Formation control method for unmanned aerial vehicle cluster

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a formation control method of an unmanned aerial vehicle cluster. According to the method, a flight parameter data set is constructed by reading fuselage data and position deviation data at each moment in the flight process of each unmanned aerial vehicle, the electric quantity of each unmanned aerial vehicle is obtained based on the flight parameter data set, and an electric quantity deviation coefficient is obtained through calculation; obtaining a deviation value between each unmanned aerial vehicle and a predicted position based on position deviation data in the flight parameter data set, obtaining a position deviation coefficient, carrying out comprehensive calculation to obtain a first risk coefficient of the unmanned aerial vehicle, and judging whether to execute a preliminary early warning instruction based on the first risk coefficient of the unmanned aerial vehicle; and finally, calculating a second risk coefficient of the unmanned aerial vehicle based on the flight disturbance coefficient of the unmanned aerial vehicle and the instruction error coefficient of the unmanned aerial vehicle, judging whether to execute a final early warning instruction, and regulating and controlling the unmanned aerial vehicle cluster formation, thereby judging the environment and the condition of the unmanned aerial vehicle, and reducing the falling and collision risks of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and in particular to a formation control method for a cluster of unmanned aerial vehicles. Background Art

[0002] Unmanned aerial vehicles are referred to as drones, and their English abbreviation is UAV. They are unmanned aircraft controlled by radio remote control equipment and self-contained program control devices. From a technical perspective, they can be divided into: unmanned helicopters, unmanned fixed-wing aircraft, unmanned multi-rotor aircraft, unmanned airships, unmanned paragliders, etc. UAVs are widely used in power inspection, crop monitoring, environmental monitoring, film and television shooting, street scene shooting, express delivery, post-disaster rescue, remote sensing mapping and other fields. UAV cluster collaboration has become an important development trend at present, with a larger control range, interactive complementation, task sharing, etc. The collaborative control of UAV cluster system can perform complex tasks, shorten the execution time, and improve attendance efficiency. However, in the process of executing tasks, the existing UAV cluster formation will have some problems, such as the position deviation of some sub-machines in the UAV formation cannot be directly fed back to the host operator, and different sub-machines will cause inconsistent power consumption when performing different position changes, which will lead to the risk of falling and collision in the control of UAV formation. Summary of the invention

[0003] 1. Technical issues to be solved

[0004] In view of the shortcomings of the prior art, the present invention provides a formation control method for a swarm of drones, which has the advantages of reducing the risks of falling and collision, and solves the above-mentioned technical problems.

[0005] (II) Technical solution

[0006] To achieve the above object, the present invention provides the following technical solution: a formation control method for a drone cluster, comprising the following steps:

[0007] S1: Collect the fuselage data and position deviation data of each drone in the current drone cluster at each moment during its flight to build a flight parameter data set;

[0008] S2: Based on the flight parameter data set, the power of each drone is obtained, and the power deviation coefficient DLPCXS is calculated. Based on the position deviation data in the flight parameter data set, the deviation value of each drone from the expected position is obtained to obtain the position deviation coefficient WZPCXS;

[0009] S3: Based on the power deviation coefficient DLPCXS and the position deviation coefficient WZPCXS, the first risk coefficient DYFX of the UAV is calculated, and based on the first risk coefficient DYFX of the UAV, it is determined whether to execute the preliminary warning instruction. The preliminary warning instruction is used to control the UAV cluster formation and determine whether to execute S4;

[0010] S4: Collect environmental data during the UAV flight process, build a UAV flight environment database, and obtain the UAV flight disturbance coefficient RDXS based on the UAV flight environment database, and obtain the UAV command error coefficient ZLWCXS based on the UAV flight environment database;

[0011] S5: Based on the UAV flight disturbance coefficient RDXS and the UAV command error coefficient ZLWCXS, the UAV second risk coefficient DEFX is calculated comprehensively, and based on the UAV second risk coefficient DEFX, it is determined whether to execute the final warning command and regulate the UAV cluster formation.

[0012] As a preferred technical solution of the present invention, the specific steps of obtaining the power of each UAV based on the flight parameter data set and calculating the power deviation coefficient DLPCXS in S2 are as follows:

[0013] S2a.1: Get the power of each drone. The specific expression is as follows:

[0014] DL=[DL t，1 ,…,DL t,a ,…,DL t,A ]

[0015] Among them, DL represents the power data set in the UAV cluster formation, which is obtained based on the flight parameter data set. t,1 ,…,DL t,a ,…,DL t，A They represent the power of the first UAV at time t, ..., the power of the a-th UAV at time t, ..., the power of the A-th UAV at time t in the UAV cluster formation respectively;

[0016] S2a.2: Calculate the power deviation coefficient DLPCXS. The specific expression is as follows:

[0017]

[0018] Among them, f(DL t,a -DL t,0 ) indicates DL t,a -DL t,0 The judgment function, DL t,0 represents the theoretical remaining power of the drone at time t, DL t,a represents the power of the a-th drone at time t, represents the average power fluctuation value, based on the fuselage data of each drone at each moment during the flight process in the flight parameter dataset, and x represents DL t,a -DL t,0 The judgment function f(DL t,a -DL t,0 ) outputs the number of 0 values, Indicates f(DL t,a -DL t,0 ) output values ​​are summed.

[0019] As a preferred technical solution of the present invention, the average power fluctuation value The expression for obtaining is as follows

[0020]

[0021] Among them, DL 0，0 Indicates the initial charge, DL t,a represents the battery power of the ath drone at time t, DL t-1，a represents the power of the a-th drone at time t-1, Express Perform the summation.

[0022] As a preferred technical solution of the present invention, the judgment function f(DL t,a -DL t,0 ) is as follows:

[0023]

[0024] Among them, f(DL t,a -DL t,0 ) indicates DL t,a -DL t,0 The judgment function, DL t,0 represents the theoretical remaining power of the drone at time t, DL t,a represents the power of the a-th drone at time t, |DL t，a -DL t,0 | indicates DL t，a -DL t,0 The absolute value of .

[0025] As a preferred technical solution of the present invention, the deviation value of each UAV from the expected position is obtained based on the position deviation data in the flight parameter data set in S2, and the specific expression of the position deviation coefficient WZPCXS is obtained as follows:

[0026]

[0027] Among them, WZ t,arepresents the position of the a-th UAV at time t, WZ t,a，0 represents the theoretical position of the a-th drone at time t, obtained based on the drone's built-in positioning module, max a∈[1，A] {WZ t,a -WZ t,a，0} indicates that 1 to A drones are based on WZ t,a -WZ t,a，0 The calculated maximum value.

[0028] As a preferred technical solution of the present invention, the specific expression of the first risk coefficient DYFX of the drone obtained by comprehensive calculation based on the power deviation coefficient DLPCXS and the position deviation coefficient WZPCXS in S3 is as follows:

[0029] DYFX=α*DLPCXS+β*WZPCXS

[0030] Among them, α and β represent weight coefficients whose sum is 1, DLPCXS represents the power deviation coefficient, and WZPCXS represents the position deviation coefficient.

[0031] As a preferred technical solution of the present invention, the specific steps of the preliminary warning instruction in S3 are as follows:

[0032] When the first risk factor of drone DYFX < the first critical value of flight risk FXLJ 1 When , S4 is not executed, and the UAV cluster formation is not regulated;

[0033] When the flight risk first critical value FXLJ 1 ≤ UAV first risk coefficient DYFX < flight risk second critical value FXLJ 2 When , execute step S4;

[0034] When the flight risk second critical value FXLJ 2 When the drone’s first risk factor DYFX is less than or equal to the first risk factor of the drone, the drone cluster returns.

[0035] As a preferred technical solution of the present invention, the specific expression of S4 for obtaining the UAV flight disturbance coefficient RDXS based on the UAV flight environment database is as follows:

[0036]

[0037] in, XS i =1 indicates that the i-th type of event occurs in the UAV flight area, XS i =0 means that no event of type i has occurred in the UAV flight area, I represents all events that affect the flight of UAVs, and e represents a natural constant;

[0038] The specific expression for obtaining the UAV command error coefficient ZLWCXS based on the UAV flight environment database is as follows:

[0039]

[0040] in, Indicates the right |T t，a -T 0，a |To sum, |T t，a -T 0，a | indicates T t，a -T 0，a The absolute value of .

[0041] As a preferred technical solution of the present invention, the specific expression of the drone second risk coefficient DEFX obtained by S5 based on the drone flight disturbance coefficient RDXS and the drone command error coefficient ZLWCXS is as follows:

[0042] DEFX=γ*RDXS+δ*ZLWCXS

[0043] Among them, RDXS represents the UAV flight disturbance coefficient, ZLWCXS represents the UAV command error coefficient, and γ and δ represent the weight coefficients whose sum is 1.

[0044] As a preferred technical solution of the present invention, the specific steps of determining whether to execute the final warning instruction based on the second risk coefficient DEFX of the drone and regulating the drone cluster formation in S5 are as follows:

[0045] When the second risk factor of drone DEFX is less than the first critical value of environmental risk HJLJ 1 When the drone cluster formation is not adjusted;

[0046] When the environmental risk first critical value HJLJ 1 ≤ Drone second risk factor DEFX<environmental risk second critical value HJLJ 2 When the flight is over, a return request is sent to the management staff;

[0047] When the environmental risk second critical value HJLJ 2 When the drone’s second risk factor DEFX is less than or equal to the drone’s second risk factor DEFX, the drone cluster will return.

[0048] Compared with the prior art, the present invention provides a formation control method for a drone cluster, which has the following beneficial effects:

[0049] The present invention constructs a flight parameter data set by collecting the fuselage data and position deviation data of each drone currently in the drone cluster at each moment during the flight process, and obtains the power of each drone based on the flight parameter data set, and calculates the power deviation coefficient, obtains the deviation value of each drone from the expected position based on the position deviation data in the flight parameter data set, obtains the position deviation coefficient, comprehensively calculates the first risk coefficient of the drone, and judges whether to execute the preliminary warning instruction based on the first risk coefficient of the drone, finally calculates the second risk coefficient of the drone based on the flight disturbance coefficient of the drone and the drone instruction error coefficient, and judges whether to execute the final warning instruction based on the second risk coefficient of the drone, regulates the drone cluster formation, thereby judging the environment and the situation of the drone, and reducing the risk of the drone falling and collision. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 , a formation control method for a drone cluster, comprising the following steps:

[0053] S1: Collect the fuselage data and position deviation data of each drone in the current drone cluster at each moment during its flight to build a flight parameter data set;

[0054] S2: Based on the flight parameter data set, the power of each drone is obtained, and the power deviation coefficient DLPCXS is calculated. Based on the position deviation data in the flight parameter data set, the deviation value of each drone from the expected position is obtained to obtain the position deviation coefficient WZPCXS;

[0055] In S2, the specific steps of obtaining the power of each drone based on the flight parameter data set and calculating the power deviation coefficient DLPCXS are as follows:

[0056] S2a.1: Get the power of each drone. The specific expression is as follows:

[0057] DL=[DL t,1 ,…,DL t,a ,…,DL t,A ]

[0058] Among them, DL represents the power data set in the UAV cluster formation, which is obtained based on the flight parameter data set. t,1 ,…,DL t,a ,…,DL t，A They represent the power of the first UAV at time t, ..., the power of the a-th UAV at time t, ..., the power of the A-th UAV at time t in the UAV cluster formation respectively;

[0059] S2a.2: Calculate the power deviation coefficient DLPCXS. The specific expression is as follows:

[0060]

[0061] Among them, f(DL t,a -DL t,0 ) indicates DL t,a -DL t,0 The judgment function, DL t,0 represents the theoretical remaining power of the drone at time t, DL t,a represents the power of the a-th drone at time t, represents the average power fluctuation value, based on the fuselage data of each drone at each moment during the flight process in the flight parameter dataset, and x represents DL t,a -DL t,0 The judgment function f(DL t,a -DL t,0 ) outputs the number of 0 values, Indicates f(DL t,a -DL t,0 ) output values ​​are summed to obtain the average power fluctuation value The expression for obtaining is as follows:

[0062]

[0063] Among them, DL 0，0 Indicates the initial charge, DL t,a represents the battery power of the ath drone at time t, DL t-1，a represents the power of the a-th drone at time t-1, Express Sum and determine the function f(DL t,a -DL t,0 ) is as follows:

[0064]

[0065] Among them, f(DL t,a -DL t,0 ) indicates DL t,a-DL t,0 The judgment function, DL t,0 represents the theoretical remaining power of the drone at time t, DL t,a represents the power of the a-th drone at time t, |DL t，a -DL t,0 | indicates DL t,a -DL t,0 The absolute value of the position deviation data in the flight parameter data set is used in S2 to obtain the deviation value of each UAV from the expected position, and the specific expression of the position deviation coefficient WZPCXS is as follows:

[0066]

[0067] Among them, WZ t,a represents the position of the a-th UAV at time t, WZ t,a，0 represents the theoretical position of the a-th drone at time t, obtained based on the drone's built-in positioning module, max a∈[1,A] {WZ t,a -WZ t,a，0} indicates that 1 to A drones are based on WZ t,a -WZ t,a，0 The calculated maximum value;

[0068] S3: Based on the power deviation coefficient DLPCXS and the position deviation coefficient WZPCXS, the first risk coefficient DYFX of the drone is calculated, and based on the first risk coefficient DYFX of the drone, it is determined whether to execute the preliminary warning instruction. The preliminary warning instruction is used to control the drone cluster formation and determine whether to execute S4. The specific expression of the first risk coefficient DYFX of the drone obtained by comprehensive calculation based on the power deviation coefficient DLPCXS and the position deviation coefficient WZPCXS in S3 is as follows:

[0069] DYFX=α*DLPCXS+β*WZPCXS

[0070] Among them, α and β represent weight coefficients whose sum is 1, DLPCXS represents the power deviation coefficient, and WZPCXS represents the position deviation coefficient. The specific steps of the preliminary warning instruction in S3 are as follows:

[0071] When the first risk factor of drone DYFX < the first critical value of flight risk FXLJ 1 When , S4 is not executed and the drone cluster formation is not regulated, which means that the drone flight risk in the current environment is low, that is, there is a low power deviation and position deviation;

[0072] When the flight risk first critical value FXLJ 1 ≤ UAV first risk coefficient DYFX < flight risk second critical value FXLJ2 When , step S4 is executed, indicating that there is a flight risk of the drone in the current environment, that is, there is a certain power deviation and position deviation, which may cause the drone to be at risk in a harsh environment;

[0073] When the flight risk second critical value FXLJ 2 When the drone cluster returns home when the value is less than or equal to the first risk factor DYFX, it means that the drone has a high flight risk in the current environment, that is, there is a high power deviation and position deviation, and there is a possibility of collision or insufficient endurance.

[0074] S4: Collect environmental data during the UAV flight process, build a UAV flight environment database, and obtain the UAV flight disturbance coefficient RDXS based on the UAV flight environment database, and obtain the UAV command error coefficient ZLWCXS based on the UAV flight environment database. The specific expression of S4 obtaining the UAV flight disturbance coefficient RDXS based on the UAV flight environment database is as follows

[0075]

[0076] in, XS i =1 indicates that the i-th type of event occurs in the UAV flight area, XS i =0 indicates that the i-th type of event does not occur in the UAV flight area, I indicates all events that affect the flight of the UAV, and e indicates a natural constant. In the present invention, the events that affect the flight of the UAV include: wind shear, turbulent airflow, strong winds, and bird gathering;

[0077] The specific expression of the UAV command error coefficient ZLWCXS based on the UAV flight environment database is as follows:

[0078]

[0079] in, Indicates the right |T t，a -T 0，a |To sum, |T t，a -T 0，a | indicates T t，a -T 0，a The absolute value of

[0080] S5: Based on the UAV flight disturbance coefficient RDXS and the UAV command error coefficient ZLWCXS, the second risk coefficient DEFX of the UAV is calculated, and based on the second risk coefficient DEFX of the UAV, it is determined whether to execute the final warning command and adjust the UAV cluster formation. The specific expression of the second risk coefficient DEFX of the UAV obtained by S5 based on the comprehensive calculation of the UAV flight disturbance coefficient RDXS and the UAV command error coefficient ZLWCXS is as follows:

[0081] DEFX=γ*RDXS+δ*ZLWCXS

[0082] Among them, RDXS represents the UAV flight disturbance coefficient, ZLWCXS represents the UAV command error coefficient, γ and δ represent weight coefficients whose sum is 1, and S5 determines whether to execute the final warning command based on the second risk coefficient DEFX of the UAV. The specific steps for regulating the UAV cluster formation are as follows:

[0083] When the second risk factor of drone DEFX is less than the first critical value of environmental risk HJLJ 1 When the drone cluster formation is not adjusted, it means that the current drone is under the first risk judgment, and there is or only a low risk situation in the flight environment, which does not have much impact on the drone's mission completion;

[0084] When the environmental risk first critical value HJLJ 1 ≤ Drone second risk factor DEFX<environmental risk second critical value HJLJ 2 When the drone is detected, a return request is sent to the manager, indicating that under the first risk judgment, there are environmental risks related to flight in the flight environment, and the manager of the control host is required to make a return judgment;

[0085] When the environmental risk second critical value HJLJ 2 ≤ the second risk factor DEFX of the drone, the drone cluster returns, indicating that under the current drone first risk judgment, there is a high environmental risk related to flight in the flight environment, and the drone is directly judged to return, and the return information is sent to the management personnel.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A formation control method for a drone swarm, characterized in that: The following steps are involved: S1: Collect the fuselage data and position deviation data of each drone in the current drone cluster at each moment during its flight to build a flight parameter data set; S2: Based on the flight parameter data set, the power of each drone is obtained, and the power deviation coefficient DLPCXS is calculated. Based on the position deviation data in the flight parameter data set, the deviation value of each drone from the expected position is obtained to obtain the position deviation coefficient WZPCXS; S3: Based on the power deviation coefficient DLPCXS and the position deviation coefficient WZPCXS, the first risk coefficient DYFX of the UAV is calculated, and based on the first risk coefficient DYFX of the UAV, it is determined whether to execute the preliminary warning instruction. The preliminary warning instruction is used to control the UAV cluster formation and determine whether to execute S4; S4: Collect environmental data during the UAV flight process, build a UAV flight environment database, and obtain the UAV flight disturbance coefficient RDXS based on the UAV flight environment database, and obtain the UAV command error coefficient ZLWCXS based on the UAV flight environment database; S5: Based on the UAV flight disturbance coefficient RDXS and the UAV command error coefficient ZLWCXS, the UAV second risk coefficient DEFX is calculated comprehensively, and based on the UAV second risk coefficient DEFX, it is determined whether to execute the final warning command and regulate the UAV cluster formation.

2. A formation control method for a drone swarm according to claim 1, characterized in that: The specific steps of obtaining the power of each UAV based on the flight parameter data set and calculating the power deviation coefficient DLPCXS in S2 are as follows: S2a.1: Get the power of each drone. The specific expression is as follows: DL=[DL t,1 ,…,DL t,a ,…,DL t,A ] Among them, DL represents the power data set in the UAV cluster formation, which is obtained based on the flight parameter data set. t,1 ,…,DL t,a ,…,DL t,A They represent the power of the first UAV at time t, ..., the power of the a-th UAV at time t, ..., the power of the A-th UAV at time t in the UAV cluster formation respectively; S2a.2: Calculate the power deviation coefficient DLPCXS. The specific expression is as follows: Among them, f(DL t,a -DL t,0 ) indicates DL t,a -DL t,0 The judgment function, DL t,0 represents the theoretical remaining power of the drone at time t, DL t,a represents the power of the a-th drone at time t, represents the average power fluctuation value, based on the fuselage data of each drone at each moment during the flight process in the flight parameter dataset, and x represents DL t,a -DL t,0 The judgment function f(DL t,a -DL t,0 ) outputs the number of 0 values, Indicates f(DL t,a -DL t,0 ) output values ​​are summed.

3. The formation control method of a drone cluster according to claim 2, characterized in that: The average power fluctuation value The expression for obtaining is as follows: Among them, DL 0,0 Indicates the initial charge, DL t,a represents the battery power of the ath drone at time t, DL t-1,a represents the power of the a-th drone at time t-1, Express Perform the summation.

4. The formation control method of a drone cluster according to claim 2, characterized in that: The judgment function f(DL t,a -DL t,0 ) is as follows: Among them, f(DL t,a -DL t,0 ) indicates DL t,a -DL t,0 The judgment function, DL t,0 represents the theoretical remaining power of the drone at time t, DL t,a represents the power of the a-th drone at time t, |DL t,a -DL t,0 | indicates DL t,a -DL t,0 The absolute value of .

5. The formation control method of a drone cluster according to claim 1, characterized in that: In S2, the deviation value of each UAV from the expected position is obtained based on the position deviation data in the flight parameter data set, and the specific expression of the position deviation coefficient WZPCXS is obtained as follows: Among them, WZ t,a represents the position of the a-th UAV at time t, WZ t,a,0 represents the theoretical position of the a-th drone at time t, obtained based on the drone's built-in positioning module, max a∈[1,A] {WZ t,a -WZ t,a,0 } indicates that 1 to A drones are based on WZ t,a -WZ t,a,0 The calculated maximum value.

6. The formation control method of a drone swarm according to claim 1, characterized in that: The specific expression of the first risk coefficient DYFX of the drone obtained by comprehensive calculation based on the power deviation coefficient DLPCXS and the position deviation coefficient WZPCXS in S3 is as follows: DYFX=α*DLPCXS+β*WZPCXS Among them, α and β represent weight coefficients whose sum is 1, DLPCXS represents the power deviation coefficient, and WZPCXS represents the position deviation coefficient.

7. The formation control method of a drone cluster according to claim 1, characterized in that: The specific steps of the preliminary warning instruction in S3 are as follows: When the first risk coefficient DYFX of the UAV is less than the first critical value FXLJ1 of the flight risk, S4 is not executed and the UAV cluster formation is not regulated; When the first critical value of flight risk FXLJ1≤the first risk coefficient of drone DYFX<the second critical value of flight risk FXLJ2, execute step S4; When the second critical value of flight risk FXLJ2 ≤ the first risk coefficient DYFX of the drone, the drone cluster returns.

8. The formation control method of a drone cluster according to claim 1, characterized in that: The specific expression of S4 for obtaining the UAV flight disturbance coefficient RDXS based on the UAV flight environment database is as follows: in, XS i =1 indicates that the i-th type of event occurs in the UAV flight area, XS i =0 means that no event of type i has occurred in the UAV flight area, I represents all events that affect the flight of UAVs, and e represents a natural constant; The specific expression for obtaining the UAV command error coefficient ZLWCXS based on the UAV flight environment database is as follows: in, Indicates the right |T t,a -T 0,a |To sum, |T t,a -T 0,a | indicates T t,a -T 0,a The absolute value of .

9. The formation control method of a drone cluster according to claim 1, characterized in that: The specific expression of the drone second risk coefficient DEFX obtained by S5 based on the drone flight disturbance coefficient RDXS and the drone command error coefficient ZLWCXS is as follows: DEFX=γ*RDXS+δ*ZLWCXS Among them, RDXS represents the UAV flight disturbance coefficient, ZLWCXS represents the UAV command error coefficient, and γ and δ represent the weight coefficients whose sum is 1.

10. The formation control method of a drone cluster according to claim 1, characterized in that: The specific steps of S5 for determining whether to execute the final warning instruction based on the second risk coefficient DEFX of the drone and regulating the drone cluster formation are as follows: When the second risk coefficient of drone DEFX is less than the first critical value of environmental risk HJLJ1, the drone cluster formation will not be regulated; When the first critical value of environmental risk HJLJ1 ≤ the second risk coefficient of drone DEFX < the second critical value of environmental risk HJLJ2, a return request is issued to the management personnel; When the second critical value of environmental risk HJLJ2 ≤ the second risk coefficient DEFX of the drone, the drone cluster returns.

Images