Multi-unmanned aerial vehicle formation control method and system

Abstract

The invention discloses a multi-unmanned aerial vehicle formation control method and system, and relates to the technical field of unmanned aerial vehicle control. The method comprises the steps of firstly defining a communication topological structure, then initializing the number of unmanned aerial vehicles and communication weights according to the communication topological structure, then establishing a formation control model, updating a formation task and pose information of the unmanned aerial vehicles, and calculating an optimal solution through a formation control law, thereby realizing stable formation flight. According to the invention, for a scene that the efficiency of a single unmanned aerial vehicle is limited and a conventional pilot following method may have a fault in an emergency, a communication topological structure is defined based on a directed weighted graph and is used for an improved formation control law, so that under the condition that the unmanned aerial vehicle has a fault, the remaining aerial vehicle groups can be controlled, and the formation control efficiency is improved. The multiple unmanned aerial vehicles can efficiently and stably execute the formation task in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control technology, and in particular to a multi-unmanned aerial vehicle formation control method and system thereof. Background Art

[0002] Drones have the advantages of simple operation and easy assembly, and are currently widely used in civil and military fields. However, as mission requirements and application scenarios become increasingly complex, a single drone is restricted by load and efficiency and cannot complete the mission comprehensively and quickly. Therefore, multiple drones can be used to obtain information from adjacent drones and perform tasks in a coordinated manner in accordance with mission requirements. In the above process, a multi-drone formation control method and system are needed.

[0003] Among the formation control methods, the pilot-follow method has low communication requirements and high formation accuracy. The physical pilot can improve the timeliness and consistency of decision-making, reduce communication redundancy and delay, and has an irreplaceable role. However, the pilot-follow method is overly dependent on the pilot. Once the pilot fails during the mission execution, the entire fleet will be out of control, resulting in mission failure. Therefore, it cannot be applied in complex environments. Summary of the invention

[0004] The present invention provides a multi-UAV formation control method and system thereof to solve the problems of low efficiency of single UAV mission execution, poor robustness of the pilot-follower formation, and inability to be applied to complex environments.

[0005] The first technical solution adopted by the present invention is as follows:

[0006] A multi-UAV formation control method comprises the following steps:

[0007] Step 1: Use a weighted directed graph G = (U, E, B) to represent the communication topology between drones, and define the vertex set U = {1, 2, ..., n} of the graph. is the edge set of graph G, B={β ij} n×n is the adjacency matrix of graph G,

[0008] Then, the number of drones and the communication weights are initialized according to the communication topology, where n represents the number of drones in the formation, edge (i, j)∈E represents that drone i can receive information from neighbor j, and β ij represents the communication weight of edge (i, j), If edge (i, j)∈E, then β ij >0, otherwise β ij =0.

[0009] Step 2: Establish a formation control model. Considering the requirement of continuous flight of UAVs, i.e., second-order continuous differentiability, it is necessary to establish a second-order system model. Let η i (t) = [p i (t), v i (t)] T , H1=[1,0] T , H2=[0,1] T , the second-order system can be expressed as:

[0010]

[0011] Among them, p i =[x i ,y i ] T , v i =[v ix , v iy ] T , a i =[a ix , a iy ] T They represent the coordinates, velocity, and acceleration of the i-th UAV, respectively. However, the sending and receiving of the UAV posture information is discrete, so discretization is required;

[0012] Knowing the speed and acceleration of the drone at time t, we can get the speed of the drone at time t+1:

[0013]

[0014] Similarly, if the position and speed of the drone at time t are known, the position of the drone at time t+1 can be obtained:

[0015]

[0016] Among them, Δt represents the step length, that is, the time interval for updating the drone's posture information;

[0017] Step 3: Update the formation task, that is, the expected formation deviation D = [δ1, δ2, ..., δ n ], the expected position of the formation at time t p d (t), the expected speed v of the formation at time t d (t);

[0018] Among them, δ i =[δ ix , δ iy ] T represents the expected formation deviation of the i-th UAV, p d (t) = [x d (t), yd (t)] T , v d (t) = [v dx (t), v dy (t)] T ;

[0019] Then the expected position of the i-th physical drone at time t can be calculated:

[0020] p id (t) = p d (t)+δ i

[0021] Step 4: Update the position information of the drone at time t, including p i (t), v i (t), p j (t), v j (t);

[0022] Among them, {j∈U|β ij >0} indicates the drones adjacent to drone i;

[0023] Step 5: According to the updated target formation deviation and the position information of adjacent UAVs, the improved formation control law is used to calculate the optimal solution. The formation control law is:

[0024]

[0025] in, represents the time-varying parameter, β id is the formation task weight, and the control law can make |p i (t)-p d (t)|→δ i ,|v i (t)-v d (t)|→[0, 0] T , multiple drone groups can achieve stable formation flight;

[0026] When the kth UAV fails, the communication weight β ik It will be set to 0 and will not affect other drones completing the formation mission;

[0027] Step 6: If any drone fails or receives a new formation mission instruction, repeat steps 3 to 5; if the formation mission is completed, stop the operation, otherwise repeat steps 4 to 5 to continue iterating;

[0028] The second technical solution adopted by the present invention is as follows:

[0029] A multi-UAV formation control system, comprising a plurality of UAVs and a ground terminal, wherein each of the UAVs comprises a data communication unit, an airborne computing unit, a flight control unit and a positioning unit; and the ground terminal comprises a PC terminal and a data communication unit;

[0030] The multi-UAV formation control method and system are characterized in that the formation control method is deployed in the onboard computing unit of each UAV.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention defines the communication topology of the formation according to a directed weighted graph, designs a formation control law in combination with the expected formation deviation and the posture information of the UAVs, and deploys the formation control law on each UAV in the formation, so as to enable the UAVs to autonomously plan flight paths and achieve the purpose of improving the execution efficiency of the formation.

[0033] 2. The present invention can adaptively adjust the communication weight β according to the faulty drone ik , setting it to 0 will not affect other UAVs in completing the formation mission, thus achieving the purpose of improving the robustness of the formation.

[0034] 3. The method of the present invention is simple and easy to implement, and can significantly improve the mission execution efficiency and fault tolerance of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Flow chart of the multi-UAV formation control method of the present invention

[0036] Figure 2 Schematic diagram of the hardware architecture of the multi-UAV formation control system of the present invention

[0037] Figure 3 Schematic diagram of the software architecture of the multi-UAV formation control system of the present invention

[0038] Figure 4 The communication topology diagram of the embodiment of the present invention is

[0039] Figure 5 The formation flight posture diagram of the embodiment of the present invention is

[0040] Figure 6 This is a fault formation trajectory diagram of the UAV 1 according to an embodiment of the present invention. Specific implementation methods

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] like Figure 1As shown, an embodiment of the present invention provides a multi-UAV formation control method, comprising the following steps:

[0044] Step 1: Use a weighted directed graph G = (U, E, B) to represent the communication topology between drones, and define the vertex set U = {1, 2, ..., n} of the graph. is the edge set of graph G, B={β ij} n×n is the adjacency matrix of graph G,

[0045] Then, the number of drones and the communication weights are initialized according to the communication topology, where n represents the number of drones in the formation, edge (i, j)∈E represents that drone i can receive information from neighbor j, and β ij represents the communication weight of edge (i, j), If edge (i, j)∈E, then β ij >0, otherwise β ij =0.

[0046] Step 2: Establish a formation control model. Considering the requirement of continuous flight of UAVs, i.e., second-order continuous differentiability, it is necessary to establish a second-order system model. Let η i (t) = [p i (t), v i (t)] T , H1=[1,0] T , H2=[0,1] T , the second-order system can be expressed as:

[0047]

[0048] Among them, p i =[x i ,y i ] T , v i =[v ix ,v iy ] T , a i =[a ix , a iy ] T They represent the coordinates, velocity, and acceleration of the i-th UAV, respectively. However, the sending and receiving of the UAV posture information is discrete, so discretization is required;

[0049] Knowing the speed and acceleration of the drone at time t, we can get the speed of the drone at time t+1:

[0050]

[0051] Similarly, if the position and speed of the drone at time t are known, the position of the drone at time t+1 can be obtained:

[0052]

[0053] Among them, Δt represents the step length, that is, the time interval for updating the drone's posture information;

[0054] Step 3: Update the formation task, that is, the expected formation deviation D = [δ1, δ2, ..., δ n ], the expected position of the formation at time t p d (t), the expected speed v of the formation at time t d (t);

[0055] Among them, δ i =[δ ix , δ iy ] T represents the expected formation deviation of the i-th UAV, p d (t) = [x d (t), y d (t)] T , v d (t) = [v dx (t), v dy (t)] T ;

[0056] Then the expected position of the i-th physical drone at time t can be calculated:

[0057] p id (t) = p d (t)+δ i

[0058] Step 4: Update the position information of the drone at time t, including p i (t), v i (t), p j (t), v j (t);

[0059] Among them, {j∈U|β ij >0} indicates the drones adjacent to drone i;

[0060] Step 5: According to the updated target formation deviation and the position information of adjacent UAVs, the improved formation control law is used to calculate the optimal solution. The formation control law is:

[0061]

[0062] in, represents the time-varying parameter, β idis the formation task weight, and the control law can make |p i (t)-p d (t)|→δ i ,|v i (t)-v d (t)|→[0, 0] T , multiple drone groups can achieve stable formation flight;

[0063] When the kth UAV fails, the communication weight β ik It will be set to 0 and will not affect other drones completing the formation mission;

[0064] Step 6: If any drone fails or receives a new formation mission instruction, repeat steps 3 to 5; if the formation mission is completed, stop the operation, otherwise repeat steps 4 to 5 to continue iterating;

[0065] like Figures 2-3 As shown, an embodiment of the present invention further provides a multi-UAV formation control system, including several UAVs and a ground terminal, wherein the UAVs each include a data communication unit, an airborne computing unit, a flight control unit, and a positioning unit, and the ground terminal includes a PC terminal and a data communication unit;

[0066] The specific contents of the multi-UAV formation control system are:

[0067] The data communication units are used to establish connections between drones and between drones and the ground terminal. The data communication units are connected by two-way wireless connections to form a formation communication network.

[0068] The onboard computing units are all configured with Ubuntu system and installed with the robot operating system ROS, which is used to set up communication nodes, deploy formation control methods, and issue UAV control instructions; the flight control unit is used to drive the UAV to execute the corresponding operations of the control instructions; the positioning unit is connected to the flight control unit to obtain the UAV's position information;

[0069] There are bidirectional wired connections between the data communication unit and the airborne computing unit, and between the airborne computing unit and the flight control unit. The airborne computing unit needs to obtain the formation tasks issued by the ground end and the posture information of the adjacent UAVs through the data communication unit, and obtain the posture information of the current UAV through the flight control unit for formation control calculations. The airborne computing unit needs to convert the acquired posture information of the current UAV into ROS messages through Mavros, and send them to other nodes through the data communication unit. The airborne computing unit needs to convert the calculated expected formation posture into Mavlink protocol packets through Mavros; and control the UAV to perform related operations through the flight control unit.

[0070] Furthermore, the multi-UAV formation control method and system are characterized in that the formation control method is deployed on the onboard computing unit of each UAV.

[0071] Here is a more specific example:

[0072] like Figure 4 As shown in the figure, it is the communication topology structure of the formation in step 1 of the multi-UAV formation control method. It can be seen from the figure that the number of UAVs in the formation is 5; UAV 1 can receive information from its neighbor, namely UAV 2; UAV 2 can receive information from its neighbor, namely UAV 1; UAV 3 can receive information from its neighbor, namely UAV 2; UAV 4 can receive information from its neighbor, namely UAV 2; UAV 5 can receive information from its neighbor, namely UAV 4; in this embodiment, the communication weights are all set to β ij =1The initial parameters of each UAV are shown in Table 1.

[0073] Table 1. Initial parameters of each drone

[0074]

[0075] like Figure 5 As shown, the flight postures of the above-mentioned UAVs according to steps 2 to 6 of the multi-UAV formation control method, including a V-shaped straight line formation and a square curve formation. It can be seen from the figure that the formation control method provided by the present invention enables each UAV to form a desired formation, and the speed deviations in the x and y directions converge to 0.

[0076] like Figure 6 As shown in the figure, it is the flight trajectory of each drone when drone 1 fails. It can be seen from the figure that drone 1 fails at t=30s. According to step 5 of the multi-drone formation control method, the communication weight β 21 It will be set to 0 and will not affect other drones to continue to complete the formation mission.

[0077] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be included in the protection scope of the present invention.

Claims

1. A formation control method, characterized in that: The following steps are involved: Step 1: Use a weighted directed graph G = (U, E, B) to represent the communication topology between drones, and define the vertex set U = {1, 2, ..., n} of the graph. is the edge set of graph G, B={β ij } n×n is the adjacency matrix of graph G, Then, the number of drones and the communication weights are initialized according to the communication topology, where n represents the number of drones in the formation, edge (i, j)∈E represents that drone i can receive information from neighbor j, and β ij represents the communication weight of edge (i, j), If edge (i,j)∈E, then β ij >0, otherwise β ij =0. Step 2: Establish a formation control model. Considering the requirement of continuous flight of UAVs, i.e., second-order continuous differentiability, a second-order system model needs to be established. Let η i (t) = [p i (t), v i (t)] T , H1=[1,0] T , H2=[0,1] T , the second-order system can be expressed as: Among them, p i =[x i ,y i ] T , v i =[v ix ,v iy ] T , a i =[a ix , a iy ] T They represent the coordinates, velocity, and acceleration of the i-th UAV, respectively. However, the sending and receiving of the UAV posture information is discrete, so discretization is required; Knowing the speed and acceleration of the drone at time t, we can get the speed of the drone at time t+1: Similarly, if the position and speed of the drone at time t are known, the position of the drone at time t+1 can be obtained: Among them, Δt represents the step length, that is, the time interval for updating the drone's posture information; Step 3: Update the formation task, that is, the expected formation deviation D = [δ1, δ2, ..., δ n ], the expected position of the formation at time t p d (t), the expected speed v of the formation at time t d (t); Among them, δ i =[δ ix , δ iy ] T represents the expected formation deviation of the i-th UAV, p d (t) = [x d (t), y d (t)] T , v d (t) = [v dx (t), v dy (t)] T ; Then the expected position of the i-th physical drone at time t can be calculated: p id (t)=p d (t)+δ i Step 4: Update the position information of the drone at time t, including p i (t), v i (t), p j (t), v j (t); Among them, {j∈U|β ij >0} indicates the drones adjacent to drone i; Step 5: According to the updated target formation deviation and the position information of adjacent UAVs, the improved formation control law is used to calculate the optimal solution. The formation control law is: in, represents the time-varying parameter, β id is the formation task weight, and the control law can make |p i (t)-p d (t)|→δ i ,|v i (t)-v d (t)|→[0, 0] T , multiple drone groups can achieve stable formation flight; When the kth UAV fails, the communication weight β ik It will be set to 0 and will not affect other drones completing the formation mission; In step 6, if any UAV fails or receives a new formation mission instruction, steps 3 to 5 are repeated; if the formation mission is completed, the operation is stopped, otherwise steps 4 to 5 are repeated to continue the iteration.

2. A multi-UAV formation control system, characterized in that: It includes several unmanned aerial vehicles and a ground terminal. The unmanned aerial vehicles each include a data communication unit, an airborne computing unit, a flight control unit and a positioning unit; the ground terminal includes a PC terminal and a data communication unit.

3. The multi-UAV formation control method and system according to claim 1 or 2, characterized in that The formation control method is deployed on the onboard computing unit of each UAV.

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