b) and (c) are respectively written as:

[0047]

[0048]

[0049]

[0050] Wherein, W1, D1 and L1 represent the adjacency matrix, degree matrix and Laplacian matrix corresponding to the topology graph in Figure 2 (a), respectively, which are called the first adjacency matrix, the first degree matrix and the first Laplacian matrix; W2, D2 and L2 represent the adjacency matrix, degree matrix and Laplacian matrix corresponding to the topology graph in Figure 2 ​​​​​The adjacency matrix, degree matrix and Laplacian matrix corresponding to the topological relationship graph in (b) are denoted as a second adjacency matrix, a second degree matrix and a second Laplacian matrix, respectively, W3, D3 and L3 represent Figure 2 The adjacency matrix, degree matrix and Laplacian matrix corresponding to the topological relationship graph in (c) are denoted as a third adjacency matrix, a third degree matrix and a third Laplacian matrix.

[0051] The quadrotor unmanned aerial vehicle model is simplified as Figure 4 For the flying unmanned aerial vehicle, a body coordinate system is constructed with {x b ,y b ,z b}, wherein z b is the direction perpendicular to the xoy plane. In the established body coordinate system, the three coordinate axes {x b ,y b ,z b} of the fuselage represent {front, right, up} of the unmanned aerial vehicle. However, in order to represent the absolute position relationship of the quadrotor unmanned aerial vehicle in the earth space, a coordinate system E of the unmanned aerial vehicle in the absolute space of the earth needs to be created, and the coordinate axes {x e ,y e ,z e} of the coordinate system E represent {north, east, up} in geography, respectively. Figure 4 It can be seen that the directions of the propellers 1 and 3 are opposite to those of the propellers 2 and 4. Through the control of the rotation speeds of the four propellers, simple translational motion of the unmanned aerial vehicle along the x, y and z directions can be realized, and complex motion can also be realized due to the speed difference, mainly including yaw, roll and pitch.

[0052] The calculation formulas of the tension T and the torque M of the propeller are as follows:

[0053]

[0054] Wherein, N' (unit: RPM, revolutions per minute) is the rotation speed of the propeller, D p (unit: m) is the diameter of the propeller, C T and C M are the dimensionless tension coefficient and torque coefficient, and p is the air density of flight.

[0055] The rotation speeds of the four propellers of the quadrotor determine the total tension f of the quadrotor:

[0056]

[0057] Wherein, T iT represents the pull force of the ith propeller, ω1 represents the rotation speed of the first propeller (S1), ω2 represents the rotation speed of the second propeller (S2), ω3 represents the rotation speed of the third propeller (S3), and ω4 represents the rotation speed of the fourth propeller (S4).

[0058] The moment generated by the propeller is:

[0059]

[0060] wherein τ x represents the moment in the x direction, τ y represents the moment in the y direction, τ z represents the moment in the z direction, and d represents the distance from the propeller to the center (O Figure 4 ) of the quadrotor. b According to equations (2) and (3), the following matrix can be obtained:

[0061]

[0062] The rotation matrix for transforming the body coordinate system to the global coordinate system is:

[0063]

[0064] wherein, θ, ψ are the pitch angle, roll angle and yaw angle, respectively. The rotation matrix for transforming the global coordinate system to the body coordinate system is the transpose matrix of . The total lift generated by the four propellers of the quadrotor in the global coordinate system is Considering the gravity G, the following can be obtained:

[0065]

[0066] Suppose that the lift generated by the rotation of a single propeller of the quadrotor is f i (i = 1, 2, 3, 4), then the following can be obtained:

[0067]

[0068] wherein, represents the pull force of the quadrotor in the body coordinate system.

[0069] Combining equations (5) and (7), the size of the pull force of the quadrotor in the global coordinate system is determined as:

[0070]

[0071] The gravity is G = [0, 0, mg] T ​​Where g is the acceleration of gravity. Combining (7) and (8) can obtain the tension F of the quad-rotor sum :

[0072]

[0073] Can obtain:

[0074]

[0075] Combining (4) and (10) with the rotational speed of the four motors as input, the output is the simple motion of the quad-rotor, that is, the translational acceleration in x, y and z directions Up to now, the kinematic model of the unmanned aerial vehicle has been established, and m represents the mass of the quad-rotor.

[0076] Wherein, step 102 specifically comprises:

[0077] Step 1021: constructing a time-varying formation tracking control law of multiple unmanned aerial vehicles.

[0078] The unmanned aerial vehicle grouping formation system constructed by the application is composed of N quad-rotor unmanned aerial vehicles, which are divided into N-M leaders and M followers, the set of followers is represented by F={1, 2, …, M}, and the set of leaders is represented by E={M+1, M+2, …, N}. The dynamic characteristics of the leaders are represented by the following formula:

[0079]

[0080] Wherein, k∈E={M+1, M+2, …, N}, x k (t) represents the state of the kth leader in the system, and the state of the kth leader represents the dynamic characteristics of the kth leader. The kinematic characteristics of the followers are represented by the following formula:

[0081]

[0082] Wherein, i∈F={1, 2, …, M}, x i (t) and u i (t) respectively represent the state variable and the control input of the ith follower in the system, A is a state matrix, and B is an input matrix. When N-M=1, there is only one leader for the unmanned aerial vehicle grouping formation system; when N-M>1, the system has multiple leaders. The time-varying formation shape vector H i (t) of the followers finally expected to form is represented by the following formula:

[0083]

[0084] Wherein, R Mn represents H idimension of (t), h i (t) represents the formation vector of UAV i, i.e. the coordinate information of UAV i, i∈F={1, 2, …, M}.

[0085] The formation tracking formation can be achieved when the formation vector satisfies the following formula:

[0086]

[0087] where, let the nonsingular matrix and satisfy and satisfy

[0088] For any bounded initial state, it can satisfy:

[0089] lim(x i (t)-h i (t)-x k (t))=0(i∈F) (15)

[0090] If all the above definitions have been satisfied, the following follower time-varying formation tracking controller is designed:

[0091]

[0092] where, i∈F={1, 2, …, M}, is the controller of the i-th follower, K represents a constant gain matrix, v i (t) represents the compensation input of the formation configuration.

[0093] The above completes the design of the formation tracking control law in a single group, but when it comes to group formation control, we need to think about how to solve the problem of information transmission between groups.

[0094] Consider a system containing two leader UAVs as shown in Figure 3 The leader UAVs are numbered 1 and 4, and there are 5 follower UAVs, numbered 2, 3, 5, 6, and 7, with UAVs 2 and 3 following UAV 1 and UAVs 5, 6, and 7 following UAV 4. The position of UAV 4 is also controlled by the overall leader UAV 1.

[0095] UAVs 1 and 4 are also considered as UAVs within a group, and obviously they also satisfy the assumption of UAVs within a single group, so we can also consider the leader of each group of grouped UAVs as a single UAV formation, which will also receive information transmission from an overall leader. Therefore, the same leader must satisfy the following control law:

[0096]

[0097] Wherein, x N is the total leader in the entire leader formation, w N is the leader adjacency matrix parameter, in Figure 3 which 1 is the total leader.

[0098] Step 1022: Time-varying formation tracking obstacle avoidance control law design of multi-unmanned aerial vehicle system based on artificial potential field method.

[0099] In the process of unmanned aerial vehicle formation flight, the collision problem between unmanned aerial vehicle groups needs to be considered due to the emergence of sudden conditions, and the artificial potential field method is introduced to realize obstacle avoidance. The artificial potential field method is a traditional local path planning obstacle avoidance algorithm, which includes a repulsive force field. In the process of movement, the obstacles and other objects generate repulsive force on the object, and the target generates attractive force on the position. The resultant force received by the object at each point in the movement process is the sum of all repulsive forces and attractive forces as shown in the formula (1). Figure 5

[0100] The key of the artificial potential field method is how to calculate the attractive force of the target point on the unmanned aerial vehicle and the repulsive force of the obstacle on the unmanned aerial vehicle. Assuming that the unmanned aerial vehicle is at point (x, y) on the plane, represented by point q, the obstacle is at point (x o ,y o ), represented by point q obs , and the target point is at point (x g ,y g ), represented by point q goal . Define the attractive force field function between the unmanned aerial vehicle and the target point as U att (q), then U att (q) satisfies:

[0101]

[0102] In the above formula, K att represents the attractive force coefficient, and p goal (q) represents the distance between the unmanned aerial vehicle and the target point, p goal (q) should satisfy:

[0103]

[0104] Taking the negative gradient of the attractive force field formula, the calculation formula of the attractive force F att (q) can be obtained:

[0105]

[0106] By analyzing the above formula, it can be obtained that the greater the distance between the unmanned aerial vehicle and the target point, the greater the attractive force received by the unmanned aerial vehicle, and when the unmanned aerial vehicle approaches the target point, the value of the attractive force tends to 0.​

[0107] The repulsive force field function U between the UAV and the obstacle is defined according to the definition method of the gravitational field function rep (q) is as follows:

[0108]

[0109] In the above formula, K rep represents a repulsive force coefficient, p obs (q) represents the distance from the UAV to the obstacle, p0 represents the artificial potential field action range of the obstacle, i.e., the repulsive force influence range, and p obs (q) should satisfy formula (22):

[0110]

[0111] The repulsive force F rep (q) is obtained by taking the negative gradient of formula (21):

[0112]

[0113] Similarly, the theoretical analysis is performed on the above formula, and the closer the distance between the UAV and the obstacle, the greater the repulsive force received by the UAV. When the distance tends to 0, the repulsive force tends to infinity, so theoretically, the UAV cannot collide with the obstacle. When the UAV performs time-varying formation tracking flight, in order to avoid collision between the UAVs, each UAV in the UAV cluster can also be regarded as a “dynamic” obstacle. When the UAV is subjected to the combined action of multiple repulsive forces, the resultant force F att of the attractive force F rep and the repulsive force F total is represented as:

[0114]

[0115] For time-varying formation tracking of a multi-UAV system, the trajectory followed by each UAV can be regarded as “attractive force”, so it is not necessary to additionally increase the attractive force field, and the repulsive force received by each UAV is derived from other UAVs and obstacles. The repulsive force field potential function of formula (17) is applied, the negative gradient of the potential field function is taken to obtain the repulsive force received by the UAV, and in combination with formula (24), the resultant force F of the repulsive force is taken as the input of the obstacle avoidance control law:

[0116]

[0117] wherein, represents the repulsive force received by the UAV i, U ij (q i , q jrepresents the repulsive force field function between the ith UAV and the jth UAV, q i represents the ith UAV, q j represents the jth UAV.

[0118] However, the artificial potential field method has the problem of initial input of formation control static error:

[0119] Since the leader has an initial speed input, the formation tracking controller is difficult to meet the ideal conditions, and time is always advancing, while the follower is always following the position of the leader, but the leader is also advancing with time, so the follower is always chasing the position of the leader at the previous time, which leads to a difference between the follower and the leader, and the difference becomes larger as the formation time becomes larger, which may appear similar to the "Zeno paradox".

[0120] In order to prevent this situation, a compensation function and a sign function are introduced:

[0121]

[0122] The compensation function c is the upper limit of the input speed of the leader, u ik is a sign function that takes 1 when the follower falls behind the expected position due to too large a difference, takes 0 when it is in the expected position, and takes -1 when it is ahead of the expected position.

[0123] In the simulation, the method of introducing a compensation function is selected to correct this uncontrollable control law. In this patrol formation, the speed of the leader is [0.3, 0.2, 0.3], so the selection of the compensation factor c is to obtain the speed of the leader in the time period, i.e. [0.3, 0.2, 0.3]. The significance of introducing this compensation function is to compensate for the error caused by the inability to converge due to the input. However, this method has limitations, because the sign function is a step function in complex situations, and the speed response of the UAV swarm cannot reach such a fast degree, so in the simulation there may be a large fluctuation. Therefore, a smaller compensation factor should be applied to the actual application, and the calculation time should be regularly overlapped with the speed response time, so that the effect on the actual object can also achieve good results, as shown in Figure 6 .

[0124] Then, for the follower UAV, combining equation (25) with equation (16), a new time-varying formation tracking control law for the UAV with anti-collision mechanism can be obtained on the basis of the original time-varying formation tracking controller, as shown in the following equation:

[0125]

[0126] where, u irepresents an obstacle avoidance controller of the UAV i, i represents a number of the UAV, K represents a constant gain matrix, M represents a number of followers, N represents a total number of UAVs in the UAV group formation, w ij represents a communication cost of the UAV i and the UAV j, i.e., w ij represents an element in a corresponding adjacency matrix, x i (t) represents a state of the UAV i, h i (t) represents a time-varying formation vector of the UAV i, x j (t) represents a state of the UAV j, h j (t) represents a time-varying formation vector of the UAV j, x N (t) represents a state of the UAV N corresponding to a total leader in the UAV group formation, v i (t) represents a compensation input of the UAV i, represents a repulsion force on the UAV i. w ik represents a communication cost of the UAV i and the UAV k, x k (t) represents a state of the UAV k, c represents a compensation function, u ik represents a sign function.

[0127] The UAV formation leader control law is obtained by combining (25) and (17):

[0128]

[0129] wherein u j represents an obstacle avoidance controller of the UAV j, w jj′ represents a communication cost of the UAV j and the UAV j', x j′ (t) represents a state of the UAV j', h j′ (t) represents a time-varying formation vector of the UAV j', w jN represents a communication cost of the UAV j and the UAV N, v j (t) represents a compensation input of the UAV j, represents a repulsion force on the UAV j, j' is valued as M+1 to N-1, which are all leader UAVs.

[0130] The UAV group formation tracking control law designed above can not only achieve a required formation shape for a task, exchange information within and between groups, but also realize autonomous obstacle avoidance functions for static obstacles and dynamic obstacles in flight.

[0131] In step 103, based on a kinematic model of the UAV, a leader obstacle avoidance controller is used to control movement of the leader, and a follower obstacle avoidance controller is used to control movement of the follower.

[0132] Embodiment 2

[0133] The embodiment provides a specific implementation method of a UAV grouping formation tracking control method with a collision prevention mechanism.

[0134] Consider a multi-UAV system including 2 leader UAVs and 7 follower UAVs. Four-UAV formation control is adopted according to the judgment result of the possibility of the location of a search and rescue target. It is assumed that the UAVs have a certain detection range, the leader UAV of the UAV formation is the center of the formation and has its own movement trajectory, and the follower UAVs fly around the leader UAV.

[0135] The expected formation of follower 1, follower 2, follower 3, follower 4, follower 5, follower 6 and follower 7 can be described by the following formula:

[0136]

[0137] Wherein, pi = π.

[0138] The compensation term can be expressed by the following formula:

[0139]

[0140] Wherein, h i (t)' represents the derivative of h i (t).

[0141] The matrix R = R T = I2 and Q = Q T = I2 are brought into formula (3):

[0142] PA+A T P-PBR -1 B T P+Q = 0 (3)

[0143] Wherein, R and Q are arbitrary positive definite constant matrices, R = R T > 0 and Q = Q T > 0, and a positive definite solution P obtained by solving an algebraic Riccati equation satisfies P = P T > 0, I2 is an intermediate variable, A is a state matrix, and B is an input matrix.

[0144] P is obtained as follows:

[0145]

[0146] The normal number δ = 0.6 is substituted into formula K = - δ [Re (λ1)] -1 R -1 B TIn P, K is solved as follows:

[0147]

[0148] wherein L2 represents a second Laplacian matrix, L3 represents a third Laplacian matrix, λ2 represents an eigenvalue of the second Laplacian matrix, λ3 represents an eigenvalue of the third Laplacian matrix, K2 and K3 are both solutions of K.

[0149] The formation of inter-group communication is through the communication between leader 1 and leader 2, the total leader is leader 1, that is, only leader 1 has input, and other robots are determined through inter-machine relationship. The positions of leader 1 and leader 2 have the following relationship:

[0150]

[0151] The compensation term can be expressed by the following formula:

[0152]

[0153] The matrix R=R T =I2, Q=Q T =I2 is brought into formula (8):

[0154] PA+A T P-PBR -1 B T P+Q=0 (8)

[0155] Solving P obtains:

[0156]

[0157] Solving λ=1 of L1, taking δ=1, and substituting K=-δ[Re(λ1)] into the formula: -1 R -1 B T In P, K is solved as follows:

[0158]

[0159] The parameters calculated are brought into the Matlab simulation results as shown in Figure 7 In the Figure 7 , follower 1, 2, 3 follow leader 1, and follower 4, 5, 6, 7 follow leader 2 to complete the search and rescue task for the 4*4 area respectively, Figure 7The middle and lower part includes leader 1, follower 1, follower 2 and follower 3 (follower 1, follower 2, follower 3), the upper part leader 2, follower 4, follower 5, follower 6 and follower 7 (follower 4, follower 5, follower 6, follower 7), and the barrier represents an obstacle. (2.5; 2) is the position of the static obstacle in this scenario, and the follower 2 and the follower 3 of the first group can be seen to actively avoid the obstacle when they are about to contact the position, while the follower 1 of the first group does not have the need to avoid the obstacle between the follower 3, but still has a large disturbance because it receives the formation communication of the follower 3, which needs to be improved in the later task completion. The communication between groups is connected through the communication between the leaders of the two groups, and the communication between the followers and the leaders of the two groups and the communication between the two group leaders is completed through twice communication to complete the communication between the followers of different groups.

[0160] The real object experiment is carried out in the laboratory, and a 4*5*2 cubic experimental space is approximately formed. At most, six unmanned aerial vehicles can be controlled simultaneously in the region to carry out flight formation experiments. After investigation, the motion capture system is selected as the indoor positioning system, and the Tello unmanned aerial vehicle is selected as the real flight four-rotor unmanned aerial vehicle. After real flight verification, the reasons for the change of the unmanned aerial vehicle formation are analyzed, it is found that the control law is consistent, and the feasibility of the control law is verified.

[0161] Embodiment 3

[0162] As Figure 8 shown, the embodiment provides a UAV grouping formation tracking control system with a collision avoidance mechanism, which specifically comprises:

[0163] A topological relationship determination module 201 is configured to determine the topological relationship of the UAV grouping formation. The UAV grouping formation includes at least one group, and the UAVs in each group include at least one leader and at least one follower. The followers in each group are in communication connection with the leaders.

[0164] An obstacle avoidance controller determination module 202 is configured to construct the obstacle avoidance controller of the leader and the obstacle avoidance controller of the follower based on the topological relationship by using the artificial potential field method.

[0165] A motion control module 203 is configured to perform motion control on the leader by using the obstacle avoidance controller of the leader, and perform motion control on the follower by using the obstacle avoidance controller of the follower.

[0166] The various embodiments described in this specification are presented for the purpose of illustrating the principles of the present application and its best mode of operation. Each of the embodiments described in this specification has been provided for the purpose of illustration only and the various embodiments are not intended to limit the present application in any way unless otherwise specifically indicated. The same parts and / or features of the various embodiments described in this specification can be referenced using the same reference numerals for the ease of understanding of the present application.

[0167] The principles and implementations of the present application have been described above with the specific examples. The above description of the embodiments is only for the purpose of helping to understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation and application range of the present application can be changed according to the idea of the present application. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for tracking control of a UAV formation with anti-collision mechanism, characterized in that, The application relates to a method for controlling a group formation of unmanned aerial vehicles (UAVs), and a UAV group formation control system. The method comprises the following steps: determining a topological relationship of the UAV group formation; the UAV group formation comprises at least one group, each group comprises at least one leader and at least one follower, and each follower is connected to the leader in communication; constructing an obstacle avoidance controller of the leader and an obstacle avoidance controller of the follower by using an artificial potential field method based on the topological relationship; controlling the movement of the leader by using the obstacle avoidance controller of the leader and controlling the movement of the follower by using the obstacle avoidance controller of the follower; ； wherein, represents an obstacle avoidance controller of the UAV, i K represents a constant gain matrix, M represents the number of followers, N represents the total number of UAVs in the UAV formation, represents a communication cost of the UAV i and the UAV j , represents a state of the UAV i , represents a time-varying formation shape vector of the UAV i , represents a state of the UAV j , represents a time-varying formation shape vector of the UAV j , represents a communication cost of the UAV i and the UAV k , represents a state of the UAV k , represents a compensation input of the UAV i , represents a repulsive force experienced by the UAV i , c represents a compensation function, represents a sign function, t represents time.​ 2.The UAV grouping formation tracking control method with anti-collision mechanism according to claim 1, wherein, the obstacle avoidance controller of the follower is expressed as: 3.The UAV grouping formation tracking control method with anti-collision mechanism according to claim 1, wherein, the topological relationship comprises a communication topological relationship diagram and an adjacency matrix of the UAV group formation. 4.The UAV grouping formation tracking control method with anti-collision mechanism according to claim 1, wherein, The leader and the follower are both quadrotor UAVs. ； wherein, represents an obstacle avoidance controller of the UAV, j represents a communication cost of the UAV, j and the UAV, j’ represents a state of the UAV, j’ represents a time-varying formation vector of the UAV, j’ represents a communication cost of the UAV, j and the UAV, N represents a compensation input of the UAV, j represents a repulsive force experienced by the UAV, j represents a communication cost of the UAV, j and the UAV, N represents a state of the UAV, j is a state of the UAV N, the UAV N being the overall leader in the group formation of UAVs.​​​​​​​​​ 5.The UAV grouping formation tracking control method with anti-collision mechanism according to claim 1, wherein, The obstacle avoidance controller of the leader is expressed as: ； wherein, represents a drone i repelled, represents the repulsion field function between the i first drone and the j first drone, represents the i first drone, represents the j first drone.

6. A UAV formation tracking control system with anti-collision mechanism, characterized in that, repulsion force borne by the UAV is expressed as: The application relates to a method for controlling a group formation of unmanned aerial vehicles (UAVs), and a UAV group formation control system. A topological relationship determining module is used for determining a topological relationship of the UAV group formation; the UAV group formation comprises at least one group, each group comprises at least one leader and at least one follower, and each follower is connected to the leader in communication; an obstacle avoidance controller determining module is used for constructing an obstacle avoidance controller of the leader and an obstacle avoidance controller of the follower by using an artificial potential field method based on the topological relationship; a movement control module is used for controlling the movement of the leader by using the obstacle avoidance controller of the leader and controlling the movement of the follower by using the obstacle avoidance controller of the follower; the obstacle avoidance controller of the follower is expressed as: ； wherein, represents an obstacle avoidance controller of the UAV i, K represents a constant gain matrix, M represents the number of followers, N represents the total number of UAVs in the UAV formation group, represents the communication cost of the UAV i and the UAV j, represents the state of the UAV i, represents the time-varying formation shape vector of the UAV i, represents the state of the UAV j, represents the time-varying formation shape vector of the UAV j, represents the communication cost of the UAV i and the UAV k, represents the state of the UAV k, represents the compensation input of the UAV i, represents the repulsion force suffered by the UAV i, c represents a compensation function, represents a sign function, t represents time.

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