CN111880574A - Unmanned aerial vehicle collision avoidance method and system - Google Patents

Abstract

The invention discloses a collision avoidance method and system for an unmanned aerial vehicle. The method includes judging whether there is a conflict with the unmanned aerial vehicle according to the information of the unmanned aerial vehicle and surrounding unmanned aerial vehicles; when there is a conflict, communicating with a potential threat unmanned aerial vehicle; determining that there is no threat Man-machine and screening two anti-collision maneuvers; generate a planned trajectory to send to the threat drone and obtain the planned trajectory or flight information of the threat drone; reconstruct the trajectory of the threat drone in the local machine; determine an optimal anti-collision maneuver and Send it to the threat drone, and the rest are used as alternative anti-collision maneuvers; obtain the optimal anti-collision maneuver determined by the threat drone according to the cost function; if there is a collision risk, the optimal anti-collision maneuver is activated according to the minimum avoidance distance value and the time determined by the excitation function. The collision maneuver implements avoidance; based on the optimal collision avoidance maneuver, a new avoidance command is generated and output to complete the collision avoidance. The problem of insufficient dynamic scene processing ability in the prior art is solved, and the processing ability and fault tolerance rate of multi-threat collision avoidance scene and dynamic threat scene are improved.

Description

Method and system for collision avoidance of unmanned aerial vehicle

technical field

The invention relates to the technical field of unmanned aerial vehicles, in particular to a collision avoidance method and system for unmanned aerial vehicles.

Background technique

Collision avoidance strategy based on the principle of "non-interference", the traditional collision avoidance algorithm idea is to perform collision detection on UAVs. Once it is judged that there is a collision threat, the UAV will immediately generate a strategy and perform collision avoidance maneuvers. This idea has the following drawbacks : The processing capability of multi-threat and dynamic threats is insufficient. At the same time, in order to ensure collision avoidance, collision avoidance needs to be carried out for a long time before the collision occurs, so as to improve the fault tolerance rate, which also greatly interferes with the normal movement of the UAV.

In the traditional collision avoidance algorithm, the motion model of the UAV is mostly based on the three-degree-of-freedom (3Dof) motion equation, and the single aircraft is simply regarded as a mass point, without considering its own performance limitations. Human-machine needs to roll, climb and other actions. The conventional motion equation based on three degrees of freedom cannot accurately describe its motion process, but the complex six degree of freedom (6Dof) motion equation has disadvantages such as large amount of calculation.

The traditional geometric algorithm is a collision-free trajectory planning algorithm, which has a good collision avoidance effect for specific collision avoidance scenarios, but also has certain limitations, especially it is difficult to solve the problems of formation collision avoidance and autonomous collision avoidance.

SUMMARY OF THE INVENTION

The present invention provides a collision avoidance method and system for an unmanned aerial vehicle, which are used to overcome the defects of the prior art, such as insufficient processing capability for multi-threat and dynamic threat collision avoidance scenarios, large interference to the normal movement of the unmanned aerial vehicle, etc. Processing capability and fault tolerance of multi-threat collision avoidance scenarios and dynamic threat scenarios.

For achieving the above object, the present invention provides a kind of UAV collision avoidance method, comprises the following steps:

Step 1, according to the obtained navigation information and GPS data of the aircraft and the UAV in the surrounding airspace, determine whether there is a conflict between the aircraft and the UAV in the surrounding airspace;

Step 2, in the case of conflict, establish data link communication between the machine and the potential threat drone; determine the threat drone according to the threat degree;

Screen at least two of a number of predetermined collision avoidance maneuvers based on the threat UAV situation;

Step 3, use the trajectory prediction algorithm to generate the planned trajectory of the aircraft corresponding to the screened anti-collision maneuvers, and send the planned trajectory to the threat drone to realize the occupation of the airspace corresponding to the planned trajectory by the threat drone, And obtain the relevant data or flight information corresponding to the planned trajectory of the threat UAV;

Step 4, reconstruct the planned trajectory of the threat drone according to the relevant data or flight information corresponding to the planned trajectory of the threat drone;

Step 5: Compare multiple combinations of the planned trajectory of the aircraft and the planned trajectory of the threat UAV through the evaluation algorithm, and determine an optimal anti-collision maneuver of the aircraft from the selected anti-collision maneuvers according to the minimum avoidance distance value and pass the data. The chain is sent to the threat drone, and the rest of the screening anti-collision maneuvers are used as alternative anti-collision maneuvers;

At the same time, obtain the optimal anti-collision maneuver of the threat UAV determined according to the cost function;

Step 6, according to the optimal anti-collision maneuver of the local machine and the optimal anti-collision maneuver of the threatening UAV, determine whether there is a collision risk between the local machine and the threatening UAV, and if so, activate the local machine The optimal collision avoidance maneuver of , and the optimal collision avoidance maneuver of the threat UAV implement avoidance.

In order to achieve the above object, the present invention also provides a collision avoidance system for unmanned aerial vehicles, comprising a memory and a processor, wherein the memory stores a collision avoidance program of the unmanned aerial vehicle, and the processor is running the collision avoidance program of the unmanned aerial vehicle. perform the steps of the above method.

The method and system provided by the present invention adopts the collision avoidance idea of "no interference", that is, the interference to the normal flight of the UAV is minimized, and the collision avoidance maneuver is stimulated at the last moment before the collision. The fixed-wing UAV first selects candidate anti-collision strategies in the preset strategy set according to the threat situation. The strategy set of the UAV is set offline in advance based on past experience, and after testing, it can ensure that the collision avoidance strategies are all correct. feasible. Subsequently, the UAV determines the optimal collision avoidance strategy through coordination, and its corresponding planned track is the UAV's reservation for a certain airspace. By planning the escape path of the UAV in advance to ensure that the UAV always has an escape path during the conflict process, it can not only effectively reduce the collision risk of the UAV formation, but also activate the collision avoidance strategy activation time as late as possible. Minimize the interference to fixed-wing UAVs, and can be effectively used in autonomous cooperative fixed-wing UAV formation flight.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained according to the structures shown in these drawings without creative efforts.

Fig. 1 is the working principle diagram of the UAV collision avoidance method proposed in the first embodiment of the invention;

Figure 2 is a schematic diagram of the planned track;

Figure 3(a) is the first geometric situation diagram when a threat UAV appears around the target aircraft;

Figure 3(b) is the second geometric situation when a threat drone appears around the target aircraft;

Fig. 4 is the trajectory prediction diagram based on the 3DOF model;

Figure 5 is a schematic diagram of the planned track;

Figure 6 is the control diagram of the UAV;

Figure 7 is a schematic diagram of track management processing multi-source data;

8 is a schematic diagram of a delay frame;

Fig. 9 is a schematic diagram of motor selection;

10 is a schematic diagram of the calculation of the excitation time of the anti-collision maneuver excitation control module;

Figure 11 is a schematic diagram of the calculation of the anti-collision maneuver excitation control module;

FIG. 12 is a flow chart of the operation of the automatic mid-air collision avoidance system according to an embodiment of the present invention.

The realization, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relationship between various components under a certain posture (as shown in the accompanying drawings). The relative positional relationship, the movement situation, etc., if the specific posture changes, the directional indication also changes accordingly.

In addition, descriptions such as "first", "second", etc. in the present invention are only for descriptive purposes, and should not be construed as indicating or implying their relative importance or implicitly indicating the number of indicated technical features. Thus, a feature delimited with "first", "second" may expressly or implicitly include at least one of that feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined.

In the present invention, unless otherwise expressly specified and limited, the terms "connected", "fixed" and the like should be understood in a broad sense, for example, "fixed" may be a fixed connection, a detachable connection, or an integrated; It can be a mechanical connection, an electrical connection, a physical connection or a wireless communication connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction between the two elements. unless otherwise expressly qualified. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but must be based on the realization by those of ordinary skill in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

Example 1

As shown in accompanying drawing 1, the embodiment of the present invention provides a kind of UAV collision avoidance method, comprises the following steps:

Step 1, according to the obtained navigation information and GPS data of the aircraft and the UAV in the surrounding airspace, determine whether there is a conflict between the aircraft and the UAV in the surrounding airspace;

Step 2, in the case of conflict, establish data link communication between the machine and the potential threat drone; determine the threat drone according to the threat degree;

Screen at least two of a number of predetermined collision avoidance maneuvers based on the threat UAV situation;

The step of determining the threat drone in the step 2 includes:

Step 201, when the threat drone is also equipped with an automatic air collision avoidance system, the threat drone is a cooperative threat drone, and the local machine establishes communication with the cooperative threat drone through a designated data link, and obtains all information. State information sent by cooperative threat drones;

Step 202, when the threat drone is not equipped with an automatic air collision avoidance system, the threat drone is a non-cooperative threat drone, and the local aircraft obtains the state information of the non-cooperative threat drone through radar or other means ;

Step 203: Sort the threat UAV status information from different sources, the data link from the automatic air collision avoidance system has the highest priority, the data link from the non-automatic air collision avoidance system has the next highest priority, and the data from radar and other unknown data source priority last;

Step 204 , in the multi-machine situation, track the intruding UAVs according to the priority and calculate their threat degree, and select the three UAVs with the highest threat degree as the threat aircraft.

The step of screening collision avoidance maneuvers in step 2 includes:

Step 21, by changing the roll angle EA and the overload factor NZ to realize nine anti-collision maneuvers in a predetermined direction;

Step 22: Simplify the motion model of the local machine from a six-degree-of-freedom motion model to a three-degree-of-freedom motion model, and construct the relative motion trajectory of the threat drone from the current position to the closest point CPA in space according to the flight state of the threat drone ;

Step 23: Screen at least two from the nine collision avoidance maneuvers according to the relative motion trajectory.

For example, when the relative motion trajectory is that the threatening drone appears above, left, and backward of the aircraft, and is facing the aircraft from left to right, from front to back, and from top to bottom, the aircraft adopts the upward, collision avoidance maneuver to the left;

When the relative movement trajectory is that the threatening drone appears below, right, and left of the target drone, and is facing the target drone from right to left, back to front, and bottom to top, the aircraft will Take a collision avoidance maneuver up and to the left.

Step 3, use the trajectory prediction algorithm to generate the planned trajectory of the aircraft corresponding to the screened anti-collision maneuvers, and send the planned trajectory to the threat drone to realize the occupation of the airspace corresponding to the planned trajectory by the threat drone, And obtain the relevant data or flight information corresponding to the planned trajectory of the threat UAV;

As shown in Figure 2, the planned trajectory is a cone-shaped region whose size depends on the uncertainty of the predicted flight path and increases with time.

In the step 3, a trajectory prediction algorithm is used to generate planned trajectories corresponding to the screened collision avoidance maneuvers, including:

Step 31, outputting a series of predicted positions corresponding to a predetermined time period according to a preset operation cycle based on the six-degree-of-freedom motion equation;

Step 32, forming a circular area on the cross section passing through the predicted position according to the uncertainty distance of the predicted trajectory around each predicted position;

Step 33 , according to the situation that the uncertain distance increases with time, a conical area with a line connecting the series of predicted positions as the center line is formed.

The step of obtaining the planned trajectory or flight information of the threat drone in the step 3 includes:

Step 301, sending the planned trajectory to the threat drone;

Step 302, when the threat drone is a cooperative drone, the receiving threat drone responds to the predicted trajectory of the local aircraft and generates a planned trajectory corresponding to the screening anti-collision maneuver according to the trajectory prediction algorithm;

When the threat drone is a non-cooperative drone, the acceleration information and speed information of the threat drone are received.

Step 4, reconstruct the planned trajectory of the threat drone according to the relevant data or flight information corresponding to the planned trajectory of the threat drone;

The step 4 includes:

When the relevant data corresponding to the planned trajectory of the threat drone comes from the dedicated data link of the automatic air collision avoidance system (that is, when the threat drone is a cooperative drone), the threat is reconstructed according to the maneuvering flight trajectory information contained in the dedicated data link. UAV trajectory;

When the flight information of the threatening drone comes from a non-automatic air collision avoidance system data link, radar or other unknown data source (i.e. when the threatening drone is a non-cooperative drone):

If the acceleration information is obtained and the target machine is moving with low overload acceleration, use the kinematic Model motion model to predict its motion trajectory;

If the acceleration information cannot be obtained, the motion trajectory is predicted along the expansion of the velocity vector;

If the acceleration information is not available and the speed of the threat aircraft is in a very low range, the ballistic model is used to predict its trajectory.

Step 5: Compare multiple combinations of the planned trajectory of the aircraft and the planned trajectory of the threat UAV through the evaluation algorithm, and determine an optimal anti-collision maneuver of the aircraft from the selected anti-collision maneuvers according to the minimum avoidance distance value and pass the data. The chain is sent to the threat drone, and the rest of the screening anti-collision maneuvers are used as alternative anti-collision maneuvers;

At the same time, obtain the optimal anti-collision maneuver of the threat UAV determined according to the cost function;

The planned trajectory of the native aircraft and the planned trajectory of the threat UAV can form multiple combinations, which are compared using an evaluation algorithm.

The step 5 includes:

Step 51, combining the planned track corresponding to the anti-collision maneuver of the drone with the movement track of the aircraft threatening the reconstruction of the drone;

For the combination of any two motion trajectories of the local aircraft and the threat UAV, the difference between the center distance of the trajectory and the trajectory uncertainty distance UD in the space-time domain is output as the minimum avoidance distance value AD;

Step 52: Determine the preferred collision avoidance maneuver of the local aircraft according to the combination of motion trajectories with the largest minimum avoidance distance value. During the coordination process of UAVs, the communication between UAVs takes a certain amount of time, because UAVs need to reserve waiting time to ensure coordination. In the process of calculating the minimum avoidance distance value, the threat UAVs will continue to circulate and cooperate in a continuous cycle. , each collaboration is based on the update maneuver generated by the latest moment.

Step 6, according to the optimal anti-collision maneuver of the local machine and the optimal anti-collision maneuver of the threatening UAV, determine whether there is a collision risk between the local machine and the threatening UAV, and if so, activate the local machine The optimal collision avoidance maneuver of , and the optimal collision avoidance maneuver of the threat UAV implement avoidance.

The step 6 includes:

Step 6: Determine whether there is a collision risk between the local aircraft and the threatening UAV, and if so, activate the optimal anti-collision maneuver to implement avoidance according to the minimum avoidance distance value and the time determined by the excitation function; the excitation function provides enough time. On the basis of the guard interval, the anti-collision maneuver excitation time is the latest target;

The step 6 includes:

Step 61, according to the optimal anti-collision maneuver determined by the local machine and the optimal anti-collision maneuver determined by the threat UAV, extract the minimum avoidance distance between the motion trajectories corresponding to the two anti-collision maneuvers;

Step 62: Calculate the allowable minimum track distance MASD, where MASD is the sum of the half wingspan WS of the aircraft and the threat UAV, plus the expected distance DSD input by the system and the accumulation of uncertainty;

Step 63: When the UAV determines that its planned track overlaps with the planned track of the threatening UAV, that is, when the predicted minimum distance PMR is less than the allowable minimum track distance MASD (same as the minimum avoidance distance), the collision avoidance maneuver is activated.

In this paper, a hybrid motion model of the UAV is constructed. For the trajectory prediction with high precision requirements, a trajectory prediction model based on the six-degree-of-freedom equation of motion is constructed, which can accurately convert the collision avoidance strategy of the UAV into the corresponding planned flight. traces, providing "faster than real-time" trajectory predictions. For the conventional flight process, the Piccolo model is used to represent it. By constructing the hybrid motion model of the fixed-wing UAV, it not only ensures the accuracy of the UAV's track, but also can significantly reduce the complexity and the amount of calculation. This solution is based on the data link to complete the automatic anti-collision operation between multiple aircrafts. Once a risk is detected, the drone will be controlled to perform a series of flight actions such as climbing and rolling until the collision threat is eliminated. The basic principle is shown in Figure 1. . The automatic air collision avoidance system obtains the navigation information and GPS data of the aircraft and the UAV in the surrounding airspace from the navigation system. Once it determines that there may be a conflict with the UAV in the surrounding airspace, it establishes a data link communication with the potentially threatening aircraft. Threat UAV Situation Screens 3 anti-collision maneuvers among 9 designated anti-collision maneuvers, including 1 preferred anti-collision maneuver and 2 alternative anti-collision maneuvers, and generates corresponding anti-collision maneuvers based on a modular trajectory prediction algorithm. Three planned trajectories, and at the same time send the planned trajectory information of the aircraft to the surrounding drones through the data link to reserve the occupation of the airspace, and obtain the same type of information of other aircraft, and use the evaluation algorithm to compare the aircraft and the threat drone. The optimal maneuver is determined according to the cost function. During the entire collision avoidance process, the system will continue to cycle through the calculation and comparison, constantly update the anti-collision maneuver, and at the same time determine whether to activate the anti-collision maneuver. If there is a spatial intersection in the memory), the autonomous escape flight operation is triggered; if no collision possibility is detected in the escape flight, the collision avoidance maneuver is updated according to the new state information, and the planned track is sent.

Embodiment 2

Based on the above-mentioned first embodiment, this embodiment provides a collision avoidance system for a UAV, including a memory and a processor, where the memory stores a collision avoidance program for the UAV, and the processor is running the collision avoidance system for the UAV The program executes the steps of the method described in any of the embodiments. As a specific embodiment of the above-mentioned UAV collision avoidance system, the processor includes an automatic air collision avoidance system, and the automatic air collision avoidance system includes the following functional modules:

Information receiving and processing module: receive and process the flight status information of the aircraft and the target UAV through airborne systems, sensors and data links;

Anti-collision maneuver generation module: It can generate maneuvers in all directions by rolling, climbing and other actions. This solution includes three types of maneuvers in total;

Collision avoidance maneuver evaluation module: The target UAV pre-screens 3 kinds of collision avoidance maneuvers from 9 kinds of collision avoidance maneuvers, including 1 preferred collision avoidance maneuver and 2 alternative collision avoidance maneuvers, based on the flight status information of the target UAV and the threatening aircraft. collision maneuver;

Motion trajectory prediction module: use the embedded trajectory prediction algorithm (Trajectory prediction algorithm) to generate 3 planned trajectories corresponding to 3 collision avoidance maneuvers;

Threat UAV trajectory reconstruction module: The target UAV is divided into two cases: cooperative and non-cooperative. It receives the planned track information of the cooperative target UAV and reconstructs its trajectory on the local computer unit, and receives the non-cooperative target UAV. flight status information and its motion trajectory is estimated on the local computer unit;

Maneuver Assessment and Selection Module: Evaluate all planned trajectory combinations of own and target UAVs and determine which combination will delay the maneuver as long as possible and provide adequate guard intervals, while for cooperative targets coordinate selection via datalinks Determine the final maneuvers performed by each UAV;

Maneuver activation and control module: Determine whether to activate the anti-collision maneuver of the aircraft according to the planned trajectory corresponding to the optimal anti-collision strategy of the aircraft and the threatening aircraft, and resume normal flight after the threat is eliminated.

Anti-collision maneuver generation module:

The UAV has a high degree of maneuverability and flexibility. It can generate maneuvers in all directions by rolling, climbing and other actions. It can be changed by changing control parameters such as elevators, ailerons, rudders, spoilers, flaps, and stabilizer angles. UAV flight attitude (pitch angle, roll angle, yaw angle), speed, acceleration, etc., so as to realize the control of the UAV, and finally generate maneuvers in different directions. When the UAV performs collision avoidance, it can generate maneuvers in all directions, but in the automatic collision avoidance system, the following restrictions are generally followed, and the maneuvers in specific directions are achieved by changing the roll angle (EA) and the overload factor (NZ), including There are nine maneuvers in three categories. Limiting the number of maneuvers to 9 can significantly reduce the budget of the system, improve the computing speed of the system, ensure the rapid response of the UAV, and facilitate the standardization of the UAV's automatic collision avoidance system. The nine maneuvers also basically ensure that the UAV can maneuver in all directions, roll and climb, and the roll angle increments relative to the current roll angle are -90°, -60°, -30°, and 0° respectively. , 30°, 60°, 90°, the climbing overload is 5g absolute acceleration; keep the roll angle and the standard g acceleration unchanged; keep the roll angle unchanged, and drop with an absolute acceleration of -0.5g. When the aircraft is maneuvering, the rolling speed is the maximum rolling speed allowed by the aircraft performance, and the climbing overload is 5g absolute acceleration. Roll angle and overload can be adjusted according to the scene.

When conducting maneuver evaluation and selection, due to the hardware limitations of computing processing power and data link information transmission, the automatic air collision avoidance system first pre-screens 3 collision avoidance maneuvers from 9 maneuvers, including 1 preferred collision avoidance maneuver and 2 The second-choice collision avoidance maneuver. The pre-selection is based on the geometric situation of the threat drone relative to the aircraft, including determining whether the threat drone is in the forward or backward direction of the aircraft, above or below the aircraft, on the left or the left of the aircraft. The right side, and whether it is a head-on or rear-end configuration, etc. As shown in Figure 3(a), the threat UAV appears above the target UAV, on the left side, backward, facing the target UAV from left to right, from front to back, from top to bottom, and it is expected to appear Go to the right under the aircraft, and the aircraft should take an upward and left flight trajectory. As shown in Figure 3(b), the threat UAV appears below the target UAV, right and left, facing the target UAV from right to left, back to front, and bottom to top. Appear above the aircraft and then turn to the left, this aircraft should take an upward, left flight trajectory.

When the automatic air collision avoidance system is judging the geometric situation, in order to simplify the calculation amount, the UAV motion model is simplified from 6DOF (six degrees of freedom motion model) to 3DOF model (three degrees of freedom motion model, as shown in Figure 4), And according to the flight status of the threat drone, including the position and speed information, the relative motion trajectory of the threat drone from the current position to the CPA (closed point of approach) in space is constructed, so as to carry out the anti-collision maneuver prediction. filter.

In the 3DOF model, the state information of the UAV includes the position information P=(xe,ye,ze) and the speed information Ve=(ue,ve,we), where Ve is the three-dimensional speed of the UAV in the ground coordinate axis system , where ψ is the yaw angle, θ is the pitch angle, φ is the roll angle, S _ψθφ is the transformation matrix from the ground coordinate system to the body coordinate system, Vb=(ub,vb,wb) is the UAV in the body coordinate system down speed.

Ve=S _ψθφ .Vb

P _t+τ (A)=P _t (A)+V _t (A)×τ

P _t+τ (T)=P _t (T)+V _t (T)×τ

τ is the time it takes for two UAVs to reach the closest point in space according to the given flight state at the current time t; P _t+τ (A) is the position where the target UAV reaches the closest point in space, P _{t+ τ} (T) is the position where the threat UAV reaches the closest point in space, and the trajectory of the threat UAV relative to the target UAV is (P _t (A)-P _t (T), P _t+τ (A) -P _t+τ (T)).

Native motion trajectory prediction module:

The motion trajectory prediction model can predict the trajectory of the UAV in the future based on the current state parameters and control parameters of the UAV. The motion trajectory prediction model will generate three sets of corresponding planned trajectories according to the three sets of collision avoidance maneuvers of the drone, and then send it to the threat drone. UAV state parameters (ub,vb,wb,xe,ye,ze,pr,qr,rr,phir,thetar,psir) include speed information, position information, attitude information. The speed information includes the vertical axis speed ub of the body, the horizontal axis speed vb of the body, and the vertical axis speed wb of the body; the position information includes the north coordinate xe of the ground coordinate axis, the east coordinate ye of the ground coordinate axis, and the altitude ze; the attitude information includes the rolling axis of the ground coordinate system. Rotation angle pir, ground coordinate axis pitch angle thetar, ground coordinate axis system yaw angle psir, roll angular velocity pr, pitch angular velocity qr, yaw angular velocity rr; UAV control parameters (dEr, dAr, dRr, dT, dASr , dFr, dSr) including elevator deflection angle dEr; aileron deflection angle, rudder deflection angle dRr, engine valve Dt, spoiler deflection angle dASr, flap deflection angle dFr, and stabilizer deflection angle dSr.

As shown in Figure 5, in this model, the prediction time of the motion trajectory prediction model is set to 5 seconds, that is, the trajectory information of the aircraft is predicted in the next 5 seconds, and the uncertainty of the predicted flight route increases with the increase of time. The running cycle of the algorithm is 10Hz, and the trajectory prediction algorithm outputs 50 predicted positions P(t) corresponding to 5s to other modules every 0.1s (at intervals of 0.1s), as shown in Figure 5, the planned track is a cone-shaped area. The midline of the region is the trajectory generated by the 50 predicted positions, and the cross-sectional size of the conical region is the trajectory uncertainty distance UD(t) corresponding to the predicted position, which depends on the uncertainty of the predicted flight path and increases with time. The trajectory uncertainty distance improves the robustness of the trajectory prediction algorithm, ensuring that 95% of the actual deviation can fall into the output of the uncertain model.

When predicting the trajectory of the UAV, considering that the UAV has the characteristics of high speed and flexibility, and has added maneuvers such as rolling and climbing in the collision avoidance process, the trajectory prediction algorithm (TPA) is based on the 6DOF (six degrees of freedom) pair. The motion state of the drone is depicted. The six-degree-of-freedom motion equation of the UAV includes two parts: the center of mass motion equation (force equation) and the rotation equation (moment equation) around the center of mass.

1. The equation of motion of the center of mass (force equation):

where V _E is the absolute speed of the drone relative to the ground coordinate system O _g x _g y _g z _g , V _B is the speed of the drone relative to the body coordinate system O _B x _B y _B z _B , and ω is the drone angular velocity of rotation.

V _B =iu _b +jv _b +kw _b

ω=i p+j q+k r

Where i, j, k are the unit vectors on the x _B , y _B , z _B axes under the body coordinate axis system respectively

阻力

和重力

在机体坐标系中O_Bx_By_Bz_B进行投影,得到以下等式Combine the engine thrust F _T , the aircraft gravity in mg, and the lift

resistance

and gravity

Projecting O _B x _B y _B z _B in the body coordinate system, the following equations are obtained

阻力

侧力

均与空气密度ρ(density of airflow),机翼面积S(wing area),翼展b(wing span),平均气动弦长

攻角(angle of attack)α，侧滑角(slide)β，操纵面偏转(the control surface deflections)δ,角速度p，q，_r(angularrate)。where lift

resistance

side force

Both are related to the air density ρ (density of airflow), the wing area S (wing area), the wingspan b (wing span), and the average aerodynamic chord length

Angle of attack α, slide angle β, the control surface deflections δ, angular velocity p, q, _r (angularrate).

2. Rotation equation (moment equation) around the center of mass:

H=Iω

Among them, I _x , I _y , I _z , and I _xz are the moment of inertia around the longitudinal axis, vertical axis and horizontal axis of the body and the inertial product of the aircraft to the longitudinal and vertical axes of the body. Considering that the aircraft has a longitudinal symmetry plane, the approximate I _xy =0, I _yz =0.

是空气动力矩在纵轴、横轴、立轴上的投影向量。Among them, the external moments M _x , My _y , and M _z are the projection vectors of M on the vertical axis, horizontal axis and vertical axis of the body, respectively. The external moment is generated from the aerodynamic moment and the engine thrust, where L _T , M _T , and N _T are the projection vectors of the aircraft thrust moment on the vertical axis, horizontal axis and vertical axis,

is the projection vector of the aerodynamic moment on the vertical, horizontal and vertical axes.

Aerodynamic torque can be expressed in a similar way to aerodynamic force.

It is assumed that the thrust point of the engine is located in the xz plane of the body coordinate system, and the distance from the center of gravity in the _z -axis direction is z _T .

In summary, the moment equation of the UAV is as follows:

Therefore, combining the force equation and moment equation of the aircraft, the state formula of the aircraft can be obtained:

Γc₂＝(I_x-I_y+I_z)I_xz，

Γc₃＝I_z，

Γc₉＝I_x，

Wherein, Γc ₄ =I _xz ,

Γc ₂ =(I _x -I _y +I _z )I _xz ,

Γc ₃ =I _z ,

Γc ₉ =I _x ,

When the UAV anti-collision system controls the flight of the aircraft, it changes the aerodynamic parameters of the aircraft by controlling the elevator, flaps, etc., and further changes the state parameters such as the speed and attitude angle of the UAV, so as to complete the corresponding maneuvering action. The response model of the UAV is shown in Figure 6. The system can not only input control parameters to control the flight, but also predict the trajectory of the UAV based on the current state of the UAV and the set control parameters.

Table 2 Units and ranges of input control parameters

control unit minimum maximum limit elevator deg -25 25 60deg/s aileron deg -21.5 21.5 80deg/s rudder deg -30 30 120deg/s flap deg 0 25 25deg/s

Remarks: The elevator is the "rudder surface" that controls the lift of the aircraft. Its function is to control the pitch of the aircraft and change the pitch angle; the aileron is the main operating rudder surface of the aircraft, which can generate rolling torque to make the aircraft roll and change the roll. Turning angle; the rudder is a movable airfoil part that realizes the aircraft heading control, which is used to control the aircraft heading and change the heading angle; the flap is an airfoil-shaped movable device on the edge of the aircraft wing, and its basic function is to fly increase lift.

Target aircraft track management module:

The target aircraft track management module is responsible for receiving and processing the input data of the threat drone. Threat drones are divided into two categories: 1) Cooperative threat drones. Cooperative threat drones are also equipped with an automatic collision avoidance system, which establishes communication with the aircraft through a designated data link and sends its own status information to the aircraft. ; 2) Non-cooperative threat drones, the aircraft obtains the status information of threat drones through other means such as radar.

Assemble the current state information of the target UAV, summarizing them into a list in a unified structure. Data for the same threat drone may come from different sources, prioritized by data source. The data link dedicated to the automatic air collision avoidance system has the highest priority, the data link of the non-automatic air collision avoidance system has the second priority, and finally the data from radar and ambiguous data sources. Figure 7 is a schematic diagram of track management processing multi-source data, eliminating/merging duplicate threat target information, and mapping the remaining tracks to a unified "related output threat list". Thus, the time is unified, and it is convenient to detect whether there is a collision risk between the information of the target machine and the local machine. When reaching the 3s threshold from the last valid data packet, due to the unreliability of the data, the corresponding target machine will be deleted from the "Related Output Threat List".

The special data link of the automatic air collision avoidance system contains the maneuvering flight trajectory information followed by the target aircraft, but the real trajectory of the target aircraft from other data sources is difficult to obtain.

(1) If the acceleration information is obtained and the target machine is in low overload acceleration motion, use the kinematicModel motion model to predict its motion trajectory;

(2) If the acceleration information cannot be obtained, the motion trajectory is predicted along the expansion of the velocity vector;

(3) If the acceleration information cannot be obtained and the speed of the threat aircraft is extremely low, use the ballistic model to predict its trajectory.

In order to save processing time, it is necessary to sort the relevant output threat list, and the target machine with higher threat degree should be processed first. The threat degree J is introduced, and the threat degree is a weighted function of the slant distance and the distance rate:

Among them, R is the straight-line distance, R _dot is the approach rate, and the weighting factor V _crit is an adjustable weighting constant, which has a higher priority than the straight-line distance and approach rate to achieve the best threat assessment effect. When the approach rate is positive (the direction of movement is away from the machine), simplify the formula and remove R _dot .

In a multi-machine situation, especially in a large-scale UAV swarm, the UAV will encounter intrusions and threats from multiple UAVs at the same time during the flight. The target drone will track the intruding drone and calculate its threat level, establish a threat level table (Threat table), sort the threats of different drones, and the threat table will be updated every 1 second. However, due to the hardware limitations of the UAV's computing processing capability and data link information transmission, the target UAV cannot respond to all threat UAVs at the same time. The system will select the three UAVs with the highest threat as the threat aircraft. And take anti-collision maneuvers for the three most threatening UAVs. Each drone only considers the three threat drones with the highest threat to it, and the system updates every 0.25 seconds. This applies to drone swarms.

5. Anti-collision strategy generation module

When encountering threats from other aircraft, the drone will select 3 groups from the 9 groups of preset maneuvers as collision avoidance maneuvers. Next, in the anti-collision strategy generation module, the UAV selects the preferred anti-collision maneuvers and alternative anti-collision maneuvers from the three groups of anti-collision maneuvers, and generates a planned track as the predetermined airspace for the UAV. When the collision maneuver is activated, the UAV will fly along the planned trajectory according to the preferred collision avoidance maneuver. In the anti-collision strategy module, the UAV first sends the 3 sets of planned track information of the aircraft to the threat UAV through the data link, and obtains the planned track information of the threat UAV at the same time, and then uses the evaluation algorithm to compare the local aircraft Combined with the planned trajectory of the threat drone, determine the best avoidance strategy, and finally send the updated preferred collision avoidance maneuver and alternative collision avoidance maneuver to other threat drones through the data link.

In the automatic air collision avoidance system, the UAV generates new maneuvers and anti-collision strategies at a frequency of four times per second, that is, the maneuver selection is updated every 0.25s based on the newly generated maneuvers, and the selected maneuvers are based on 20 per second. The frequency of updating the flight status information. From selecting maneuver combinations to generating collision avoidance strategies, collision avoidance algorithms introduce delayed waits to send native messages and receive information from other UAVs. If there is no delay, the anti-collision algorithm takes action without receiving the latest news, which may lead to misjudgment or failure. In a time-delay framework, the collision avoidance algorithm updates the trajectory with current flight state information, but does not allow maneuvers or command changes, so that all participants have a reasonable time to receive information and make decisions. Delayed wait mechanisms are critical to ensure that maneuvers are side-by-side/synchronized and to perform safe, expected maneuvers. If no new maneuvering options are received from other UAVs after the delay, the previous optimal collision avoidance strategy must be implemented. As shown in Figure 8, under the delay framework, the new three groups of anti-collision maneuvers generated by the UAV will continue the previous anti-collision strategy, and the last preferred anti-collision maneuver will still be used as the newly generated three groups of anti-collision maneuvers. The optimal collision avoidance maneuver of the maneuver is sent to the threat UAV, and then the combination comparison is performed according to the new collision avoidance maneuver of the threat UAV, and the collision avoidance strategy is updated. The new combination comparison determines whether to update the collision avoidance strategy. When the collision avoidance algorithm selects a new preferred collision avoidance maneuver, the automatic air collision avoidance system needs to evaluate whether all threat aircraft can receive the change before allowing the new maneuver to be performed.

The process of maneuvering combination is shown in Figure 9. At a certain moment, it is assumed that there are several UAVs in the airspace that threaten each other. The automatic collision avoidance system comprehensively considers every anti-collision maneuver of each threatening UAV. The planned trajectories corresponding to the collision maneuver are combined one by one, the AD between each pair of UAVs is calculated, and the cost of all combinations is obtained. There are 9 combinations for every two cooperative aircraft (3 target aircraft and local aircraft, matching in pairs); for non-cooperative targets, because the threat aircraft does not have automatic maneuvers, there are only 4 combinations; if it is the situation of three cooperative target aircraft , then 81 trajectory combinations need to be considered to determine the best trajectory combination. The cost function is used to evaluate single-machine and multi-machine threat scenarios, and the selected maneuver combinations are as follows:

The principle of choosing the optimal combination of the own aircraft and the target aircraft is to compare the available maneuvers for each UAV and select the combination that can delay the activation of the collision avoidance maneuver as much as possible. The collision avoidance algorithm selects the trajectory combination with the largest AD value of the "minimum avoidance distance" to delay the activation of the maneuver. The predicted minimum avoidance distance is calculated by subtracting the trajectory uncertainty distance UD from the minimum distance between two trajectories in the space-time domain (track center distance). M _i (m) represents the anti-collision maneuver m adopted by the drone i; AD(M _i (m), M _j (n)) represents when the drone i adopts the maneuver mode m, and the drone j adopts the maneuver mode n The minimum avoidance distance between the two machines.

ω _ij (m,n)=1/AD _ij (m,n)

When UAVs take specified collision avoidance maneuvers, the cost function of the entire UAV swarm is as follows:

In the maneuvering combination method, the maneuvering method that the UAV should take is as follows:

Anti-collision maneuver excitation control module:

A design principle of the anti-collision algorithm is to not interfere. In order to minimize interference and still achieve anti-collision, the automatic air collision avoidance system needs to activate the maneuver as late as possible, and the collision of drone A entering drone B is avoided. Anti-collision maneuvers can be activated in front of the airspace sphere, and the earlier the activation time, the more maneuvering methods the drone can choose, and the less likely it is to collide. The later the activation time, the more maneuvering range the drone can choose. Small, when the activation time is later than a certain time, no matter what maneuvers are taken, the collision of the two drones cannot be prevented. As shown in Figure 10, if UAV A takes a maneuver at maneuver point 1, the collision of two UAVs can be avoided whether it is a preferred maneuver or a backup maneuver, but the activation time is too early, which has a great impact on the normal flight of the UAV; Taking action at maneuver point 3, no matter whether the optimal maneuver or the backup maneuver can prevent drone A from entering the collision avoidance airspace sphere of drone B, the collision cannot be avoided; if action is taken at maneuver point 2, the backup maneuver enters the threat The drone collides with the avoidance airspace sphere, but the optimal maneuver is just tangent to the collision avoidance airspace sphere to achieve collision avoidance. The maneuver point 2 is the latest time for the drone to avoid collision, and it is also the best activation time for the automatic collision avoidance system. The anti-collision maneuver excitation control module is to find the optimal collision avoidance position similar to maneuver point 2 during the flight of the UAV, so that the UAVs can just achieve collision avoidance and minimize interference. At the same time, during collision avoidance, the radius of the collision avoidance airspace sphere in the automatic collision avoidance system can be adjusted according to the actual situation to achieve the best collision avoidance effect.

As shown in Figure 11, the anti-collision maneuver excitation control module compares the planned trajectory corresponding to the optimal maneuver of the own aircraft provided by the trajectory prediction algorithm with the received planned trajectory of the optimal maneuver corresponding to the threat aircraft, and calculates the predicted minimum distance PMR (Predicted Minimum Range) to determine the best timing for maneuver activation.

The anti-collision maneuver excitation control module also needs to calculate the minimum allowable track distance MASD (Minimum AllowedSeparation Distance). MASD is the sum of the half wingspan WS (Wing Span) of the aircraft and the target aircraft, plus the desired distance DSD (Desired Separation Distance) input by the system Seperation Distance) and the accumulation of the following Uncertainty:

(1) Navigation uncertainty;

(2) Uncertainty of trajectory prediction;

(3) Uncertainty of trajectory reconstruction/fitting;

(4) Uncertainty of data link transmission;

(5) Uncertainty of track data calculation.

MASD=DSD+U+∑WS

When the UAV determines that its planned track overlaps with the planned track of the threatening UAV, that is, when the predicted minimum distance PMR is less than the allowable minimum track distance MASD, the anti-collision maneuver will be activated, and the UAV will act according to the preferred anti-collision maneuver. , where the desired distance DSD is a fixed value, which is input by the system in advance.

The algorithm structure is as follows:

In the automatic air collision avoidance system, the operation flow of the automatic collision avoidance operation algorithm is shown in Figure 12.

Initialize the aircraft state, set the initial state parameters and control parameters of the UAV

Calculate and store the UAV's roll angle (EA), overload factor (NZ) and other parameters, and generate the flight trajectory for the next 20 seconds according to the UAV's flight state parameters, and send the UAV's flight route to the surrounding through the data link empty other drones.

Receive the flight route of other drones in the surrounding airspace, compare it with its own flight route, and determine whether it constitutes a conflict with the drone. Generate a threat table, and at the same time list the three drones with the highest threat to the target drone as threat drones

Based on the current position CP(t0), velocity V, and the aircraft position CP(t0+Δt) at the estimated time (t0+Δt) based on the current position CP(t0) of the aircraft and the threat UAV, according to the flight trajectory plan of the threat UAV relative to the target UAV Select 3 sets of maneuvers from 9 sets of maneuvers to obtain the roll angle ΔEA and overload factor NZ that need to be compensated;

Generate the planned track of the target UAV according to the compensated roll angle ΔEA and the overload factor NZ;

Combine the planned trajectory of the target UAV and the threat UAV, calculate the cost function of different combinations, and determine the preferred maneuver;

Detects whether there is a collision risk between the target UAV and the threat UAV. If a risk is detected, an avoidance operation is triggered.

(8) During the avoidance maneuver, the roll angle (EA) and the overload factor (NZ) are adjusted, and a new flight operation command is calculated from the compensated roll angle ΔEA and the overload factor ΔNZ.

The above descriptions are only the preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Under the inventive concept of the present invention, the equivalent structural transformations made by the contents of the description and drawings of the present invention, or the direct/indirect application Other related technical fields are included in the scope of patent protection of the present invention.

Claims (10)

1. An unmanned aerial vehicle collision avoidance method is characterized by comprising the following steps:

step 1, judging whether the local unmanned aerial vehicle conflicts with the peripheral unmanned aerial vehicle or not according to the acquired navigation information and GPS data of the local unmanned aerial vehicle and the peripheral unmanned aerial vehicle;

step 2, establishing data link communication between the unmanned aerial vehicle and the potential threat unmanned aerial vehicle under the condition that a conflict exists; determining a threat unmanned aerial vehicle according to the threat degree;

screening at least two of a plurality of preset anti-collision machines according to the situation of the threat unmanned aerial vehicle;

step 3, generating a planned track of the self-body corresponding to the screened anti-collision maneuvers respectively by using a track prediction algorithm, sending the planned track to the threat unmanned aerial vehicle to realize the occupation of the airspace corresponding to the planned track by the threat unmanned aerial vehicle, and acquiring related data or flight information corresponding to the planned track of the threat unmanned aerial vehicle;

step 4, reconstructing a planned track of the threat unmanned aerial vehicle according to relevant data or flight information corresponding to the planned track of the threat unmanned aerial vehicle;

step 5, comparing a plurality of combinations of the planned track of the self-body and the planned track of the threat unmanned aerial vehicle through an evaluation algorithm, determining the optimal collision avoidance maneuver of the self-body from the screened collision avoidance maneuvers according to the minimum avoidance interval value, and sending the optimal collision avoidance maneuver to the threat unmanned aerial vehicle through a data link, wherein the rest screened collision avoidance maneuvers are used as alternative collision avoidance maneuvers;

meanwhile, obtaining the optimal collision avoidance maneuver threatening the unmanned aerial vehicle determined according to the cost function;

and 6, judging whether collision risks exist between the self body and the threat unmanned aerial vehicle or not according to the optimal anticollision maneuver of the self body and the optimal anticollision maneuver of the threat unmanned aerial vehicle, and if so, exciting the optimal anticollision maneuver of the self body and the optimal anticollision maneuver of the threat unmanned aerial vehicle to implement avoidance.

2. The collision avoidance method for unmanned aerial vehicles according to claim 1, wherein the step of determining threat to the unmanned aerial vehicle in step 2 comprises:

step 201, when a threat unmanned aerial vehicle is also loaded with an automatic aerial collision avoidance system, the threat unmanned aerial vehicle is a cooperative threat unmanned aerial vehicle, the threat unmanned aerial vehicle establishes communication with the cooperative threat unmanned aerial vehicle through a specified data link, and self state information sent by the cooperative threat unmanned aerial vehicle is obtained;

step 202, when the threat unmanned aerial vehicle is not equipped with an automatic aerial collision avoidance system, the threat unmanned aerial vehicle is a non-cooperative threat unmanned aerial vehicle, and the state information of the non-cooperative threat unmanned aerial vehicle is obtained by the unmanned aerial vehicle through a radar or other ways;

step 203, sorting the state information of the threat unmanned aerial vehicles passing through different sources, wherein the priority of the special data chain from the automatic air collision avoidance system is the highest, the priority of the data chain from the non-automatic air collision avoidance system is the second priority, and the priority of the data chain from the radar and other unknown data sources is the last;

and step 204, tracking the invading unmanned aerial vehicles according to the priority and calculating the threat degree of the invading unmanned aerial vehicles in a multi-vehicle situation, and selecting three unmanned aerial vehicles with the highest threat degree as threat machines.

3. The collision avoidance method for unmanned aerial vehicles according to claim 1, wherein the step of screening collision avoidance maneuvers in step 2 comprises:

step 21, realizing nine collision avoidance maneuvers in a preset direction by changing a roll angle EA and an overload factor NZ;

step 22, simplifying the motion model of the unmanned aerial vehicle from a six-degree-of-freedom motion model into a three-degree-of-freedom motion model, and constructing a relative motion track threatening the unmanned aerial vehicle to reach a space closest point CPA from the current position according to the flight state of the threatening unmanned aerial vehicle;

and 23, screening at least two of the nine anti-collision mechanisms according to the relative motion tracks.

4. The collision avoidance method for unmanned aerial vehicles according to claim 2, wherein in step 3, the generating of the planned trajectories respectively corresponding to the screened collision avoidance maneuvers by using a trajectory prediction algorithm comprises:

step 31, outputting a series of predicted positions corresponding to a preset time period according to a preset operation cycle based on a six-degree-of-freedom motion equation;

step 32, predicting the uncertainty distance of the track around each predicted position according to the uncertainty of the flight path, and forming a circular area on the cross section passing through the predicted position;

and step 33, forming a conical area taking the series of predicted positions as a central line according to the condition that the uncertain distance increases along with the time.

5. The collision avoidance method for unmanned aerial vehicles according to claim 4, wherein the step of obtaining the planned trajectory or flight information threatening the unmanned aerial vehicle in step 3 comprises:

step 301, sending a planned trajectory to the threatening unmanned aerial vehicle;

step 302, when the threat unmanned aerial vehicle is a cooperative unmanned aerial vehicle, receiving a predicted track of the threat unmanned aerial vehicle responding to a local vehicle and generating a planned track corresponding to the screening collision avoidance maneuver according to a track prediction algorithm;

and when the threat unmanned aerial vehicle is a non-cooperative unmanned aerial vehicle, receiving acceleration information and speed information of the threat unmanned aerial vehicle.

6. The collision avoidance method for unmanned aerial vehicles according to claim 5, wherein the step 4 comprises:

when relevant data corresponding to the planned track threatening the unmanned aerial vehicle come from a special data chain of the automatic aerial collision avoidance system, reconstructing a motion track of the threatening unmanned aerial vehicle according to the maneuvering flight track information contained in the special data chain;

when the flight information threatening the unmanned aerial vehicle comes from a non-automatic aerial collision avoidance system data chain, radar or other unknown data sources:

if the acceleration information is obtained and the target machine moves at low overload acceleration, predicting the movement track of the target machine by using a kinematical Model movement Model;

if the acceleration information can not be obtained, predicting the motion track along a speed vector expansion mode;

if acceleration information cannot be obtained and the threat machine speed is in a very low range, the ballistic model ballisticmodel is used for predicting the motion track of the threat machine.

7. The collision avoidance method for unmanned aerial vehicles according to claim 1, wherein the step 5 comprises:

step 51, combining a planned track corresponding to the unmanned aerial vehicle anticollision maneuver with a motion track threatening the reconstruction of the unmanned aerial vehicle on the unmanned aerial vehicle one by one;

aiming at any two motion track combinations of the self-propelled vehicle and the threat unmanned aerial vehicle, outputting the difference between the track center distance and the track uncertainty distance UD in the space-time domain as a minimum avoidance distance value AD;

and step 52, determining the optimal collision avoidance maneuver of the local machine according to the motion track combination with the maximum minimum avoidance interval value.

8. The collision avoidance method for unmanned aerial vehicles according to claim 1, wherein the step 6 comprises:

step 61, extracting a minimum avoidance interval between motion tracks corresponding to the two collision avoidance maneuvers according to the optimal collision avoidance maneuver determined by the unmanned aerial vehicle and the optimal collision avoidance maneuver determined by the threat unmanned aerial vehicle;

step 62, calculating an allowable minimum trajectory distance MASD, wherein the MASD is the sum of the semi-span WS of the unmanned aerial vehicle and the threat unmanned aerial vehicle, and the sum of the expected distance DSD input by the system and the uncertainty;

and step 63, when the unmanned aerial vehicle judges that the planned track of the unmanned aerial vehicle is overlapped with the planned track of the threatening unmanned aerial vehicle, namely the predicted minimum distance PMR is smaller than the allowed minimum track distance MASD, activating the collision avoidance maneuver.

9. The unmanned aerial vehicle collision avoidance method of claim 8, wherein the uncertainty in step 62 comprises: navigation uncertainty, trajectory prediction uncertainty, trajectory reconstruction/fitting uncertainty, data link transmission uncertainty, and trajectory data computation uncertainty.

10. An unmanned aerial vehicle collision avoidance system, comprising a memory and a processor, wherein the memory stores an unmanned aerial vehicle collision avoidance program, and the processor executes the steps of any one of the methods of claims 1 to 9 when running the unmanned aerial vehicle collision avoidance program.

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