A rotor unmanned aerial vehicle operation safety interval calibration method based on collision risk

Abstract

The application discloses a rotor unmanned aerial vehicle operation safety interval calibration method based on collision risk, comprising the following steps: based on rotor unmanned aerial vehicle shape size data and operation attitude limitation, a collision judgment model of any two unmanned aerial vehicles is constructed; through set rotor unmanned aerial vehicle track error distribution and air route configuration, a real-time collision risk calculation model of the rotor unmanned aerial vehicle is constructed; based on the real-time collision risk calculation model of the rotor unmanned aerial vehicle, a system safety level model is constructed, and the system safety level model is solved based on a preset system safety level target value constraint condition, so that the relative position of any two unmanned aerial vehicles under the safety level is obtained, and the rotor unmanned aerial vehicle operation safety interval is calibrated. The instantaneous collision risk of the rotor unmanned aerial vehicle can be calculated in real time, so that the safety interval of the rotor unmanned aerial vehicle in the structured air route is calibrated, and support is provided for verifying rationalization of the structured air route design.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) operation safety and airway network planning, specifically to a method for calibrating safe operating intervals for rotary-wing UAVs based on collision risk. Background Technology

[0002] The operational safety interval for unmanned aerial vehicles (UAVs) is defined for structured flight paths in urban low-altitude environments. Maintaining a safe distance between UAVs along pre-defined routes ensures safe and efficient operation, thereby achieving a certain level of safety for the operational system. The safety interval calibration results can also provide verification support for the rationality of UAV flight path network delineation. UAV safety interval calibration can generally be transformed into an operational conflict detection calculation problem. By setting up conflict encounter scenarios for UAVs, the probability or risk of conflict between two UAVs is calculated, and then the corresponding safety level is set, ultimately calibrating the interval.

[0003] In recent years, the main method for calibrating safe distances between drones has been to calculate the collision probability between two drones. This involves setting the conflict scenario and probability error distribution between the two drones, and then calculating the collision probability through integration. However, due to the non-additive nature of collision probabilities, this method cannot assess the overall conflict risk of a particular operating system.

[0004] Furthermore, the current safety standard for drones is "equivalent safety level," meaning that the operational safety level of drones must not be lower than that of traditional civil aircraft, i.e., 10. -7 Number of collisions per flight hour. There is currently no method for calibrating safety intervals based on the "equivalent safety level" of drones.

[0005] Furthermore, urban low-altitude airway networks under logistics mission conditions present higher safety requirements for drone operations compared to high-altitude airspace environments, due to denser obstacles, higher population density, and more complex low-altitude environments. Secondly, compared to civil aircraft, drones have shorter ranges and slower speeds, meaning that collision risk calculation methods cannot be directly applied to the traditional civil aviation field. Summary of the Invention

[0006] Therefore, considering the limitations of complex low-altitude environments and the safety requirements of UAV operations, a collision risk-based method for calibrating safe operating intervals for rotorcraft UAVs is proposed, which can provide safety assurance for UAV operations. Furthermore, it supports the verification of the rationality of UAV route network delineation, ultimately integrating UAV safety interval standards into the civil aviation safety standard system.

[0007] This invention proposes a method for calibrating the safe operating interval of rotary-wing UAVs based on collision risk. The method calculates and accumulates the collision risk of any two UAVs in a set conflict scenario in real time, and then calibrates the safe operating interval of the UAVs by setting the system safety level.

[0008] A method for calibrating safe operating intervals for rotary-wing unmanned aerial vehicles based on collision risk includes the following steps:

[0009] Step 1: Based on the shape and size data and operating attitude constraints of the rotorcraft UAV, construct a collision protection zone model for the rotorcraft UAV, and construct a collision judgment model for any two UAVs based on the rotorcraft UAV collision protection zone model;

[0010] Step 2: Based on the collision judgment model of any two UAVs, construct a real-time collision risk calculation model for rotary-wing UAVs by setting the trajectory error distribution and flight path configuration of the rotary-wing UAVs;

[0011] Step 3: Based on the real-time collision risk calculation model of the rotorcraft UAV, construct a system safety level model, and solve the system safety level model based on the preset system safety level target value constraint to obtain the relative position Δp of any two UAVs under the safety level.

[0012] Step 4: Based on the relative position Δp of any two UAVs at the system level, calibrate the safe operating interval of the rotary-wing UAVs.

[0013] In some embodiments, step 1 involves constructing a collision protection zone model for the rotorcraft UAV based on its shape and size data and operational attitude constraints, including:

[0014] Step 1.1: Construct the minimum circumscribed cylinder of the rotorcraft UAV based on its maximum radius r and total height h; based on the minimum circumscribed cylinder and considering the operating attitude constraints of the rotorcraft UAV, establish the circumscribed ellipsoid of the minimum circumscribed cylinder with dimensions (R, H); R and H are the horizontal radius and total height of the circumscribed ellipsoid, respectively; use the circumscribed ellipsoid as the collision protection zone for the rotorcraft UAV.

[0015] Furthermore, the operating attitude limitations of the rotorcraft UAV include the maximum pitch angle θ. max With maximum roll angle

[0016] In some embodiments, step 1, constructing a collision determination model for any two drones based on the rotorcraft drone collision protection zone model, includes:

[0017] Step 1.2: For any two drones, one being a relative drone and the other a random drone, combine (unite) the collision protection zones of the two drones to obtain a combined collision protection zone region D; based on the relative positions of the two drones and the combined region D, construct a collision determination model for the two drones:

[0018]

[0019] Among them, I D Δp indicates whether a collision has occurred, and Δp represents the relative position of any two drones.

[0020] In some embodiments, step 2 includes:

[0021] Step 2.1: By setting the flight path configuration of the rotary-wing UAV, obtain the heading angle ψ and pitch angle θ of the UAV, and obtain the transformation matrix M between the global coordinate system and the body coordinate system based on the heading angle ψ and pitch angle θ;

[0022] Step 2.2: Obtain the initial nominal position of the UAV By setting the trajectory error of the rotary-wing drone Obtain the nominal position mean The flight path error of the UAV follows a Gaussian distribution along the aircraft's coordinate axes. Where the diagonal matrix diag is the symbol for a diagonal matrix. These represent the track errors in the x, y, and z directions, respectively.

[0023] Based on the transformation matrix M and the nominal position mean The global position distribution of the UAV is obtained as p ~ N3;

[0024]

[0025] Among them, MΛM T The covariance of the transformed probability distribution;

[0026] Step 2.3: Based on the global position distribution of the two drones, calculate the relative positions of the two drones: The relative position probability distribution Δp ~ N3 of the two drones is obtained:

[0027]

[0028] Where p S p O These are the position probability distributions of random drones and relative drones, respectively. M represents the trajectory error of a random UAV and a relative UAV, respectively. S M OThe transformation matrices for random UAVs and relative UAVs, respectively, Λ S Λ O These are the diagonal matrices for random drones and relative drones, respectively;

[0029] Step 2.4: Obtain the initial position p0 = [x0 y0 z0] relative to the UAV. T This leads to the subsequent relative position with respect to the initial position of the drone as the origin: p O (t) = [x - x0 y - y0 z - z0] T ;

[0030] Calculate the combined collision protection zone D for the two drones:

[0031]

[0032] Among them, R f H f Let be the radius and total height of the collision protection zone D, respectively, and let A be the size matrix of the collision protection zone D.

[0033] Step 2.5: Calculate the real-time collision probability P(kT) between the two drones. s The calculation formula is:

[0034]

[0035] Among them, kT s For different time periods, Δp(kT) s ( ) represents the relative positions of the two drones. M represents the average initial relative position of the two drones in each stage. S Λ S (kT s M S T +M O Λ O (kT s M O T The relative position covariance of the two drones in each stage;

[0036] Step 2.6: Obtain the velocity vector projection v' = [v'] of the random UAV in the direction relative to the UAV. x ,v' y ,v' z Then, the collision ratio R(O) during the collision between the two drones is calculated using the following formula:

[0037]

[0038] Where R(O) is the collision relationship ratio, Rf H f These represent the radius and total height of the collision protection zone D, respectively.

[0039] Step 2.7: Calculate the collision risk between the two drones. Based on different directions, the collision risk is divided into: longitudinal collision risk F x Lateral collision risk F y Vertical collision risk F z The formulas are as follows:

[0040]

[0041]

[0042]

[0043] The total collision risk for a single drone pair is:

[0044] F(t)=F x (t)+F y (t)+F z (t)

[0045] Where t represents the running time, E(0) is the proximity rate, representing the frequency of loss intervals between any two drones per unit time, P(O) is the collision probability, R(O) is the collision ratio, and the final collision risk calculation result is: the frequency of collisions occurring per unit time; when the relative velocity projection v' of two drones in a certain direction... i When (i = x, y, z) is 0, the collision risk in that direction is 0.

[0046] Furthermore, in some embodiments, the flight path configuration of the rotary-wing UAV includes: a single flight path, a parallel flight path, and a vertically parallel flight path.

[0047] In some embodiments, E(0) is 0.01.

[0048] In some embodiments, step 3 includes:

[0049] Step 3.1: System security level F total (t) The collision risks of any two of the n rotary-wing UAVs in the operating system are accumulated, and the calculation formula is as follows:

[0050]

[0051] F total (t)≤F max (t)

[0052] Where F max (t) represents the preset target value for the system security level;

[0053] Step 3.2: Based on the preset system security level target value F max Solve the constraints (t) to obtain the relative position Δp of any two UAVs under the safety level, and use Δp to calibrate the safe operating interval of the UAVs.

[0054] In some embodiments, the system security level target value F max (t) Equivalent safety level for using drones: 10- 7 The number of collisions per flight hour, i.e., the number of system collisions per hour, must not exceed 10. -7 Second-rate.

[0055] In a second aspect, the present invention provides a collision risk-based operating safety interval calibration device for rotary-wing unmanned aerial vehicles, including a processor and a storage medium;

[0056] The storage medium is used to store instructions;

[0057] The processor is configured to operate according to the instructions to perform the steps of the method according to the first aspect.

[0058] Thirdly, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0059] Compared with existing technologies, the advantages of this invention are as follows: This invention provides a collision risk-based method for calibrating safe operating intervals for rotary-wing UAVs. First, based on the shape, size, and operational attitude limitations of the rotary-wing UAV, a three-dimensional model of its collision protection zone is created. Then, by setting the trajectory error distribution and route configuration of the UAV, the real-time collision risk of the UAV is calculated using an Event model. Finally, based on the total collision risk of the operating system and the set operational safety level, the safe operating interval of the UAV is calibrated. This invention proposes a method for calibrating safe operating intervals for rotary-wing UAVs based on UAV operational safety level standards. This method can calculate the instantaneous collision risk of rotary-wing UAVs in real time, thereby calibrating their safe operating intervals in structured routes and providing support for verifying the rationality of structured route design. Using the collision frequency per unit time as the risk calculation result is consistent with existing UAV operational safety levels. Furthermore, this invention can calculate the collision risk value of an operating system with multiple UAVs, thereby calibrating the intervals of the entire route structure network, and the calibration results can be integrated into the existing civil aviation safety standard system. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the overall implementation of an embodiment of the present invention.

[0061] Figure 2 This is a schematic diagram of the collision protection zone for a rotary-wing unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0062] Figure 3 This is a schematic diagram of the combined collision protection zone of a rotary-wing unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0063] Figure 4 This is a schematic diagram of the route structure used for safety interval calibration according to an embodiment of the present invention.

[0064] Figure 5 This is a schematic diagram of the safe interval calibration of a drone according to an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings.

[0066] Example 1

[0067] A method for calibrating safe operating intervals for rotary-wing unmanned aerial vehicles based on collision risk, comprising:

[0068] Step 1: Based on the shape and size data and operating attitude constraints of the rotorcraft UAV, construct a collision protection zone model for the rotorcraft UAV, and construct a collision judgment model for any two UAVs based on the rotorcraft UAV collision protection zone model;

[0069] Step 2: Based on the collision judgment model of any two UAVs, construct a real-time collision risk calculation model for rotary-wing UAVs by setting the trajectory error distribution and flight path configuration of the rotary-wing UAVs;

[0070] Step 3: Based on the real-time collision risk calculation model of the rotorcraft UAV, construct a system safety level model, and solve the system safety level model based on the preset system safety level target value constraint to obtain the relative position Δp of any two UAVs under the safety level.

[0071] Step 4: Based on the relative position Δp of any two UAVs at the system level, calibrate the safe operating interval of the rotary-wing UAVs.

[0072] In some embodiments, a collision risk-based method for calibrating safe operating intervals for rotary-wing UAVs, such as... Figure 1 As shown: The specific steps are as follows:

[0073] Step 1: Perform a 3D model of the shape and size of the rotorcraft drone and construct a 3D collision protection zone for the rotorcraft drone.

[0074] Step 1.1: Obtain the maximum radius and total height (r, h) of the rotorcraft UAV, construct the minimum circumscribed cylinder of the rotorcraft UAV, and based on this, consider its attitude changes: maximum pitch angle and maximum roll angle. Construct a circumscribed ellipsoid of the minimum circumscribed cylinder, with dimensions (R, H), where R and H are the horizontal radius and total height of the circumscribed ellipsoid, respectively; use this circumscribed ellipsoid as the collision protection zone for the rotary-wing UAV. For example... Figure 2 As shown, the dimensional relationships are derived as follows:

[0075]

[0076]

[0077] Among them, V em The empty space between the two shapes.

[0078] Step 1.2: Set up any two drones, one as a relative drone and the other as a random drone, denoted by subscripts O and S respectively. Combine the collision protection zones of the two drones into a combined collision protection zone region D, with a size of D. like Figure 3 As shown. A collision detection model for the two drones is constructed:

[0079]

[0080] Among them, I D Δp indicates whether a collision has occurred, and Δp represents the relative position of any two drones.

[0081] Step 2: By setting the route configuration and track error, the improved Event model is used to calculate the collision risk of the UAV in real time.

[0082] Step 2.1: By setting different flight path configurations, including: single flight path, horizontal parallel flight path, and vertical parallel flight path, the heading angle ψ and pitch angle θ of the UAV are obtained, and then the transformation matrix between the global coordinate system and the body coordinate system is obtained:

[0083]

[0084] Step 2.2: Obtain the initial nominal position of the UAV By setting the trajectory error of the rotary-wing drone Obtain the nominal position mean In this embodiment, the UAV's trajectory error is set to follow a Gaussian distribution along the aircraft's coordinate axes. Where the diagonal matrix diag is the symbol for a diagonal matrix. These are the track errors in the x, y, and z directions, respectively, and their calculation formulas are as follows:

[0085]

[0086] Where t is the flight time, t a With t b The system delay is set to 1 second. a and b represent the positioning accuracy in the horizontal and vertical directions, in meters (m). Corresponding upper limits are set; in this embodiment, the upper limits are set to (2m, 2m, 3.2m). Based on the transformation matrix M and... The global position distribution of the UAV is obtained as p ~ N3;

[0087]

[0088] Where p represents the probability distribution of the drone's location. MΛM is the nominal position mean. T Let be the covariance of the transformed probability distribution.

[0089] Step 2.3: Based on the global position distribution of the two drones, calculate the relative positions of the two drones: The relative position probability distribution Δp ~ N3 of the two drones is obtained:

[0090]

[0091] Where p S p O These are the position probability distributions of random drones and relative drones, respectively. M represents the trajectory error of a random UAV and a relative UAV, respectively. S M O The transformation matrices for random UAVs and relative UAVs, respectively, Λ S Λ O These are the diagonal matrices for random drones and relative drones, respectively;

[0092] Step 2.4: Obtain the initial position p0 = [x0 y0 z0] relative to the UAV. T This leads to the subsequent relative position with respect to the initial position of the drone as the origin: p O (t) = [x - x0 y - y0 z - z0] T ;

[0093] Calculate the combined collision protection zone D for the two drones:

[0094]

[0095] Among them, R f H fLet be the radius and total height of the collision protection zone D, respectively, and let A be the size matrix of the collision protection zone D.

[0096] Step 2.5: Calculate the real-time collision probability P(kT) between the two drones. s The calculation formula is:

[0097]

[0098] Among them, kT s For different time periods, Δp(kT) s ( ) represents the relative positions of the two drones. M represents the average initial relative position of the two drones in each stage. S Λ S (kT s M S T +M O Λ O (kT s M O T Let be the covariance of the relative positions of the two drones in each stage.

[0099] Step 2.6: Obtain the velocity vector projection v' = [v'] of the random UAV in the direction relative to the UAV. x ,v' y ,v' z Then, the collision ratio R(O) during the collision between the two drones is calculated using the following formula:

[0100]

[0101] Step 2.7: Finally, calculate the collision risk between the two drones. Based on different directions, the collision risk is divided into: longitudinal collision risk F x Lateral collision risk F y Vertical collision risk F z The formulas are as follows:

[0102]

[0103]

[0104]

[0105] The total collision risk for a single drone pair is:

[0106] F(t)=F x (t)+F y (t)+F z (t)

[0107] Where t represents the running time, E(0) is the proximity rate, representing the frequency of loss intervals between any two drones per unit time, P(O) is the collision probability, R(O) is the collision ratio, and the final collision risk calculation result is: the frequency of collisions occurring per unit time; when the relative velocity projection v' of two drones in a certain direction... i When (i = x, y, z) is 0, the collision risk in that direction is 0. In some embodiments, E(0) is 0.01.

[0108] Step 3: Calculate the collision risk of any two drones within the operating system and sum the results. Calibrate the safe distance between drones by setting the system safety level. The distance calibration result for the intersecting flight path scenario is the relative distance between the two drones.

[0109] Step 3.1: System security level F total (t) The collision risks of any two of the n rotary-wing UAVs in the operating system are accumulated, and the calculation formula is as follows:

[0110]

[0111] F total (t)≤F max (t)

[0112] Where F max (t) represents the preset target value for the system security level;

[0113] Step 3.2: Based on the preset system security level target value F max Solve the constraints (t) to obtain the relative position Δp of any two UAVs under the safety level, and use Δp to calibrate the safe operating interval of the UAVs.

[0114] In some embodiments, the system security level target value F max (t) Equivalent safety level for using drones: 10 -7 The number of collisions per flight hour, i.e., the number of system collisions per hour, must not exceed 10. -7 Second-rate.

[0115] This invention realizes a method for calibrating safe distances for rotary-wing UAVs based on real-time collision risk calculation. It proposes a complete calibration method for safe distances in large-scale operation of rotary-wing UAVs under structured routes in the future, and can effectively calculate collision risks and calibrate safe distances for different flight path structures and different UAV models.

[0116] Example 2

[0117] Secondly, this embodiment provides a collision risk-based rotorcraft unmanned aerial vehicle (UAV) operation safety interval calibration device, including a processor and a storage medium;

[0118] The storage medium is used to store instructions;

[0119] The processor is configured to operate according to the instructions to perform the steps of the method according to Embodiment 1.

[0120] Example 3

[0121] Thirdly, this embodiment provides a storage medium on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for calibrating safe operating intervals for rotary-wing unmanned aerial vehicles based on collision risk, characterized in that, Includes the following steps: Step 1: Based on the shape and size data and operational attitude constraints of the rotorcraft UAV, construct a collision protection zone model for the rotorcraft UAV, including: based on the maximum radius of the rotorcraft UAV... With the whole high Construct the minimum circumscribed cylinder of the rotary-wing UAV; based on the minimum circumscribed cylinder and considering the operational attitude constraints of the rotary-wing UAV, construct the circumscribed ellipsoid of the minimum circumscribed cylinder with dimensions of [missing information]. ; , The horizontal radius and total height of the circumscribed ellipsoid are respectively defined; the circumscribed ellipsoid is used as the collision protection zone for the rotary-wing UAV; and a collision judgment model for any two UAVs is constructed based on the collision protection zone model of the rotary-wing UAV. Step 2: Based on the collision judgment model of any two UAVs, and by setting the trajectory error distribution and flight path configuration of the rotorcraft UAVs, construct a real-time collision risk calculation model for the rotorcraft UAVs, including: Obtain the velocity vector projection of the random UAV in the direction relative to the UAV. , The relative velocity projections of the two drones in the x, y, and z directions are given, and the collision relationship ratio during the collision process is calculated. The calculation formula is as follows: ； in For collision relationship ratio, , These are combined collision protection zones. The radius and total height; Calculate the collision risk between two drones, and categorize the collision risk according to different directions: longitudinal collision risk. Lateral collision risk Vertical collision risk The formulas are as follows: ； ； ； The total collision risk for a single drone pair is: ； in, Represents runtime. The proximity rate represents the frequency of time between the loss of any two drones per unit of time. Let be the collision probability. The collision risk ratio is calculated as follows: the frequency of collisions per unit time; the relative velocity projection of two drones in a certain direction. When the value is 0, the collision risk in that direction is 0; Step 3: Based on the real-time collision risk calculation model of the rotorcraft UAV, construct a system safety level model, and solve the system safety level model based on the preset system safety level target value constraints to obtain the relative positions of any two UAVs under this safety level. Step 4: Based on the relative positions of any two UAVs at the system level, calibrate the safe operating interval of the rotary-wing UAVs.

2. The method for calibrating safe operating intervals of rotary-wing UAVs based on collision risk according to claim 1, characterized in that, The operating attitude limitations of the rotary-wing UAV include the maximum pitch angle. With maximum roll angle .

3. The method for calibrating safe operating intervals of rotary-wing UAVs based on collision risk according to claim 1, characterized in that, In step 1, a collision determination model for any two UAVs is constructed based on the rotorcraft UAV collision protection zone model, including: Step 1.2: For any two drones, one being a relative drone and the other a random drone, combine the collision protection zones of the two drones to obtain the combined collision protection zone area. Based on the relative positions of the two drones and the combined area Construct a collision detection model for two drones: ； in, Indicates whether a collision has occurred. This indicates the relative positions of any two drones.

4. The method for calibrating safe operating intervals for rotary-wing unmanned aerial vehicles based on collision risk according to claim 1, characterized in that, Step 2 also includes: Step 2.1: Obtain the heading angle of the UAV by setting the flight path configuration of the rotary-wing UAV. and pitch angle Based on the heading angle and pitch angle Obtain the transformation matrix M between the global coordinate system and the body coordinate system; Step 2.2: Obtain the initial nominal position of the UAV By setting the trajectory error of the rotary-wing drone Obtain the nominal position mean The flight path error of the UAV follows a Gaussian distribution along the aircraft's coordinate axes, and is... , where the diagonal matrix ; The symbol for a diagonal matrix is... These represent the track errors in the x, y, and z directions, respectively. Based on the transformation matrix M and the nominal position mean The global position distribution of the UAV is obtained. ; ； in, The covariance of the transformed probability distribution; Step 2.3: Based on the global position distribution of the two drones, calculate the relative positions of the two drones: The relative position probability distribution of the two drones was obtained. : ； in , These are the position probability distributions of random drones and relative drones, respectively. , These represent the trajectory errors of random and relative drones, respectively. , These are the transformation matrices for random drones and relative drones, respectively. , These are the diagonal matrices for random drones and relative drones, respectively; Step 2.4: Obtain the initial position relative to the UAV This allows us to obtain the subsequent relative positions relative to the initial position of the drone as the origin: ; Calculate the combined area of ​​the collision protection zone for two drones : ； in, , These are combined collision protection zones. The radius and total height, Indicates a combination of collision protection zones The size matrix; Step 2.5: Calculate the real-time collision probability of the two drones. The calculation formula is: ； in, For different time periods, The relative positions of the two drones. The average initial relative positions of the two drones in each stage. Let be the covariance of the relative positions of the two drones in each stage.

5. The method for calibrating safe operating intervals for rotary-wing unmanned aerial vehicles based on collision risk according to claim 4, characterized in that, The flight path configurations of the rotary-wing UAV include: single flight path, parallel flight path, and vertical-parallel flight path.

6. The method for calibrating safe operating intervals for rotary-wing unmanned aerial vehicles based on collision risk according to claim 1, characterized in that, Take 0.

01.

7. The method for calibrating safe operating intervals of rotary-wing UAVs based on collision risk according to claim 1, characterized in that, Step 3 specifically includes: Step 3.1: System Security Level The collision risks of any two of the n rotary-wing UAVs in the operating system are accumulated using the following formula: ； ≤ ； in The preset system security level target value; Step 3.2: Based on the preset system security level target value By solving for the constraints, the relative positions of any two drones under this safety level can be obtained. ,use The safe operating intervals for drones are calibrated.

8. The method for calibrating safe operating intervals of rotary-wing UAVs based on collision risk according to claim 1, characterized in that, The target system security level, using the equivalent security level of the drone: 10 -7 The number of collisions per flight hour, i.e., the number of system collisions per hour, must not exceed 10. -7 Second-rate.

9. A collision risk-based operational safety interval calibration device for rotary-wing unmanned aerial vehicles, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 8.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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