A method for stable airdrop deployment by UAV swarms under high-speed and large-disturbance conditions

Abstract

This invention provides a stable airdrop deployment method for UAV swarms under high-speed, high-disturbance conditions, ensuring stable initial conditions for UAVs and improving the success rate of go-arounds. It also enables the deployment of all UAVs within constraints of altitude and time. Through analysis of extensive airdrop test flight data of deployment units and UAVs, and summarizing the attitude oscillation characteristics of deployment units with parachutes at balanced fall speeds, a fast and stable deployment scheme with adaptive hierarchical processing of release conditions is proposed for the first time. In the first stage, a relatively strict criterion is used to constrain the pitch angle and angular rate at the moment of exiting the deployment compartment, providing better initial conditions for the UAVs. This solves the problem of poor conditions for deploying UAVs with open compartments under high-speed, high-disturbance conditions, significantly improving the go-around rate of UAVs.

Description

Technical Field

[0001] This invention belongs to the field of drone swarm systems and mainly relates to a stable airdrop and deployment method for drone swarms. Background Technology

[0002] In a drone swarm deployment system, the carrier aircraft deploys a deployment platform equipped with deployment units. The platform glides at high speed towards the target area and then releases deployment units containing a large swarm of drones. These deployment units sequentially deploy their deceleration parachutes to slow to equilibrium landing speed before releasing the swarm of drones according to a predetermined strategy. The drones then self-organize to form a large-scale swarm formation to execute the planned mission. During the deployment of the swarm of drones, the deployment units themselves experience significant disturbances and rapid changes in attitude and angular velocity. If the deployment units are placed in optimal attitude at the moment of deployment, and overly strict exit conditions are applied, it becomes difficult to find suitable deployment points, preventing all drones from exiting the drones within the constraints of altitude and time. Conversely, using lenient exit conditions leads to greater randomness in the exit attitude of the drones, hindering go-arounds. Therefore, a stable and effective drone deployment scheme is needed to ensure stable initial conditions for the drones, improve the success rate of go-arounds, and enable the deployment of all drones within the constraints of altitude and time. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, this invention proposes a stable airdrop method for UAV swarms under high-speed and large-disturbance conditions, which enables UAVs to have stable initial conditions and improves the success rate of go-around, and can complete the deployment of all UAVs under the constraints of limited altitude and time.

[0004] A stable airdrop method for drone swarms under high-speed, large-disturbance conditions includes the following steps:

[0005] Step 1: Navigation unit data filtering and smoothing;

[0006] The main control unit of the deployment platform continuously obtains the current pitch angle, roll angle, yaw angle and pitch rate from the navigation unit, and smooths the pitch angle data for any jumps in the pitch angle data used.

[0007] Step 2: Compare the smoothed pitch angle of the navigation unit in the current frame with the smoothed pitch angles of the navigation unit in the recorded N consecutive frames to determine the launch conditions;

[0008] Step 2.1: Determine the first-level constraint launch conditions;

[0009] When all the requirements of the first-level constraint launch condition are met simultaneously, the first-level constraint algorithm is used. If any item of the first-level constraint launch condition is not met, the process jumps to step 2.2.

[0010] The first-level constraint launch condition is:

[0011] a. The current time is greater than the start time t1 of the first-level constraint and less than or equal to the termination time t2 of the first-level constraint;

[0012] b. The smoothed pitch angle is greater than the first-level constraint threshold δ min ;

[0013] c. The pitch rate of the current frame obtained from the navigation unit is greater than the minimum pitch rate ωz. min And less than the maximum pitch rate ωz max The minimum pitch rate ωz min and the maximum value of pitch rate ωz max This is the default value;

[0014] d. If the N / 2th frame of the smoothed pitch angles from the first N frames is an extreme point among all the data;

[0015] e. If the former The pitch angle after frame smoothing shows a monotonically increasing trend;

[0016] f. The pitch angle after smoothing in the current frame shows a monotonically decreasing trend compared to the pitch angles after smoothing in the previous 1 to N / 2 frames;

[0017] g. The drone launch time interval is t. delay Second;

[0018] Step 2.2: Determine the secondary constraint launch conditions. If all secondary constraint launch conditions are met simultaneously, then the secondary constraint algorithm is used; if any of the secondary constraint launch conditions are not met, then proceed to step 2.3.

[0019] The secondary constraint launch conditions are:

[0020] a. The current time is greater than the termination time t2 of the first-level constraint and less than or equal to the termination time t3 of the second-level constraint;

[0021] b. When the smoothed pitch angle is greater than the secondary constraint threshold δ min_2 ;

[0022] c. The pitch rate is not constrained in any way;

[0023] d. If the N / 2th frame of the smoothed pitch angles from the first N frames is an extreme point among all the data;

[0024] e. front The pitch angle after frame smoothing shows a monotonically increasing trend;

[0025] f. The pitch angle after smoothing in the current frame shows a monotonically decreasing trend compared to the pitch angles after smoothing in the previous 1 to N / 2 frames;

[0026] g. UAV launch time interval t delay Second;

[0027] Step 2.3: Determine the three-level constraint launch conditions;

[0028] If the current time is greater than the termination time t3 of the second-level constraint and less than the termination time t4 of the third-level constraint, then the constraint t4 must be forced to comply. delay Launch the drone in seconds; if the current time is less than or equal to the termination time t3 of the second-level constraint or greater than or equal to the termination time t4 of the third-level constraint, then the current frame processing ends, jump to step 1, and start the processing of the next frame.

[0029] Furthermore, the steps for filtering and smoothing the pitch angles in the obtained navigation data are as follows:

[0030] Step 1-1: Data definition and initialization;

[0031] N smooth Maximum smooth frame count; N smooth =5;

[0032] f1_δ is the pitch angle of the first frame preceding the current frame; the initial value of the pitch angle of the first frame preceding the current frame is 0;

[0033] f2_δ is the pitch angle of the second frame preceding the current frame; the initial value of the pitch angle of the second frame preceding the current frame is 0;

[0034] f3_δ is the pitch angle of the third frame preceding the current frame; the initial value of the pitch angle of the third frame preceding the current frame is 0;

[0035] f4_δ is the pitch angle of the fourth frame preceding the current frame; the initial value of the pitch angle of the fourth frame preceding the current frame is 0.

[0036] `count` is the update count for the navigation unit; the initial value of `count` is 0; and `count` ≤ N. smooth ;

[0037] δ is the pitch angle of the current frame;

[0038] ωz is the pitch angular velocity of the current frame;

[0039] sum δ This is the sum of pitch angles;

[0040] δ smooth The pitch angle after smoothing;

[0041] Step 1-2: Obtain the pitch angle δ and pitch angular velocity ωz of the current frame navigation unit, update the navigation unit update count, and increment the value of count by 1; if count is greater than or equal to N smooth Then let count = N smooth Otherwise, keep the value of count.

[0042] Steps 1-3: Update the pitch angle of the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame;

[0043] The calculation steps for updating the pitch angle of the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame are as follows:

[0044] f4_δ=f3_δ;

[0045] f3_δ=f2_δ;

[0046] f2_δ=f1_δ;

[0047] f1_δ=δ;

[0048] Steps 1-4: Obtain the smoothed pitch angle;

[0049] Sum the pitch angles of the current frame, the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame.

[0050] sum δ =δ+f1_δ+f2_δ+f3_δ+f4_δ

[0051]

[0052] δ smooth This is the smoothed pitch angle.

[0053] Furthermore, the specific steps of the first-level constraint algorithm are as follows:

[0054] 1) When the current time is greater than the start time t1 of the first-level constraint and less than or equal to the termination time t2 of the first-level constraint, calculate the judgment condition state. δ and state ωz ;

[0055] When δ smooth >δ min And δ smooth When <0, state δ The value of δ is 1 when δsmooth >δ min or δ smooth When any term <0 is not satisfied, state δ The value is 0;

[0056] When ωz≥ωz min And when ωz≤0, state ωz The value is 1; when ωz ≥ ωz min If either ωz≤0 is not satisfied, then state ωz The value is 0;

[0057] Where ωz is the pitch rate of the navigation unit; ωz min The minimum pitch rate;

[0058] 2) Determine if the smoothed pitch angle meets the extreme value judgment condition. extreme ;

[0059] If the N / 2th frame in the first N frames is an extreme point in all data, and the first... If the number of frames shows a monotonically increasing trend, and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend; and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend, then the extreme value judgment condition is state. extreme =1; otherwise, the extreme value judgment condition is state. extreme =0;

[0060] 3) If state δ >0 and state ωz >0 and state extreme If the value is greater than 0 and the time constraint is met, then the deployment of the sub-machine is allowed, and the deployment time is recorded; if the state is greater than 0, then the deployment of the sub-machine is allowed. δ >0 or state ωz >0 or state extreme If any condition is not met in step >0, then proceed to step 2.2;

[0061] The time constraint is: the current time is greater than the sum of the time interval between the previous drone deployment and the drone launch time, and the current time is less than the termination time t2 of the first-level constraint.

[0062] Furthermore, the steps of the second-level constraint algorithm are as follows:

[0063] 1) When the current time is greater than the termination time t2 of the first-level constraint and less than or equal to the termination time t3 of the second-level constraint, calculate the second-level judgment condition state. δ_2 ;

[0064] When δ smooth >δ min_2 And δ smoothWhen < 0, the second-level judgment condition is state. δ_2 The value of δ is 1 when δ smooth >δ min_2 or δ smooth When any term <0 is not satisfied, the second-level condition `state` is applied. δ_2 The value is 0;

[0065] 2) Determine if the smoothed pitch angle satisfies the second-order extreme value judgment condition. extreme_2 ;

[0066] If the N / 2th frame in the first N frames is an extreme point in all data, and the first... If the number of frames shows a monotonically increasing trend, and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend, then the second-order extremum judgment condition is state. extreme_2 =1; otherwise, the second-order extreme value judgment condition is state. extreme_2 =0;

[0067] 3) If the second-level condition state δ_2 >0 and the condition for judging second-order extreme values ​​(state) extreme_2 If the value is greater than 0 and the time constraint is met, then the deployment of the sub-machine is allowed, and the deployment time is recorded; if any of the above conditions are not met, then proceed to step 2.3.

[0068] The beneficial effects of this invention are as follows: Compared with other deployment and opening technologies, the stable airdrop and deployment technology for UAV swarms under high speed and large disturbance conditions proposed in this invention can achieve rapid and stable deployment of UAVs under the dual constraints of altitude and time, and can ensure that the UAVs have a good attitude when leaving the cabin. It has been applied in the airdrop and deployment of UAV swarm systems, and the effectiveness of the invention has been proven through experiments. Multiple experimental data show that the method can achieve a 100% deployment success rate, and the UAV re-fly rate can also reach more than 66%. Attached Figure Description

[0069] Figure 1 This is a flowchart of the algorithm's single-frame processing flow;

[0070] Figure 2 It is a diagram showing the pitch angle and dispensing time of the dispensing unit;

[0071] Figure 3 It is a diagram showing the pitch angular velocity of the spreading unit and the spreading time.

[0072] Figure 4 It is a diagram showing the trajectory angles of the dispersal units and the dispersal timing;

[0073] Figure 5 It is a diagram showing the yaw rate and dispensing time of the dispensing unit;

[0074] Figure 6 It is a diagram showing the rolling angle and spreading time of the spreading unit;

[0075] Figure 7 It is a diagram showing the angular velocity of the spreading unit and the spreading time.

[0076] In the diagram, red circles represent drones launched under Level 1 or Level 2 constraints; green circles represent drones launched under Level 3 constraints. Detailed Implementation

[0077] This invention discloses a method for stable airdrop deployment of UAV swarms under high-speed, large-disturbance conditions. Based on empirical conclusions derived from extensive flight test data of deployment units and UAVs, the optimal deployment time is determined dynamically by the polarity relationship between attitude angle and angular rate. The optimal time for deployment is when the pitch angle exhibits a monotonic change. At this time, the deployment unit has a negative pitch angular velocity and moves in a direction perpendicular to the ground. After the UAV exits its capsule, it is essentially in a vertically downward attitude, with its body direction close to its velocity direction, resulting in small angles of attack and sideslip. Furthermore, the vertically downward attitude ensures stable tilting of the aircraft regardless of the direction of pull-up, facilitating a successful go-around. To ensure successful launch of all UAVs, a rapid and stable capsule-opening deployment scheme with adaptive hierarchical processing of release conditions is proposed. In the first stage, relatively strict criteria are used to constrain the pitch angle and angular rate at the moment of capsule exit, while the constraints are relaxed in the second and third stages.

[0078] The technical solution adopted by this invention to solve its technical problem is a stable airdrop deployment method for UAV swarms under high-speed and large-disturbance conditions, comprising the following steps:

[0079] Step 1: Navigation unit data filtering and smoothing;

[0080] The main control unit of the deployment platform continuously obtains the current pitch angle, roll angle, yaw angle and pitch rate from the navigation unit. It smooths the pitch angle data for any jumps in the pitch angle data it uses. It also continuously filters and smooths the pitch angle in the obtained navigation data, mainly to filter out possible jumps, so that the data is relatively smooth and reliable and will not affect subsequent logical judgments.

[0081] The steps to filter and smooth the pitch angle in the obtained navigation data are as follows:

[0082] Step 1-1: Data definition and initialization;

[0083] N smooth Maximum smooth frame count; N smooth =5;

[0084] f1_δ is the pitch angle of the first frame preceding the current frame; the initial value of the pitch angle of the first frame preceding the current frame is 0;

[0085] f2_δ is the pitch angle of the second frame preceding the current frame; the initial value of the pitch angle of the second frame preceding the current frame is 0;

[0086] f3_δ is the pitch angle of the third frame preceding the current frame; the initial value of the pitch angle of the third frame preceding the current frame is 0;

[0087] f4_δ is the pitch angle of the fourth frame preceding the current frame; the initial value of the pitch angle of the fourth frame preceding the current frame is 0.

[0088] `count` is the update count for the navigation unit; the initial value of `count` is 0; and `count` ≤ N. smooth ;

[0089] δ is the pitch angle of the current frame;

[0090] ωz is the pitch angular velocity of the current frame;

[0091] sum δ This is the sum of pitch angles;

[0092] δ smooth The pitch angle after smoothing;

[0093] Step 1-2: Obtain the pitch angle δ and pitch angular velocity ωz of the current frame navigation unit, update the navigation unit update count, and increment the value of count by 1; if count is greater than or equal to N smooth Then let count = N smooth Otherwise, keep the value of count.

[0094] Steps 1-3: Update the pitch angle of the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame;

[0095] Update the pitch angle of the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame. The calculation steps are as follows:

[0096] f4_δ=f3_δ;

[0097] f3_δ=f2_δ;

[0098] f2_δ=f1_δ;

[0099] f1_δ=δ

[0100] Steps 1-4: Obtain the smoothed pitch angle;

[0101] Sum the pitch angles of the current frame, the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame.

[0102] sum δ =δ+f1_δ+f2_δ+f3_δ+f4_δ

[0103]

[0104] δ smooth That is, the smoothed pitch angle;

[0105] Step 2: Compare the smoothed pitch angle of the navigation unit in the current frame with the smoothed pitch angles of the navigation unit in the recorded N consecutive frames to determine the launch conditions;

[0106] Step 2.1: Determine the first-level constraint launch conditions;

[0107] When all the requirements of the first-level constraint launch condition are met simultaneously, the first-level constraint algorithm is used. If any item of the first-level constraint launch condition is not met, the process jumps to step 2.2.

[0108] The first-level constraint launch condition is:

[0109] a. The current time is greater than t1 and less than or equal to t2;

[0110] b. The smoothed pitch angle is greater than the first-level constraint threshold δ min ;

[0111] c. The pitch rate of the current frame obtained from the navigation unit is greater than the minimum pitch rate ωz. min And less than the maximum pitch rate ωz max The minimum pitch rate ωz min and the maximum value of pitch rate ωz max This is the default value;

[0112] d. If the N / 2th frame of the smoothed pitch angles from the first N frames is an extreme point among all the data;

[0113] e. If the former The pitch angle after frame smoothing shows a monotonically increasing trend;

[0114] f. The pitch angle after smoothing in the current frame shows a monotonically decreasing trend compared to the pitch angles after smoothing in the previous 1 to N / 2 frames;

[0115] g. The drone launch time interval is t. delay Second;

[0116] The specific steps of the algorithm for first-level constraints are as follows:

[0117] 1) When the current time is greater than the start time t1 of the first-level constraint and less than or equal to the termination time t2 of the first-level constraint, calculate the judgment condition state. δ and state ωz ;

[0118] When δ smooth >δ min And δ smooth When <0, state δ The value of δ is 1 when δ smooth >δ min or δ smooth When any term <0 is not satisfied, state δ The value is 0;

[0119] When ωz≥ωz min And when ωz≤0, state ωz The value is 1; when ωz ≥ ωz min If either ωz≤0 is not satisfied, then state ωz The value is 0;

[0120] Where ωz is the pitch rate of the navigation unit; ωz min The minimum pitch rate;

[0121] 2) Determine if the smoothed pitch angle meets the extreme value judgment condition. extreme ;

[0122] If the N / 2th frame in the first N frames is an extreme point in all data, and the first... If the number of frames shows a monotonically increasing trend, and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend; and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend, then the extreme value judgment condition is state. extreme =1; otherwise, the extreme value judgment condition is state. extreme =0;

[0123] 3) If state δ >0 and state ωz >0 and state extreme If the value is greater than 0 and the time constraint is met, then the deployment of the sub-machine is allowed, and the deployment time is recorded; if the state is greater than 0, then the deployment of the sub-machine is allowed. δ >0 or state ωz >0 or state extreme If any condition is not met in step >0, then proceed to step 2.2;

[0124] The time constraint is: the current time is greater than the sum of the time interval between the time of the previous drone deployment and the drone launch time, and the current time is less than the termination time t2 of the first-level constraint;

[0125] Step 2.2: Determine the secondary constraint launch conditions. If all secondary constraint launch conditions are met simultaneously, then the secondary constraint algorithm is used; if any of the secondary constraint launch conditions are not met, then proceed to step 2.3.

[0126] The secondary constraint launch conditions are:

[0127] a. The current time is greater than t2 and less than or equal to t3;

[0128] b. When the smoothed pitch angle is greater than the secondary constraint threshold δ min_2 ;

[0129] c. The pitch rate is not constrained in any way;

[0130] d. If the N / 2th frame of the smoothed pitch angles from the first N frames is an extreme point among all the data;

[0131] e. front The pitch angle after frame smoothing shows a monotonically increasing trend;

[0132] f. The pitch angle after smoothing in the current frame shows a monotonically decreasing trend compared to the pitch angles after smoothing in the previous 1 to N / 2 frames;

[0133] g. UAV launch time interval t delay Second;

[0134] The steps of the second-level constraint algorithm are as follows:

[0135] 1) When the current time is greater than the termination time t2 of the first-level constraint and less than or equal to the termination time t3 of the second-level constraint, calculate the second-level judgment condition state. δ_2 ;

[0136] When δ smooth >δ min_2 And δ smooth When <0, the second-level condition state δ_2 The value of δ is 1 when δ smooth >δ min_2 or δ smooth When any term <0 is not satisfied, the second-level condition `state` is checked. δ_2 The value is 0;

[0137] 2) Determine if the smoothed pitch angle satisfies the second-order extreme value judgment condition. extreme_2 ;

[0138] If the N / 2th frame in the first N frames is an extreme point in all data, and the first... If the number of frames shows a monotonically increasing trend, and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend, then the second-order extremum judgment condition is state. extreme_2=1; otherwise, the second-order extreme value judgment condition is state. extreme_2 =0;

[0139] 3) If the second-level condition state δ_2 >0 and the condition for judging second-order extreme values ​​(state) extreme_2 If the value is greater than 0 and the time constraint is met, then the deployment of the sub-machine is allowed, and the deployment time is recorded; if any of the above conditions are not met, then proceed to step 2.3.

[0140] Step 2.3: Determine the three-level constraint launch conditions;

[0141] If the current time is greater than the termination time t3 of the second-level constraint and less than the termination time t4 of the third-level constraint, then the constraint t4 must be forced to comply. delay The drone is launched every second to ensure that all sub-drones can safely exit the cabin; if the current time is less than or equal to the termination time t3 of the second-level constraint or greater than or equal to the termination time t4 of the third-level constraint, the processing of the current frame ends and jumps to step 1-2 to start the processing of the next frame.

[0142] The ground coordinate system is defined as follows: the origin A is the position of the body's center of mass at the moment of launch; the Ax axis points north in the horizontal plane, with the direction being positive; the Az axis is located in the horizontal plane and is perpendicular to the Ax axis, pointing east, with the direction being positive; the Ay axis is perpendicular to the plane formed by Ax and Z, forming a right-handed coordinate system.

[0143] The aircraft coordinate system is positioned as follows: the origin O is located at the instantaneous center of inertia of the UAV; the Ox1 axis is aligned with the UAV's longitudinal axis, pointing positively towards the head; the Oy1 axis lies within the UAV's longitudinal plane of symmetry, perpendicular to the Ox1 axis and pointing positively upwards; the Oz1 axis is perpendicular to the x1Oy1 plane, forming a right-handed coordinate system. The angle between the Ox1 axis and the Axz plane is the pitch angle δ, with the UAV's longitudinal axis above the horizontal plane being positive; the angle between the projection of the UAV's longitudinal axis onto the horizontal plane and the Ax axis is the yaw angle ψ, with leftward yaw being positive; the angle between the Oy1 axis and the vertical plane containing the UAV's longitudinal axis is the roll angle γ, with rightward roll being positive.

[0144] To make the objectives, contents, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0145] The implementation of a stable airdrop technology for drone swarms under high-speed, high-disturbance conditions is achieved through the following steps:

[0146] 1: Start, set the start time and end time for the three constraint states, as well as the pitch angle and pitch velocity thresholds for the three constraint states;

[0147] 2: Continuously read the attitude angle and attitude angular velocity data given by the scattering unit navigation, perform model calculations, and issue the hatch opening and scattering command.

[0148] Compared with other deployment and deployment technologies, the stable airdrop deployment technology for UAV swarms under high speed and large disturbance conditions proposed in this invention can achieve rapid and stable deployment of UAVs under dual constraints of altitude and time, and can ensure that the UAVs maintain a good attitude when exiting the deployment capsule. This technology has been applied in UAV swarm deployment systems during airdrop, and its effectiveness has been proven through experiments. Taking a UAV swarm project as an example, a total of 5 deployment tests were conducted, and the method achieved a 100% deployment success rate, with a UAV go-around rate of over 66%.

[0149] Table 1 is a statistical table of the success rate of a drone swarm airdrop test and the return flight.

[0150] Table 1. Statistics on the success rate of drone swarm airdrop test and re-flight.

[0151]

[0152]

Claims

1. A method for stable airdrop deployment by a drone swarm under high-speed, large-disturbance conditions, characterized in that: Includes the following steps: Step 1: Navigation unit data filtering and smoothing; The main control unit of the deployment platform continuously obtains the current pitch angle, roll angle, yaw angle and pitch rate from the navigation unit, and smooths the pitch angle data for any jumps in the pitch angle data used. Step 2: Compare the smoothed pitch angle of the navigation unit in the current frame with the smoothed pitch angles of the navigation unit in the recorded N consecutive frames to determine the launch conditions; Step 2.1: Determine the first-level constraint launch conditions; When all the requirements of the first-level constraint launch condition are met simultaneously, the first-level constraint algorithm is used. If any item of the first-level constraint launch condition is not met, the process jumps to step 2.

2. The first-level constraint launch condition is: a. The current time is greater than the start time t1 of the first-level constraint and less than or equal to the termination time t2 of the first-level constraint; b. The smoothed pitch angle is greater than the first-level constraint threshold δ min ; c. The pitch rate of the current frame obtained from the navigation unit is greater than the minimum pitch rate ωz. min And less than the maximum pitch rate ωz max The minimum pitch rate ωz min and the maximum value of pitch rate ωz max This is the default value; d. If the N / 2th frame of the smoothed pitch angles from the first N frames is an extreme point among all the data; e. If the former The pitch angle after frame smoothing shows a monotonically increasing trend; f. The pitch angle after smoothing in the current frame shows a monotonically decreasing trend compared to the pitch angles after smoothing in the previous 1 to N / 2 frames; g. The drone launch time interval is t. delay Second; Step 2.2: Determine the secondary constraint launch conditions. If all secondary constraint launch conditions are met simultaneously, then the secondary constraint algorithm is used; if any of the secondary constraint launch conditions are not met, then proceed to step 2.

3. The secondary constraint launch conditions are: a. The current time is greater than the termination time t2 of the first-level constraint and less than or equal to the termination time t3 of the second-level constraint; b. When the smoothed pitch angle is greater than the secondary constraint threshold δ min_2 ; c. The pitch rate is not constrained in any way; d. If the N / 2th frame of the smoothed pitch angles from the first N frames is an extreme point among all the data; e. front The pitch angle after frame smoothing shows a monotonically increasing trend; f. The pitch angle after smoothing in the current frame shows a monotonically decreasing trend compared to the pitch angles after smoothing in the previous 1 to N / 2 frames; g. UAV launch time interval t delay Second; Step 2.3: Determine the three-level constraint launch conditions; If the current time is greater than the termination time t3 of the second-level constraint and less than the termination time t4 of the third-level constraint, then the constraint t4 must be forced to comply. delay Launch drones in seconds; If the current time is less than or equal to the termination time t3 of the second-level constraint, or greater than or equal to the termination time t4 of the third-level constraint, then the processing of the current frame ends, and the process jumps to step 1 to start the processing of the next frame.

2. The method for stable airdrop deployment by a drone swarm under high-speed, large-disturbance conditions according to claim 1, characterized in that, The steps to filter and smooth the pitch angle in the obtained navigation data are as follows: Step 1-1: Data definition and initialization; N smooth Maximum smooth frame count; N smooth =5; f1_δ is the pitch angle of the first frame preceding the current frame; the initial value of the pitch angle of the first frame preceding the current frame is 0; f2_δ is the pitch angle of the second frame preceding the current frame; the initial value of the pitch angle of the second frame preceding the current frame is 0; f3_δ is the pitch angle of the third frame preceding the current frame; the initial value of the pitch angle of the third frame preceding the current frame is 0; f4_δ is the pitch angle of the fourth frame preceding the current frame; the initial value of the pitch angle of the fourth frame preceding the current frame is 0. `count` is the update count for the navigation unit; the initial value of `count` is 0; and `count` ≤ N. smooth ; δ is the pitch angle of the current frame; ωz is the pitch angular velocity of the current frame; sum δ This is the sum of pitch angles; δ smooth The smoothed pitch angle; Step 1-2: Obtain the pitch angle δ and pitch angular velocity ωz of the current frame navigation unit, update the navigation unit update count, and increment the value of count by 1; if count is greater than or equal to N smooth Then let count = N smooth Otherwise, keep the value of count. Steps 1-3: Update the pitch angle of the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame; The calculation steps for updating the pitch angle of the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame are as follows: f4_δ=f3_δ; f3_δ=f2_δ; f2_δ=f1_δ; f1_δ=δ; Steps 1-4: Obtain the smoothed pitch angle; Sum the pitch angles of the current frame, the first frame before the current frame, the second frame before the current frame, the third frame before the current frame, and the fourth frame before the current frame. sum δ =δ+f1_δ+f2_δ+f3_δ+f4_δ δ smooth This is the smoothed pitch angle.

3. The method for stable airdrop deployment by a drone swarm under high-speed, large-disturbance conditions according to claim 1, characterized in that, The specific steps of the algorithm for first-level constraints are as follows: 1) When the current time is greater than the start time t1 of the first-level constraint and less than or equal to the termination time t2 of the first-level constraint, calculate the judgment condition state. δ and state ωz ; When δ smooth >δ min And δ smooth When <0, state δ The value of δ is 1 when δ smooth >δ min or δ smooth When any term <0 is not satisfied, state δ The value is 0; When ωz≥ωz min And when ωz≤0, state ωz The value is 1; when ωz ≥ ωz min If either ωz≤0 is not satisfied, then state ωz The value is 0; Where ωz is the pitch rate of the navigation unit; ωz min The minimum pitch rate; 2) Determine if the smoothed pitch angle meets the extreme value judgment condition. extreme ; If the N / 2th frame in the first N frames is an extreme point in all data, and the first... If the number of frames shows a monotonically increasing trend, and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend; and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend, then the extreme value judgment condition is state. extreme =1; Otherwise, the extreme value judgment condition is state. extreme =0; 3) If state δ >0 and state ωz >0 and state extreme If the value is greater than 0 and the time constraint is met, then the deployment of the sub-machine is allowed, and the deployment time is recorded; if the state is greater than 0, then the deployment of the sub-machine is allowed. δ >0 or state ωz >0 or state extreme If any condition is not met in step >0, then proceed to step 2.2; The time constraint is: the current time is greater than the sum of the time interval between the previous drone deployment and the drone launch time, and the current time is less than the termination time t2 of the first-level constraint.

4. The method for stable airdrop deployment by a drone swarm under high-speed, large-disturbance conditions as described in claim 1, characterized in that, The steps of the second-level constraint algorithm are as follows: 1) When the current time is greater than the termination time t2 of the first-level constraint and less than or equal to the termination time t3 of the second-level constraint, calculate the second-level judgment condition state. δ_2 ; When δ smooth >δ min_2 And δ smooth When < 0, the second-level judgment condition is state. δ_2 The value of δ is 1 when δ smooth >δ min_2 or δ smooth When any term <0 is not satisfied, the second-level condition `state` is applied. δ_2 The value is 0; 2) Determine if the smoothed pitch angle satisfies the second-order extreme value judgment condition. extreme_2 ; If the N / 2th frame in the first N frames is an extreme point in all data, and the first... If the number of frames shows a monotonically increasing trend, and the current frame and the previous 1 to N / 2 frames show a monotonically decreasing trend, then the second-order extremum judgment condition is state. extreme_2 =1; otherwise, the second-order extreme value judgment condition is state. extreme_2 =0; 3) If the second-level condition state δ_2 >0 and the condition for judging second-order extreme values ​​(state) extreme_2 If the value is greater than 0 and the time constraint is met, then the deployment of the sub-machine is allowed, and the deployment time is recorded; if any of the above conditions are not met, then proceed to step 2.

3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements a stable airdrop deployment method for UAV swarms under high-speed, high-disturbance conditions as described in any one of claims 1-4.

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