CN115599127A - Unmanned aerial vehicle formation obstacle avoidance control method based on laser radar - Google Patents

Abstract

The invention relates to an unmanned aerial vehicle formation obstacle avoidance control method based on a laser radar, belonging to the technical field of unmanned aerial vehicle offset correction, and the method comprises the following steps: acquiring a plurality of flight periods of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles from the beginning of flying until the unmanned aerial vehicles meet obstacles, calculating the wind disturbance degree of each unmanned aerial vehicle in each flight period, and determining the precompensation value of each unmanned aerial vehicle by using the historical flight data of each unmanned aerial vehicle and the wind disturbance degree of each unmanned aerial vehicle in each flight period; obtaining a final offset correction value of the theoretical obstacle avoidance track according to the precompensation value of each unmanned aerial vehicle, and correcting the theoretical obstacle avoidance track by using the final offset correction value to obtain a final obstacle avoidance track of the formation of the unmanned aerial vehicles; according to the method, the offset of each unmanned aerial vehicle is attached when the theoretical obstacle avoidance track of the unmanned aerial vehicle formation is corrected, and the compensation accuracy of the theoretical obstacle avoidance track is improved.

Description

Unmanned aerial vehicle formation obstacle avoidance control method based on laser radar

Technical Field

The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to an unmanned aerial vehicle formation obstacle avoidance control method based on a laser radar.

Background

Unmanned aerial vehicle is the unmanned aerial vehicle who utilizes radio remote control equipment and the program control device manipulation of self-contained, unmanned aerial vehicle is widely used at present in technical field such as search and rescue, survey, search and examine, patrol and examine, shoot plant protection, unmanned aerial vehicle can carry out the flight task alone, also can constitute unmanned aerial vehicle formation by many unmanned aerial vehicles and carry out the flight task in coordination, the key technical problem of unmanned aerial vehicle formation, mainly include formation design, pneumatic coupling, the dynamic adjustment of formation, track planning and unmanned aerial vehicle keep away the technical scheduling problem.

Because a low-altitude dynamic complex fusion airspace seriously threatens the formation flight operation of the unmanned aerial vehicles, the obstacle avoidance technology becomes a key link of a task decision system for the formation of the unmanned aerial vehicles; the leader-wing law is the method which is most commonly used in the control of the formation of a plurality of unmanned aerial vehicles at present, one unmanned aerial vehicle in the formation of the unmanned aerial vehicles is set as the leader, the other unmanned aerial vehicles are wing aircrafts, and laser radars carried by the leader can collect barrier data under any light condition, so that the formation of the unmanned aerial vehicles can be facilitated to plan a theoretical obstacle avoidance track for avoiding conflicts, the flight safety of the unmanned aerial vehicles is greatly improved, the leader calculates a safe flight track through a radar obstacle avoidance system, the whole formation is guided to fly, the wing aircrafts only need to follow the leader by a certain control strategy and formation, no extra track calculation is needed, and the laser radars carried by the leader are used for concentration on the reconnaissance and search operations of a cluster.

In the prior art, in order to eliminate track deviation, the method generally includes inputting the whole flight data of the unmanned aerial vehicle formation into a gaussian mixture model to obtain a pre-compensation value, and inputting general flight environment data such as wind resistance, wind direction, flight time, air density and the like recorded in the flight data of the unmanned aerial vehicle formation into the gaussian mixture model to obtain the pre-compensation value, but in the actual unmanned aerial vehicle formation, the compensation requirement of each unmanned aerial vehicle cannot be met due to the fact that each unmanned aerial vehicle is different in flight track, wind resistance and formation form in the flight process, the whole flight data of the unmanned aerial vehicle formation is directly used for obtaining the pre-compensation value, so that the compensation effect accuracy of the theoretical obstacle avoidance track of the unmanned aerial vehicle formation is low, and reasonable obstacle avoidance of the unmanned aerial vehicle formation cannot be realized.

Disclosure of Invention

The invention provides an unmanned aerial vehicle formation obstacle avoidance control method based on a laser radar, which is used for solving the problem that the compensation effect precision is low because the compensation requirement of each unmanned aerial vehicle cannot be met when the theoretical obstacle avoidance track is corrected at present.

The invention relates to an unmanned aerial vehicle formation obstacle avoidance control method based on a laser radar, which adopts the following technical scheme:

acquiring historical flight data of each unmanned aerial vehicle in the unmanned aerial vehicle formation before each unmanned aerial vehicle starts flying until encountering an obstacle;

according to the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space in the historical flight data, dividing the flight time of each unmanned aerial vehicle from the beginning of flight to the time before the unmanned aerial vehicle meets the obstacle into a plurality of flight time periods; the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space in each flight period is kept unchanged;

acquiring a theoretical wind resistance value of each unmanned aerial vehicle in each flight period;

calculating the wind disturbance degree of each unmanned aerial vehicle in each flight period according to the flight time of each unmanned aerial vehicle in each flight period, the maximum correction power during wind resistance confrontation, the maximum acceleration during offset correction and the received theoretical wind resistance value, wherein the flight time, the maximum correction power during wind resistance confrontation, the maximum acceleration during offset correction and the received theoretical wind resistance value are acquired from historical flight data;

inputting historical flight data of each unmanned aerial vehicle and the wind disturbance degree of each unmanned aerial vehicle in each flight period into a Gaussian mixture model, and outputting a clustering result of each unmanned aerial vehicle; the historical flight data of each unmanned aerial vehicle comprises the maximum offset of each unmanned aerial vehicle in each flight period, and the clustering result of each unmanned aerial vehicle comprises a plurality of clusters;

acquiring a theoretical obstacle avoidance track calculated by a long and medium-sized unmanned aerial vehicle formation machine at the moment of meeting an obstacle, taking the direction of flight of the unmanned aerial vehicle formation machine at the next moment of meeting the obstacle according to the theoretical obstacle avoidance track as the theoretical flight direction, and taking the spatial included angle between the theoretical flight direction and the wind direction of the unmanned aerial vehicle formation machine at the moment of meeting the obstacle as the predicted included angle of each unmanned aerial vehicle in the unmanned aerial vehicle formation machine;

acquiring clusters of a predicted included angle of each unmanned aerial vehicle classified in a clustering result corresponding to the unmanned aerial vehicle, and taking a maximum offset mean value contained in the classified clusters corresponding to each unmanned aerial vehicle as a pre-compensation value of each unmanned aerial vehicle;

and obtaining a final offset correction value of the theoretical obstacle avoidance track according to the precompensation value of each unmanned aerial vehicle, and correcting the theoretical obstacle avoidance track by using the final offset correction value to obtain the final obstacle avoidance track of the unmanned aerial vehicle formation.

Further, the step of obtaining a final offset correction value of the theoretical obstacle avoidance trajectory according to the pre-compensation value of each unmanned aerial vehicle includes:

calculating the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation by utilizing the wind disturbance degree of each unmanned aerial vehicle in all flight periods;

weighting the pre-compensation value of each unmanned aerial vehicle by utilizing the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation to obtain a weighted pre-compensation value of each unmanned aerial vehicle;

and adding the weighted pre-compensation values of all unmanned aerial vehicles in the unmanned aerial vehicle formation to obtain a final offset correction value of the obstacle avoidance track.

Further, the step of calculating the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles by using the wind disturbance degree of each unmanned aerial vehicle in all flight periods comprises:

the maximum wind disturbance degree of each unmanned aerial vehicle in all flight periods is used as the maximum wind disturbance degree, and the minimum wind disturbance degree of each unmanned aerial vehicle in all flight periods is used as the minimum wind disturbance degree;

taking a first ratio of the maximum wind disturbance degree and the minimum wind disturbance degree as a ratio of wind disturbance degree extreme values suffered by each unmanned aerial vehicle;

calculating the fluctuation entropy value of the wind disturbance degree of each unmanned aerial vehicle by utilizing the wind disturbance degree of each unmanned aerial vehicle in all flight periods;

calculating a first product of the ratio of the wind disturbance degree extreme value suffered by each unmanned aerial vehicle and the wind disturbance degree fluctuation entropy value suffered by the unmanned aerial vehicle;

and carrying out normalization processing on the first product corresponding to each unmanned aerial vehicle to obtain the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation.

Further, the step of weighting the pre-compensation value of each unmanned aerial vehicle by using the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation to obtain the weighted pre-compensation value of each unmanned aerial vehicle comprises the following steps:

calculating the sum of the historical wind disturbance degree fluctuation values of all unmanned aerial vehicles in the formation of the unmanned aerial vehicles;

taking a second ratio of the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle to the sum of the historical wind disturbance degree fluctuation values as a weight coefficient of a precompensation value of the unmanned aerial vehicle;

and multiplying the pre-compensation value of each unmanned aerial vehicle by the weight coefficient of the pre-compensation value of the unmanned aerial vehicle to obtain the weighted pre-compensation value of each unmanned aerial vehicle.

Further, the step of obtaining the theoretical wind resistance value to which each drone is subjected during each flight period includes:

according to the average movement speed of each unmanned aerial vehicle obtained according to historical flight data in each flight period along the flight direction, the average air density of the flight height of the unmanned aerial vehicle, the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space and the frontal windward area of each unmanned aerial vehicle, the theoretical wind resistance value of each unmanned aerial vehicle in each flight period is calculated.

Further, the step of obtaining the wind disturbance degree of each unmanned aerial vehicle in each flight period comprises:

taking a second product of the flight time of each unmanned aerial vehicle in each flight period and the maximum acceleration of each unmanned aerial vehicle during offset correction as the maximum instantaneous speed of each unmanned aerial vehicle in the direction of the counter-windage in each flight period;

taking a third ratio of the maximum corrective power of each unmanned aerial vehicle in resisting the wind resistance in each flight period to the maximum instantaneous speed in the direction of resisting the wind resistance as the maximum resistance of each unmanned aerial vehicle in resisting the wind resistance in each flight period;

taking a fourth ratio of the maximum opposing force of each unmanned aerial vehicle to the wind resistance in each flight period to the flight time as the average opposing force of each unmanned aerial vehicle to the wind resistance in each flight period in unit time;

and taking a fifth ratio of the average counter force of the unit time counter wind resistance of each unmanned aerial vehicle in each flight period to the received theoretical wind resistance value as the wind disturbance degree of each unmanned aerial vehicle in each flight period.

Further, the step of obtaining the theoretical obstacle avoidance track calculated by the medium and long aircrafts in the formation of the unmanned aerial vehicles when encountering the obstacle comprises:

the method comprises the steps of obtaining the offset of track deviation of long and medium-sized aircrafts in a formation of unmanned aerial vehicles when encountering an obstacle, and obtaining the distance between the long aircraft and the obstacle measured when the long aircraft generates track deviation;

calculating the distance between the corrected long machine and the obstacle by using the offset of the long machine which generates track deviation when the long machine encounters the obstacle and the distance between the long machine and the obstacle which is measured when the long machine generates track deviation;

and acquiring a theoretical obstacle avoidance track by using a radar obstacle avoidance system carried by the long machine according to the distance between the long machine and the obstacle after correction.

Further, the calculation step of the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space in each flight period comprises the following steps:

establishing a three-dimensional space coordinate system;

calculating included angles of the flight direction of each unmanned aerial vehicle and the wind direction in the X axis, the Y axis and the Z axis in the three-dimensional space coordinate system in each flight period according to the angle components of the wind direction in the three-dimensional space coordinate system in each flight period and the angle components of the flight direction of each unmanned aerial vehicle in the three-dimensional space coordinate system in each flight period in the X axis, the Y axis and the Z axis;

and calculating included angles and values of the flight direction and the wind direction of each unmanned aerial vehicle in each flight period in an X axis, a Y axis and a Z axis, and taking the Euclidean norm of the included angles and values as the included angle between the flight direction and the wind direction space of each unmanned aerial vehicle in each flight period.

The invention has the beneficial effects that:

in order to meet the compensation requirement of each unmanned aerial vehicle when compensating for a theoretical obstacle avoidance track, firstly, the flight time interval is divided according to the change of the included angle between the flight direction and the wind direction of each unmanned aerial vehicle, and because the included angle between the wind direction and the flight track of the unmanned aerial vehicle does not change in one flight time interval, the wind disturbance degree of the unmanned aerial vehicle in one flight time interval is considered to be the same; because the wind disturbance degree of each unmanned aerial vehicle in the formation is different necessarily under different formation formations and different wind disturbance angles, the wind disturbance degree of each unmanned aerial vehicle in each flight period is calculated; because the pre-compensation values required by each unmanned aerial vehicle under different formation, wind direction, flight trajectory and different wind disturbance degrees are different, after the wind disturbance degree of each unmanned aerial vehicle in each flight period is obtained, historical flight data of each unmanned aerial vehicle and the wind disturbance degree of each unmanned aerial vehicle in each flight period are input into a Gaussian mixture model, the clustering result of each unmanned aerial vehicle is output, each clustering result comprises a plurality of clusters, and a wind disturbance degree parameter is added when the Gaussian mixture model is clustered, so that the flight periods with approximately the same wind disturbance degree of each unmanned aerial vehicle are divided into the same cluster;

then, obtaining a theoretical obstacle avoidance track calculated by a medium-length machine in the formation of the unmanned aerial vehicles at the moment when the medium-length machine meets the obstacle, taking the direction of the flight of the formation of the unmanned aerial vehicles at the next moment when the formation of the unmanned aerial vehicles meets the obstacle as the theoretical flight direction, and taking the space included angle between the theoretical flight direction and the wind direction of the formation of the unmanned aerial vehicles when the formation of the unmanned aerial vehicles meets the obstacle as the predicted included angle of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles; because the theoretical obstacle avoidance track does not consider track deviation, the predicted included angle needs to be input into the clustering result of each unmanned aerial vehicle, and the cluster in which the predicted included angle is classified in the clustering result of each unmanned aerial vehicle is obtained; when the Gaussian mixture model is used for clustering, the input parameters comprise the maximum offset of each unmanned aerial vehicle in each flight time interval, the average value of the maximum offset contained in the classified cluster corresponding to each unmanned aerial vehicle and used as the pre-compensation value of each unmanned aerial vehicle, because the difference of each parameter contained in the same cluster is small, namely the included angle between the flight direction and the wind direction space in each flight time interval contained in the same cluster and the difference of the parameter of the maximum offset in each flight time interval are small, the average value of the maximum offset contained in the classified cluster corresponding to each unmanned aerial vehicle and used as the pre-compensation value of each unmanned aerial vehicle and the final offset correction value of the theoretical obstacle avoidance track are obtained according to the pre-compensation value of each unmanned aerial vehicle, so that the final offset value is more suitable for the pre-compensation requirement of each unmanned aerial vehicle, and the final offset correction value is used for correcting the theoretical obstacle avoidance track to obtain the final obstacle correction track of the unmanned aerial vehicle formation.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic diagram illustrating general steps of an embodiment of a method for controlling formation and obstacle avoidance of an unmanned aerial vehicle based on a laser radar;

FIG. 2 is a schematic diagram of the transmission and reception of infrared beams after the track deviation of the beam expander according to the present invention;

fig. 3 is a schematic view of the distance between the straightening bench and the obstacle according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The embodiment of the invention relates to an unmanned aerial vehicle formation obstacle avoidance control method based on a laser radar, which comprises the following steps as shown in figure 1:

s1, obtaining historical flight data of each unmanned aerial vehicle in the unmanned aerial vehicle formation before the unmanned aerial vehicles start flying until the unmanned aerial vehicles meet obstacles.

Every unmanned aerial vehicle in the unmanned aerial vehicle formation can carry on the anemoscope, the anemoscope is an anemoscope utilizing the ultrasonic resonance principle, and is used for monitoring the wind speed and the wind direction number distance of a low-altitude area.

The air density of unmanned aerial vehicle position can acquire through the barometer, because highly can influence the change of atmospheric pressure, can acquire the atmospheric pressure at different flying height places through the barometer, and then calculates air density value according to atmospheric pressure, and the computational formula of air density value among the prior art is: density of air

And the air density of the unmanned aerial vehicle at different flight heights can be obtained according to the atmospheric pressure at different flight heights obtained by the barometer, wherein the atmospheric pressure is 1.293 (actual atmospheric pressure/standard physical atmospheric pressure) (273/thermodynamic temperature), and the thermodynamic temperature is = centigrade + 273.

The historical flight data before each unmanned aerial vehicle starts to fly until meeting the obstacle, including the real-time wind direction of each unmanned aerial vehicle flight in-process, the real-time flight direction of each unmanned aerial vehicle flight in-process, the real-time air density of each unmanned aerial vehicle position, the real-time moving speed of each unmanned aerial vehicle in the flight direction, the correction power when each unmanned aerial vehicle is in real time to resist the windage in the flight process, the real-time offset correction time acceleration of each unmanned aerial vehicle flight in-process, the real-time flight time of each unmanned aerial vehicle, the offset of each unmanned aerial vehicle in the flight process of deviating from the flight path in real time.

S2, dividing the flight time of each unmanned aerial vehicle from the beginning of flying to the time before the unmanned aerial vehicle meets an obstacle into a plurality of flight time intervals according to the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space in the historical flight data; wherein, every unmanned aerial vehicle flight direction and wind direction space contained angle remain unchanged in every flight period.

The calculation step of the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space in each flight period comprises the following steps: establishing a three-dimensional space coordinate system; wherein, the direction from the west to the east is taken as the X axis of the three-dimensional space coordinate system, the direction from the south to the north is taken as the Y axis of the three-dimensional space coordinate system, and the height direction is taken as the Z axis of the three-dimensional space coordinate system; calculating included angles between the flight direction of each unmanned aerial vehicle and the wind direction in the X axis, the Y axis and the Z axis in the three-dimensional space coordinate system in each flight period according to the angle components of the wind direction in the X axis, the Y axis and the Z axis in the three-dimensional space coordinate system in each flight period and the angle components of the flight direction of each unmanned aerial vehicle in the X axis, the Y axis and the Z axis in the three-dimensional space coordinate system in each flight period; and calculating included angles and values of the flight direction and the wind direction of each unmanned aerial vehicle in each flight period in an X axis, a Y axis and a Z axis, and taking the Euclidean norm of the included angles and values as the included angle between the flight direction and the wind direction space of each unmanned aerial vehicle in each flight period.

Because the included angles of the flight direction and the wind direction space of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles are the same, after a three-dimensional space coordinate system is established, the unmanned aerial vehicles are formed into the formation

The first of an unmanned plane

The flight period is taken as an example, the first

The individual unmanned plane is on the second

The calculation formula of the included angle between the flight direction and the wind direction in the X axis in each flight period is as follows:

wherein the content of the first and second substances,

denotes the first

The individual unmanned plane is on the second

The included angle between the flight direction and the wind direction in the X axis in each flight period;

denotes the first

The individual unmanned plane is on the second

The angular component of the wind direction and the X-axis for each flight period;

denotes the first

The individual unmanned plane is on the second

The angular component of the flight direction and the X axis during each flight period;

indicating the absolute value sign.

In a calculation formula of an included angle between the wind direction of each unmanned aerial vehicle in each flight period and the flight direction on the X axis, the angular component between the wind direction of each unmanned aerial vehicle in each flight period and the X axis and the absolute value of the difference between the angular component between the flight direction of each unmanned aerial vehicle in each flight period and the X axis are used as the included angle between the flight direction of each unmanned aerial vehicle in each flight period and the wind direction on the X axis.

First, the

An unmanned aerial vehicle is in

The calculation formula of the included angle between the wind direction of each flight period and the flight direction of the unmanned aerial vehicle formation in the Y axis is as follows:

wherein the content of the first and second substances,

denotes the first

The individual unmanned plane is on the second

The included angle between the flight direction and the wind direction in the Y axis in each flight period;

denotes the first

The individual unmanned plane is on the second

The angular component of the wind direction and the Y axis for each flight period;

denotes the first

The individual unmanned plane is on the second

The angular component of the direction of flight and the Y axis for each flight interval;

indicating the absolute value sign.

In a calculation formula of an included angle between the wind direction of each unmanned aerial vehicle in each flight period and the flight direction in the Y axis, the difference value between the angular component between the wind direction of each unmanned aerial vehicle in each flight period and the Y axis and the angular component between the flight direction of each unmanned aerial vehicle in each flight period and the Y axis is used as the included angle between the flight direction of each unmanned aerial vehicle in each flight period and the wind direction in the Y axis.

First, the

The individual unmanned plane is on the second

The calculation formula of the included angle between the wind direction of each flight period and the flight direction of the unmanned aerial vehicle formation on the Z axis is as follows:

wherein, the first and the second end of the pipe are connected with each other,

is shown as

An unmanned aerial vehicle is in

The included angle between the flight direction and the wind direction in the Z axis in each flight period;

is shown as

An unmanned aerial vehicle is in

The angular component of the wind direction and the Z axis during each flight period;

is shown as

An unmanned aerial vehicle is in

The angle component of the flight direction and the Z axis in each flight period;

indicating the absolute value sign.

In a calculation formula of an included angle between the wind direction of each unmanned aerial vehicle in each flight period and the flight direction in the Z axis, the difference value between the angular component between the wind direction of each unmanned aerial vehicle in each flight period and the Z axis and the angular component between the flight direction of each unmanned aerial vehicle in each flight period and the Z axis is used as the included angle between the flight direction of each unmanned aerial vehicle in each flight period and the wind direction in the Z axis.

According to the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space in the historical flight data, dividing the flight time of each unmanned aerial vehicle from the beginning of flight to the time before the unmanned aerial vehicle meets the obstacle into a plurality of flight time periods; the included angle between the flying direction of each unmanned aerial vehicle and the wind direction space in each flying time interval is kept unchanged;

first, the

An unmanned aerial vehicle is in

Included angle values of the flight direction and the wind direction on an X axis, a Y axis and a Z axis in each flight period are respectively

、

、

Then to the first

The individual unmanned plane is on the second

The calculation formula of the included angle between the flight direction and the wind direction space in each flight time interval is as follows:

wherein the content of the first and second substances,

is shown as

An unmanned aerial vehicle is in

The flight direction and the wind direction space included angle in each flight period;

is shown as

The individual unmanned plane is on the second

The included angle between the flight direction and the wind direction in the X axis in each flight period;

denotes the first

The individual unmanned plane is on the second

The included angle between the flight direction and the wind direction in the Y axis in each flight period;

denotes the first

The individual unmanned plane is on the second

The included angle between the flight direction and the wind direction in the Z axis in each flight period;

expressing a Euclidean norm symbol;

in the first place

The individual unmanned plane is on the second

In the calculation formula of the included angle between the flight direction and the wind direction space in each flight time interval, the first step is known

An unmanned aerial vehicle is in

The included angle values of the flight direction and the wind direction in the X axis, the Y axis and the Z axis at each flight time are respectively

、

、

Under the condition of (1), calculating the second by using an Euclidean norm calculation formula

The individual unmanned plane is on the second

The included angle between the flight direction and the wind direction space in each flight time interval is a calculation formula in the prior art.

S3, obtaining a theoretical wind resistance value of each unmanned aerial vehicle in each flight period.

The step of obtaining the theoretical wind resistance value that each unmanned aerial vehicle receives in each flight period includes: according to the average movement speed of each unmanned aerial vehicle obtained according to historical flight data in each flight period along the flight direction, the average air density of the flight height of the unmanned aerial vehicle, the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space and the frontal windward area of each unmanned aerial vehicle, the theoretical wind resistance value of each unmanned aerial vehicle in each flight period is calculated.

Because each unmanned aerial vehicle in the unmanned aerial vehicle formation carries the storage recording module, the storage recording module can store the movement speed of the unmanned aerial vehicle along the flight direction, and because each unmanned aerial vehicle has the corresponding movement speed at each flight time, the mean value of the movement speeds at all the flight times contained in each flight time interval is taken as the average movement speed along the flight direction in each flight time interval; because each unmanned aerial vehicle has the corresponding air density of the flying height at each flying time, the average value of the air densities corresponding to all flying times contained in each flying period is taken as the average air density of the flying height in each flying period.

Because in same flight period, the wind direction is fixed with unmanned aerial vehicle flight direction, consequently think that in same flight period, the skew that unmanned counterwork windage was done is corrected to the even rectilinear motion that accelerates, and the counterwork direction is the opposite direction of wind direction promptly.

The calculation formula of the theoretical wind resistance value of each unmanned aerial vehicle in each flight period is as follows:

wherein the content of the first and second substances,

is shown as

An unmanned aerial vehicle is in

The theoretical wind resistance experienced by each flight period;

the air resistance coefficient is a constant value

；

Is shown as

The individual unmanned plane is on the second

Average air density at the flight level of each flight period;

denotes the first

The frontal area of the individual drone;

denotes the first

An unmanned aerial vehicle is in

Average movement velocity in the flight direction over a flight period;

denotes the first

An unmanned aerial vehicle is in

The flight direction and wind direction space included angle in each flight time interval;

to represent

Cosine value of (d).

In the formula for calculating the theoretical wind resistance value to which each drone is subjected during each flight period,

for the first time in formation of unmanned aerial vehicles

The wind resistance of the unmanned aerial vehicle during forward windward flight is a calculation formula of the wind resistance of the unmanned aerial vehicle during forward windward flight in the prior art;

is shown as

An unmanned aerial vehicle is in

The flight direction and wind direction space included angle in each flight time interval; calculating the first time of unmanned aerial vehicle formation by using a calculation formula in the prior art

Wind resistance when the individual unmanned aerial vehicle flies forward windward, and

an unmanned aerial vehicle is in

The included angle between the flight direction and the wind direction space in each flight time interval is calculated

The individual unmanned plane is on the second

The theoretical wind resistance value received when flying along the space included angle in each flight period.

And S4, calculating the wind disturbance degree of each unmanned aerial vehicle in each flight period according to the flight time of each unmanned aerial vehicle in each flight period, the maximum correction power in the process of resisting wind resistance, the maximum acceleration in the process of offset correction and the received theoretical wind resistance value, which are acquired according to historical flight data.

The method for acquiring the wind disturbance degree of each unmanned aerial vehicle in each flight period comprises the following steps: taking a second product of the flight time of each unmanned aerial vehicle in each flight period and the maximum acceleration of each unmanned aerial vehicle during offset correction as the maximum instantaneous speed of each unmanned aerial vehicle in the direction of the counter-windage in each flight period; taking a third ratio of the maximum corrective power of each unmanned aerial vehicle in resisting the wind resistance in each flight period to the maximum instantaneous speed in the direction of resisting the wind resistance as the maximum resistance of each unmanned aerial vehicle in resisting the wind resistance in each flight period; taking a fourth ratio of the maximum opposing force of each unmanned aerial vehicle to the wind resistance in each flight period to the flight time as the average opposing force of each unmanned aerial vehicle to the wind resistance in each flight period in unit time; and taking a fifth ratio of the average counter force of the unit time counter wind resistance of each unmanned aerial vehicle in each flight period to the received theoretical wind resistance value as the wind disturbance degree of each unmanned aerial vehicle in each flight period.

When the unmanned aerial vehicles in the unmanned aerial vehicle formation are influenced by the outside and have a tendency of rising or falling in height, the control unit in the unmanned aerial vehicle adjusts the power of the motor of the unmanned aerial vehicle to perform reverse motion compensation, if the unmanned aerial vehicle has a tendency of being blown away from a flight track transversely by wind or has deviated, the control unit of the unmanned aerial vehicle can start a side flight mode to offset the side flight mode, the time required by deviation correction of the unmanned aerial vehicle and the consumed power of the unmanned aerial vehicle are mainly related to different degrees of wind disturbance of the unmanned aerial vehicles at different positions of the unmanned aerial vehicle formation, when each flight period is fixed, the unmanned aerial vehicle starts to make counter acceleration from 0 and finishes the change of the flight period, and the maximum acceleration during the deviation correction can exist in the whole flight period; the deviation is resisted in the whole flight period, power is consumed in the deviation resisting process, and the power consumed for resisting the deviation in the whole flight period is accumulated, namely the maximum correcting power of each unmanned aerial vehicle in resisting wind resistance in each flight period.

Therefore, the wind disturbance degree of each unmanned aerial vehicle in each flight period is calculated according to the flight time of each unmanned aerial vehicle in each flight period after the start of flight, the maximum correction power of each unmanned aerial vehicle in each flight period when the unmanned aerial vehicle resists wind resistance, the maximum acceleration of each unmanned aerial vehicle in each flight period when the unmanned aerial vehicle deviates from correction and the theoretical wind resistance value of each unmanned aerial vehicle in each flight period, which are recorded by the storage and recording module when the unmanned aerial vehicle flies.

The calculation formula of the wind disturbance degree of each unmanned aerial vehicle in each flight period is as follows:

wherein the content of the first and second substances,

is shown as

An unmanned aerial vehicle is in

The wind disturbance degree suffered by each flight period;

denotes the first

The individual unmanned plane is on the second

Maximum corrective power against wind resistance for each flight period;

is shown as

An unmanned aerial vehicle is in

Maximum acceleration at each flight interval offset correction;

denotes the first

An unmanned aerial vehicle is in

Time of flight within each flight period;

is shown as

The individual unmanned plane is on the second

The theoretical wind resistance experienced by each flight period.

Each unmanned aerial vehicle is subjected to in each flight periodIn the calculation formula of the wind disturbance degree,

is of the original formula

Wherein, in the step (A),

indicating the first deviation in the entire correction process

The individual unmanned plane is on the second

Maximum instantaneous speed in the direction of the opposing windage during each flight period;

denotes the first

The individual unmanned plane is on the second

Maximum corrective power against windage during one flight period, divided by

An unmanned aerial vehicle is in

The maximum instantaneous speed along the direction of the wind resistance in each flight period is obtained

The individual unmanned plane is on the second

Maximum opposing force to wind resistance in each flight period;

then it indicates the first

An unmanned aerial vehicle is in

Average resistance to wind resistance per unit time over a flight period, i.e.

Denotes the first

An unmanned aerial vehicle is in

Average opposing force of opposing wind resistance per unit time within each flight period; the theoretical wind resistance values of the unmanned aerial vehicles with the same size in the unmanned aerial vehicle formation in the same flight time interval are the same, and when the theoretical wind resistance values are the same, the unmanned aerial vehicles in the unmanned aerial vehicle formation in the first flight time interval are

An unmanned aerial vehicle is in

The greater the average counter force against the wind resistance per unit time in the flight period, the greater the mean counter force

An unmanned aerial vehicle is in

The greater the degree of wind disturbance experienced during an individual flight period.

S5, inputting historical flight data of each unmanned aerial vehicle and the wind disturbance degree of each unmanned aerial vehicle in each flight period into a Gaussian mixture model, and outputting a clustering result of each unmanned aerial vehicle; the historical flight data of each unmanned aerial vehicle comprises the maximum offset of each unmanned aerial vehicle in each flight period, and the clustering result of each unmanned aerial vehicle comprises a plurality of clusters.

Inputting historical flight data of each unmanned aerial vehicle and the wind disturbance degree of each unmanned aerial vehicle in each flight time interval into a Gaussian mixture model, wherein due to the fact that the wind disturbance degree of each unmanned aerial vehicle in each flight time interval is added, when a pre-compensation value of a theoretical obstacle avoidance track is obtained, the compensation requirement of each unmanned aerial vehicle is met, the compensation effect on the theoretical obstacle avoidance track is low in accuracy, and further the reasonable obstacle avoidance track cannot be obtained; under different formation formations and different wind disturbance angles, the wind disturbance degree that each unmanned aerial vehicle received in the formation is different certainly, therefore different unmanned aerial vehicles required precompensation value is different under different formation, wind direction, flight track, need begin to take off and calculate to the unmanned aerial vehicle wind disturbance degree of current all flight periods to in substituting it into the gaussian mixture model, make the prediction result more laminate each unmanned aerial vehicle's precompensation demand.

Inputting historical flight data of each unmanned aerial vehicle and the wind disturbance degree of each unmanned aerial vehicle in each flight period into a Gaussian mixture model, and updating three parameters of the Gaussian mixture model by using the clustering process of the Gaussian mixture model as the prior art: the method comprises the steps of mixing coefficients, mean vector and covariance matrix, calculating posterior probability of mixed components of each unmanned aerial vehicle in each flight period (the mixed components are parameters of each unmanned aerial vehicle in each flight period and include (wind resistance, wind speed, included angle between flight direction and wind direction space, maximum offset, wind disturbance degree and the like), updating model parameters in sequence, classifying data with the same parameters into the same cluster by a Gaussian mixture model, enabling a clustering result of each unmanned aerial vehicle to comprise a plurality of clusters, inputting historical flight data of each unmanned aerial vehicle and wind disturbance degree of each unmanned aerial vehicle in each flight period into the Gaussian mixture model, and outputting the clustering result of each unmanned aerial vehicle on the whole flight data.

S6, obtaining a theoretical obstacle avoidance track calculated by the medium-length unmanned aerial vehicle formation machine when meeting the obstacle, taking the direction of the unmanned aerial vehicle formation when meeting the obstacle at the next moment according to the theoretical obstacle avoidance track as a theoretical flight direction, and taking the space included angle between the theoretical flight direction and the wind direction of the unmanned aerial vehicle formation when meeting the obstacle as a predicted included angle of each unmanned aerial vehicle in the unmanned aerial vehicle formation.

The method for acquiring the theoretical obstacle avoidance track calculated when the medium and long unmanned aerial vehicles in the formation meet the obstacle comprises the following steps: the method comprises the steps of obtaining the offset of track deviation of long and medium-sized aircrafts in a formation of unmanned aerial vehicles when encountering an obstacle, and obtaining the distance between the long aircraft and the obstacle measured when the long aircraft generates track deviation; calculating the distance between the corrected long machine and the obstacle by using the offset of the long machine which generates track deviation when the long machine encounters the obstacle and the distance between the long machine and the obstacle which is measured when the long machine generates track deviation; and acquiring a theoretical obstacle avoidance track by using a radar obstacle avoidance system carried by the long machine according to the distance between the long machine and the obstacle after correction.

Because the formation modes are different, one or more than one long machine can be provided, as shown in fig. 2, the long machine is a schematic diagram of transmitting infrared beams and receiving infrared beams after the track deviation occurs; the laser emitter that the long machine carried can launch infrared light beam, when meetting the barrier on predetermineeing the flight path, can return to laser receiver through diffuse reflection, radar module multiplies the velocity of light according to the time interval of sending with received signal, divide by 2 again, can calculate the distance of transmitter and object, point cloud data through discernment barrier obtains the orbit of detouring, and the ROS communication mechanism that adopts can realize reading and control of many unmanned aerial vehicle cluster mutual data, and the formation transform and the perception of unmanned aerial vehicle network deployment are in coordination.

Then the long machine identifies the obstacle in the point cloud data collected on the preset flight track in the front, firstly the point cloud data motion deviation caused by the self offset needs to be removed, then the obstacle avoidance flight track of the whole unmanned aerial vehicle formation is obtained through calculation, and the theoretical obstacle avoidance track is prevented from being transmitted to other unmanned aerial vehicles in the formation.

The long machine takes place the offset that the track skew takes place for the long machine of storage record module when skew, when long machine discernment barrier, with the unmanned aerial vehicle formation well long machine when meetting the barrier constantly as keeping away the barrier moment, acquire the offset that the track skew takes place for the long machine at the moment of keeping away the barrier, acquire long machine simultaneously and take place the track skew gained and the barrier distance, the offset that the track skew takes place for the long machine at the moment of utilizing keeping away the barrier, and long machine takes place the track skew gained and the barrier distance, calculate and correct back long machine and barrier distance.

The calculation formula of the distance between the corrected long machine and the obstacle is as follows:

wherein the content of the first and second substances,

showing the distance between the straightening tractor and the obstacle;

the distance between the long machine and the obstacle is identified by the infrared beam received by the laser receiver when the track of the long machine deviates;

representing the included angle between the receiving direction of the laser receiver and the flight track direction of the long plane;

to represent

Cosine value of (d);

to represent

The sine value of (d);

representing the offset of the track deviation of the long machine at the obstacle avoidance moment; after correction, it is longCalculating the distance between the machine and the obstacle;

、

respectively representing two right-angle side lengths, substituting into Pythagorean theorem to obtain the distance between the corrected long machine and the obstacle

。

As shown in fig. 3, which is a schematic diagram of a distance between a corrected long machine and an obstacle in the present invention, a calculation formula of the distance between the corrected long machine and the obstacle performs a correlation calculation according to the pythagorean theorem in the prior art, and the corrected obstacle direction can also be obtained by using the pythagorean theorem; after the long machine obtains the distance between the corrected long machine and the obstacle and the direction of the corrected obstacle, a plurality of theoretical obstacle avoidance tracks can be obtained according to the original radar obstacle avoidance system carried by the long machine, and the theoretical obstacle avoidance tracks are obtained by calculation according to the original radar obstacle avoidance system carried by the unmanned aerial vehicle, and the method is the prior art.

After receiving the theoretical obstacle avoidance track sent by the long machine, other unmanned aerial vehicles also need to perform flight track pre-compensation according to respective historical flight data, that is, in the line of the theoretical obstacle avoidance track, there is also an offset caused by wind disturbance, and in case that the theoretical obstacle avoidance track is too close to an obstacle due to a small offset, danger may occur, so that the theoretical obstacle avoidance track needs to be pre-compensated.

After the theoretical obstacle avoidance track is obtained, the flying direction of the unmanned aerial vehicle formation in the obstacle avoidance track at the next moment is used as the theoretical flying direction, and the space included angle between the theoretical flying direction and the wind direction of the unmanned aerial vehicle formation in the obstacle avoidance track is used as the prediction included angle.

And S7, obtaining the cluster of each predicted included angle of each unmanned aerial vehicle classified in the clustering result of the corresponding unmanned aerial vehicle, and taking the maximum offset mean value contained in the classified cluster corresponding to each unmanned aerial vehicle as the pre-compensation value of each unmanned aerial vehicle.

After the predicted included angle is obtained, the predicted included angle is matched with the clustering result of each unmanned aerial vehicle, one unmanned aerial vehicle is selected as a target unmanned aerial vehicle, the predicted included angle is matched with the clustering result of the target unmanned aerial vehicle, the clustering result of the target unmanned aerial vehicle corresponding to the maximum posterior probability is used as a cluster in which the predicted included angle is classified in the clustering result of the unmanned aerial vehicle, and the process of matching one parameter in the known cluster with the clustering result is the prior art and is not repeated herein.

Adding and averaging all the maximum offsets contained in the classified clusters corresponding to the target unmanned aerial vehicle to obtain the precompensation value of the target unmanned aerial vehicle, and obtaining the precompensation value of each unmanned aerial vehicle in the same way

Wherein, in the process,

denotes the first

Pre-compensation value of each drone.

And S8, obtaining a final offset correction value of the theoretical obstacle avoidance track according to the pre-compensation value of each unmanned aerial vehicle, and correcting the theoretical obstacle avoidance track by using the final offset correction value to obtain the final obstacle avoidance track of the unmanned aerial vehicle formation.

The step of obtaining the final offset correction value of the theoretical obstacle avoidance track according to the pre-compensation value of each unmanned aerial vehicle comprises the following steps: calculating the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation by utilizing the wind disturbance degree of each unmanned aerial vehicle in all flight periods; weighting the pre-compensation value of each unmanned aerial vehicle by utilizing the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation to obtain a weighted pre-compensation value of each unmanned aerial vehicle; and adding the weighted pre-compensation values of all unmanned aerial vehicles in the unmanned aerial vehicle formation to obtain a final offset correction value of the obstacle avoidance track.

Because each unmanned aerial vehicle different positions in the formation, for example the unmanned aerial vehicle of formation edge, head and the tail, central point put, at whole flight in-process, the wind that receives disturbs or more stable or undulant great, the unmanned aerial vehicle of edge position is more sensitive to the wind direction, the wind degree of disturbing undulant great when the wind direction changes, and the unmanned aerial vehicle of central point position, no matter the wind direction changes the unmanned aerial vehicle that all can pass through edge position earlier, therefore its wind degree of disturbing has fluctuated lessly.

Therefore, the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation needs to be calculated, meanwhile, the pre-compensation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation is weighted by the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle to obtain the weighted post-pre-compensation value of each unmanned aerial vehicle, so that the correction result of the theoretical obstacle avoidance track is fitted with the offset of a single unmanned aerial vehicle, the influence of the offset fluctuation of unmanned aerial vehicles at different positions in the formation on the correction result is avoided, and the safety of the unmanned aerial vehicle formation when the obstacle avoidance command is executed is greatly improved.

The method for calculating the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles by utilizing the wind disturbance degree of each unmanned aerial vehicle in all flight periods comprises the following steps: the maximum wind disturbance degree of each unmanned aerial vehicle in all flight periods is used as the maximum wind disturbance degree, and the minimum wind disturbance degree of each unmanned aerial vehicle in all flight periods is used as the minimum wind disturbance degree; taking a first ratio of the maximum wind disturbance degree and the minimum wind disturbance degree as a ratio of wind disturbance degree extreme values suffered by each unmanned aerial vehicle; calculating the fluctuation entropy of the wind disturbance degree of each unmanned aerial vehicle in all flight periods by utilizing the wind disturbance degree of each unmanned aerial vehicle; calculating a first product of the ratio of the wind disturbance degree extreme value suffered by each unmanned aerial vehicle and the wind disturbance degree fluctuation entropy value suffered by the unmanned aerial vehicle; and carrying out normalization processing on the first product corresponding to each unmanned aerial vehicle to obtain the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation.

The calculation formula of the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation is as follows:

wherein, the first and the second end of the pipe are connected with each other,

indicating first in formation of drones

Historical wind disturbance degree fluctuation values of the individual unmanned aerial vehicles;

is shown as

The value of each unmanned aerial vehicle in all flight periods is

The number of occurrences of historical wind disturbance levels;

denotes the first

The total number of all flight periods of each drone;

is shown as

The total type number of the interference degree value results of each unmanned aerial vehicle in all flight periods;

representing a natural logarithm;

denotes the first

The maximum wind disturbance degree of each unmanned aerial vehicle in all flight periods;

is shown as

The minimum wind disturbance degree of each unmanned aerial vehicle in all flight periods;

representing a hyperbolic tangent function;

representing a rounding symbol.

In the calculation formula of the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles,

the data are the fluctuation entropy value of the disturbance degree in the historical flight data of the ith unmanned aerial vehicle, the entropy value can be used for evaluating the data confusion,

the maximum wind disturbance degree in the historical flight data of the ith unmanned aerial vehicle is greater than the minimum wind disturbance degree, namely the severe condition of data fluctuation, and the maximum wind disturbance degree is used as the influence coefficient of the fluctuation entropy of the wind disturbance degree, the maximum wind disturbance degree and the minimum wind disturbance degree are multiplied by each other,

the larger the size is, the larger the fluctuation of the wind disturbance degree representing the historical flight data of the ith unmanned aerial vehicle is, and then the hyperbolic tangent function is utilized

Performing direct proportional normalization, i.e.

The larger the size of the hole is,

the larger the value between 0 and 1 is,

the historical wind disturbance degree fluctuation value of the ith unmanned aerial vehicle is represented.

The method for weighting the pre-compensation value of each unmanned aerial vehicle to obtain the weighted pre-compensation value of each unmanned aerial vehicle by utilizing the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation comprises the following steps: calculating the sum of the historical wind disturbance degree fluctuation values of all unmanned aerial vehicles in the formation of the unmanned aerial vehicles; taking a second ratio of the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle to the sum of the historical wind disturbance degree fluctuation values as a weight coefficient of a pre-compensation value of the unmanned aerial vehicle; and multiplying the pre-compensation value of each unmanned aerial vehicle by the weight coefficient of the pre-compensation value of the unmanned aerial vehicle to obtain the weighted pre-compensation value of each unmanned aerial vehicle.

For each drone

Except for all drones

And summing to obtain a normalization result of the unmanned aerial vehicle in all the historical wind disturbance degree fluctuation values, taking the normalization result as a weight coefficient of an offset compensation value, and performing weighting calculation to obtain a final correction result of the theoretical obstacle avoidance track.

Wherein, the first and the second end of the pipe are connected with each other,

representing a final offset correction value;

indicating first in formation of drones

Historical wind disturbance degree fluctuation values of the individual unmanned aerial vehicles;

indicating first in formation of drones

A precompensation value for each drone;

representing the total number of the unmanned aerial vehicles in the unmanned aerial vehicle formation;

in the calculation formula of the final offset correction value,

is composed of

The weight coefficient of the unmanned aerial vehicle is that the unmanned aerial vehicle at different positions in the formation, such as the unmanned aerial vehicle at the edge, the head and the tail and the central position of the formation, is stable or fluctuates greatly in the whole flying process, the unmanned aerial vehicle at the edge position is more sensitive to the wind direction, the fluctuation of the wind disturbance degree is large when the wind direction changes, and the unmanned aerial vehicle at the central position passes through the unmanned aerial vehicle at the edge position firstly no matter the wind direction changes, so that the fluctuation of the wind disturbance degree is small, and the unmanned aerial vehicle with the large fluctuation value of the historical wind disturbance degree is distributed with large weight;

is to show to

Weighting the offset compensation value of the unmanned aerial vehicle to obtain the second

Weighting of individual dronesPost pre-compensation value;

the weighted precompensation values of all the unmanned aerial vehicles in the unmanned aerial vehicle formation are added to obtain a final offset correction value of the obstacle avoidance track

(ii) a And after the final offset correction value is obtained, theoretical obstacle avoidance track correction is carried out by using the final offset correction value, and the final offset correction value is superposed to the opposite offset direction to complete pre-compensation.

The invention provides an unmanned aerial vehicle formation obstacle avoidance control method based on a laser radar, and aims to solve the problems that when deviation compensation is carried out by a Gaussian mixture model used for deviation of a traditional unmanned aerial vehicle formation track, flight data based on the whole unmanned aerial vehicle formation is input into the Gaussian mixture model to obtain a pre-compensation value, so that the compensation effect is not ideal enough, and the actual compensation requirements of all unmanned aerial vehicles in the unmanned aerial vehicle formation are not met.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.

Claims (8)

1. An unmanned aerial vehicle formation obstacle avoidance control method based on a laser radar is characterized by comprising the following steps:

acquiring historical flight data of each unmanned aerial vehicle in the unmanned aerial vehicle formation before each unmanned aerial vehicle starts to fly until encountering an obstacle;

according to the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space in the historical flight data, dividing the flight time of each unmanned aerial vehicle from the beginning of flight to the time before the unmanned aerial vehicle meets the obstacle into a plurality of flight time periods; the included angle between the flight direction of each unmanned aerial vehicle and the wind direction space in each flight period is kept unchanged;

acquiring a theoretical wind resistance value of each unmanned aerial vehicle in each flight period;

calculating the wind disturbance degree of each unmanned aerial vehicle in each flight period according to the flight time of each unmanned aerial vehicle in each flight period, the maximum correction power in resisting wind resistance, the maximum acceleration in deviation correction and the received theoretical wind resistance value, which are acquired according to historical flight data;

inputting historical flight data of each unmanned aerial vehicle and the wind disturbance degree of each unmanned aerial vehicle in each flight period into a Gaussian mixture model, and outputting a clustering result of each unmanned aerial vehicle; the historical flight data of each unmanned aerial vehicle comprises the maximum offset of each unmanned aerial vehicle in each flight period, and the clustering result of each unmanned aerial vehicle comprises a plurality of clusters;

obtaining a theoretical obstacle avoidance track calculated by a medium-length machine in a formation of unmanned aerial vehicles at the moment of meeting an obstacle, taking the direction of flying of the formation of the unmanned aerial vehicles according to the theoretical obstacle avoidance track at the next moment of meeting the obstacle as a theoretical flying direction, and taking a space included angle between the theoretical flying direction and the wind direction of the formation of the unmanned aerial vehicles when meeting the obstacle as a predicted included angle of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles;

acquiring a cluster of each unmanned aerial vehicle, into which a predicted included angle is classified, in a clustering result corresponding to the unmanned aerial vehicle, and taking a maximum offset mean value contained in the classified cluster corresponding to each unmanned aerial vehicle as a pre-compensation value of each unmanned aerial vehicle;

and obtaining a final offset correction value of the theoretical obstacle avoidance track according to the precompensation value of each unmanned aerial vehicle, and correcting the theoretical obstacle avoidance track by using the final offset correction value to obtain the final obstacle avoidance track of the unmanned aerial vehicle formation.

2. The method for controlling formation of unmanned aerial vehicles based on laser radar to avoid obstacles according to claim 1, wherein the step of obtaining a final offset correction value of the theoretical obstacle avoidance trajectory according to the pre-compensation value of each unmanned aerial vehicle comprises:

calculating the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation by utilizing the wind disturbance degree of each unmanned aerial vehicle in all flight periods;

weighting the pre-compensation value of each unmanned aerial vehicle by utilizing the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation to obtain a weighted pre-compensation value of each unmanned aerial vehicle;

and adding the weighted pre-compensation values of all unmanned aerial vehicles in the unmanned aerial vehicle formation to obtain a final offset correction value of the obstacle avoidance track.

3. The method for controlling obstacle avoidance in formation of unmanned aerial vehicles based on lidar according to claim 2, wherein the step of calculating the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the formation of unmanned aerial vehicles by using the wind disturbance degree of each unmanned aerial vehicle in all flight periods comprises:

the maximum wind disturbance degree of each unmanned aerial vehicle in all flight periods is used as the maximum wind disturbance degree, and the minimum wind disturbance degree of each unmanned aerial vehicle in all flight periods is used as the minimum wind disturbance degree;

taking a first ratio of the maximum wind disturbance degree and the minimum wind disturbance degree as a ratio of wind disturbance degree extreme values suffered by each unmanned aerial vehicle;

calculating the fluctuation entropy of the wind disturbance degree of each unmanned aerial vehicle in all flight periods by utilizing the wind disturbance degree of each unmanned aerial vehicle;

calculating a first product of the ratio of the wind disturbance degree extreme value suffered by each unmanned aerial vehicle and the wind disturbance degree fluctuation entropy value suffered by the unmanned aerial vehicle;

and carrying out normalization processing on the first product corresponding to each unmanned aerial vehicle to obtain the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the unmanned aerial vehicle formation.

4. The method for controlling obstacle avoidance in formation of unmanned aerial vehicles based on lidar according to claim 2, wherein the step of weighting the pre-compensation value of each unmanned aerial vehicle to obtain the weighted pre-compensation value of each unmanned aerial vehicle by using the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle in the formation of unmanned aerial vehicles comprises:

calculating the sum of the historical wind disturbance degree fluctuation values of all unmanned aerial vehicles in the formation of the unmanned aerial vehicles;

taking a second ratio of the historical wind disturbance degree fluctuation value of each unmanned aerial vehicle to the sum of the historical wind disturbance degree fluctuation values as a weight coefficient of a precompensation value of the unmanned aerial vehicle;

and multiplying the pre-compensation value of each unmanned aerial vehicle by the weight coefficient of the pre-compensation value of the unmanned aerial vehicle to obtain the weighted pre-compensation value of each unmanned aerial vehicle.

5. The unmanned aerial vehicle formation obstacle avoidance control method based on the laser radar as claimed in claim 1, wherein the step of obtaining the theoretical wind resistance value that each unmanned aerial vehicle receives in each flight period comprises:

according to the average moving speed of each unmanned aerial vehicle in the flying direction, the average air density of the flying height of the unmanned aerial vehicle, the included angle between the flying direction and the wind direction space and the frontal windward area of each unmanned aerial vehicle in each flying period, which are obtained according to historical flying data, the theoretical wind resistance value of each unmanned aerial vehicle in each flying period is calculated.

6. The method for controlling unmanned aerial vehicle formation obstacle avoidance based on lidar of claim 1, wherein the step of obtaining the wind disturbance degree of each unmanned aerial vehicle in each flight period comprises:

taking a second product of the flight time of each unmanned aerial vehicle in each flight period and the maximum acceleration of the unmanned aerial vehicle during offset correction as the maximum instantaneous speed of each unmanned aerial vehicle in the direction of resisting the wind resistance in each flight period;

taking a third ratio of the maximum corrective power of each unmanned aerial vehicle in resisting the wind resistance in each flight period to the maximum instantaneous speed in the direction of resisting the wind resistance as the maximum resistance of each unmanned aerial vehicle in resisting the wind resistance in each flight period;

taking a fourth ratio of the maximum opposing force of each unmanned aerial vehicle to the wind resistance in each flight period to the flight time as the average opposing force of each unmanned aerial vehicle to the wind resistance in each flight period in unit time;

and taking a fifth ratio of the average counter force of the unit time counter wind resistance of each unmanned aerial vehicle in each flight period to the received theoretical wind resistance value as the wind disturbance degree of each unmanned aerial vehicle in each flight period.

7. The unmanned aerial vehicle formation obstacle avoidance control method based on the laser radar as claimed in claim 1, wherein the step of obtaining the theoretical obstacle avoidance track calculated by the long and medium-length machines in the unmanned aerial vehicle formation at the moment of encountering the obstacle comprises:

the method comprises the steps of obtaining the offset of track deviation of long and medium-sized aircrafts in a formation of unmanned aerial vehicles when encountering an obstacle, and obtaining the distance between the long aircraft and the obstacle measured when the long aircraft generates track deviation;

calculating the distance between the corrected long machine and the obstacle by using the offset of the long machine which generates track deviation when the long machine encounters the obstacle and the distance between the long machine and the obstacle which is measured when the long machine generates track deviation;

and acquiring a theoretical obstacle avoidance track by using a radar obstacle avoidance system carried by the long machine according to the distance between the long machine and the obstacle after correction.

8. The unmanned aerial vehicle formation obstacle avoidance control method based on the laser radar as claimed in claim 1, wherein the step of calculating the included angle between the flight direction and the wind direction space of each unmanned aerial vehicle in each flight period comprises:

establishing a three-dimensional space coordinate system;

calculating included angles of the flight direction of each unmanned aerial vehicle and the wind direction in the X axis, the Y axis and the Z axis in the three-dimensional space coordinate system in each flight period according to the angle components of the wind direction in the three-dimensional space coordinate system in each flight period and the angle components of the flight direction of each unmanned aerial vehicle in the three-dimensional space coordinate system in each flight period in the X axis, the Y axis and the Z axis;

and calculating included angles and values of the flight direction and the wind direction of each unmanned aerial vehicle in each flight period in an X axis, a Y axis and a Z axis, and taking the Euclidean norm of the included angles and values as the included angle between the flight direction and the wind direction space of each unmanned aerial vehicle in each flight period.

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