CN111865395B - Trajectory generation and tracking method and system for unmanned aerial vehicle formation communication - Google Patents

Trajectory generation and tracking method and system for unmanned aerial vehicle formation communication Download PDF

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CN111865395B
CN111865395B CN202010534402.5A CN202010534402A CN111865395B CN 111865395 B CN111865395 B CN 111865395B CN 202010534402 A CN202010534402 A CN 202010534402A CN 111865395 B CN111865395 B CN 111865395B
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杨志华
任安宁
齐晓晗
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Shenzhen Graduate School Harbin Institute of Technology
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Abstract

The invention provides a trajectory generation and tracking method for formation communication of unmanned aerial vehicles, which comprises the following steps: s1, constructing an unmanned aerial vehicle communication system model, and reestablishing communication between the origin unmanned aerial vehicle and the destination unmanned aerial vehicle by adopting the relay unmanned aerial vehicle; s2, predicting the tracks of the source unmanned aerial vehicle and the target unmanned aerial vehicle, and transmitting the tracks to the relay unmanned aerial vehicle in real time; and S3, planning the track of the relay unmanned aerial vehicle in real time according to the predicted tracks of the source unmanned aerial vehicle and the target unmanned aerial vehicle. The invention also provides a track generation and tracking system for unmanned aerial vehicle formation communication. The invention has the beneficial effects that: the method for reestablishing the stable connection is provided for the conditions that shadow and fading exist in the low-altitude environment and the link connection of the two communication unmanned aerial vehicles is disconnected in the severe environment, and the assumption that the source node and the destination node of the traditional communication are fixed is broken through.

Description

Trajectory generation and tracking method and system for unmanned aerial vehicle formation communication
Technical Field
The invention relates to an unmanned aerial vehicle, in particular to a trajectory generation and tracking method and system for formation communication of the unmanned aerial vehicle.
Background
The high-speed civilian unmanned aerial vehicle of low latitude has played important effect in many fields such as commodity circulation transportation, evening performance, match live broadcast. The unmanned aerial vehicle inevitably meets complicated communication environment at the in-process of carrying out some tasks, compares in fixed basic station on ground, and unmanned aerial vehicle has high mobility, deploys nimble characteristics, therefore unmanned aerial vehicle ubiquitous in various applications, including unmanned aerial vehicle relay under emergency, follow satellite uninstallation data etc. in the hot spot region.
In wireless communication, relaying is a method for improving system communication throughput and assisting system re-communication in which a direct link is lost due to channel change. Under the influence of node mobility, a common relay base station cannot meet the requirement on the mobility flexibility of the base station, and along with the popularization of unmanned aerial vehicles, the unmanned aerial vehicles are widely used as an air relay auxiliary communication method.
Compared with the traditional static relay, the unmanned aerial vehicle mobile relay has the following advantages:
(1) the system can be deployed quickly, and can be used as a relay to join the system at any time when the communication system is needed. Is suitable for processing unknown and emergency events.
(2) The mobile communication system has high mobility and flexibility, can freely move between a sending end and a receiving end, can adjust the position at any time to achieve the optimal communication performance, and enhances the performance of the communication system.
In recent years, research on relay unmanned aerial vehicles mainly includes unmanned aerial vehicles as auxiliary communication tools of ground base stations, and when the coverage of the ground base stations is limited or limited, the unmanned aerial vehicles are used as relays for transmitting information. The auxiliary unmanned aerial vehicle can be divided into static unmanned aerial vehicle deployment, semi-dynamic unmanned aerial vehicle deployment and dynamic unmanned aerial vehicle deployment according to whether the auxiliary unmanned aerial vehicle is movable. The static unmanned aerial vehicle is deployed to realize that an optimal communication position is selected between a transmitting end and a receiving end for communication, so that the communication requirements of the transmitting end and the receiving end can be met, or the coverage of a communication area is met by deploying the static unmanned aerial vehicle. Semi-dynamic unmanned aerial vehicle deploys that ground user is static, and unmanned aerial vehicle removes, and in practical application, unmanned aerial vehicle need constantly remove in order to satisfy different demands. The dynamic unmanned aerial vehicle deployment is that the positions of ground users and unmanned aerial vehicles are changed, and for some mobile users, the ground users mainly refer to ground hotspot users, and in order to ensure the communication quality in the moving process, the deployment of the dynamic unmanned aerial vehicle is needed.
In the present phase, research on relay unmanned aerial vehicles mainly focuses on providing aerial assistance for ground base stations, generally fixing the flight height of the unmanned aerial vehicle, and searching the optimal position of communication between two ground base stations needing communication. The relay unmanned aerial vehicle is supplementary to ground basic station, and both ends communication base station is fixed, also provides convenience for the solution of problem.
However, when the unmanned aerial vehicle moving at a high speed flies in a low altitude, shadow fading is generated due to occlusion, communication interruption can be caused when the environment is complex, namely, the communication of the unmanned aerial vehicle moving at a high speed is interrupted, and link connection cannot be reestablished, so that communication interruption is caused.
Therefore, in the case of link disconnection between two communicating drones, how to provide a method for reestablishing a stable connection breaks through the assumption that the source node and the destination node of the conventional communication are stationary, which is a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a track generation and tracking method and system for unmanned aerial vehicle formation communication.
The invention provides a trajectory generation and tracking method for formation communication of unmanned aerial vehicles, which comprises the following steps:
s1, constructing an unmanned aerial vehicle communication system model, dividing a communicated inorganic cluster into a source unmanned aerial vehicle and a target unmanned aerial vehicle, and reestablishing communication between the source unmanned aerial vehicle and the target unmanned aerial vehicle by using a relay unmanned aerial vehicle when the source unmanned aerial vehicle and the target unmanned aerial vehicle cannot communicate with each other, namely no direct communication link exists;
s2, predicting the tracks of the source unmanned aerial vehicle and the target unmanned aerial vehicle, and transmitting the tracks to the relay unmanned aerial vehicle in real time;
and S3, planning the track of the relay unmanned aerial vehicle in real time according to the predicted tracks of the source unmanned aerial vehicle and the target unmanned aerial vehicle.
As a further improvement of the present invention, in step S1, the relay drone reestablishes communication between the originating drone and the destination drone, using the DF forwarding scheme.
As a further improvement of the present invention, in step S2, the trajectories of the source drone and the target drone are predicted separately using a trajectory prediction algorithm based on the extended kalman filter, and the position at the next time is predicted at the current time.
As a further improvement of the invention, the trajectory prediction algorithm based on the extended Kalman filtering comprises:
inputting: an initial position value, an initial measured value;
and (3) outputting: predicted trajectory values, deviation values;
1): preprocessing a track;
2): initializing parameters;
3): acquiring the current state and the position of the current moment;
4): circulating for given times;
5): recording the value predicted each time by the extended Kalman filtering algorithm;
6) calculating the deviation between the predicted value and the actual value;
7) ending the cycle;
8) outputting the predicted value and the deviation.
As a further improvement of the present invention, step S3 includes the following sub-steps:
s31, solving the optimal problem by a PSO algorithm based on a penalty function;
s32, generating a smooth track of the relay unmanned aerial vehicle;
and S33, controlling the relay unmanned aerial vehicle to fly out of the optimal track.
The invention also provides a trajectory generation and tracking system for formation communication of unmanned aerial vehicles, which comprises a readable storage medium, wherein execution instructions are stored in the readable storage medium, and when executed by a processor, the execution instructions are used for realizing the method in any one of the above.
The invention has the beneficial effects that: through the scheme, a stable connection reestablishing method is provided for the conditions that shadow and fading exist in a low-altitude environment and the link connection of the two communication unmanned aerial vehicles is disconnected in a severe environment, and the assumption that a source node and a destination node of traditional communication are immovable is broken through.
Drawings
Fig. 1 is an interruption scene diagram of a trajectory generation and tracking method for formation communication of unmanned aerial vehicles according to the present invention.
Fig. 2 is a plot of trajectory prediction and predicted deviation for the direction of source drone X, Y.
Fig. 3 is a plot of trajectory prediction and predicted deviation in the Z direction of the source drone.
Fig. 4 is a diagram of trajectory prediction and predicted deviation of the direction of the destination drone X, Y.
Fig. 5 is a diagram of trajectory prediction and predicted deviation in the Z direction of the target drone.
Fig. 6 is a diagram of relay drone optimal position and maximum transmission rate.
Fig. 7 is a three-dimensional path point diagram of the relay drone.
Fig. 8 is a diagram of the flight trajectory of the relay drone.
Fig. 9 is a diagram comparing the flight trajectory of the relay unmanned aerial vehicle with the planned trajectory position.
Fig. 10 is a plot of the flight speed of the relay drone compared to the planned trajectory speed.
Detailed Description
The invention is further described with reference to the following description and embodiments in conjunction with the accompanying drawings.
A track generation and tracking method for formation communication of unmanned aerial vehicles is characterized in that a relay unmanned aerial vehicle is used for carrying out auxiliary communication, a DF forwarding mode is adopted, under the condition that the unmanned aerial vehicle continuously moves, a PSO algorithm with penalty factors is adopted to plan a relay unmanned aerial vehicle track which enables a separated formation to be reconnected for communication and to obtain the optimal transmission rate, the positions of a source unmanned aerial vehicle and a relay unmanned aerial vehicle are predicted by considering the real-time performance of the motion of the unmanned aerial vehicle, the position of the relay unmanned aerial vehicle is planned in real time, and the real-time performance and the effectiveness of the algorithm are guaranteed. The dynamic constraint of the unmanned aerial vehicle is considered, and the actual flying characteristics of the unmanned aerial vehicle are highlighted. And controlling the relay unmanned aerial vehicle to fly according to the planned smooth track by using an unmanned aerial vehicle control algorithm, wherein the track deviation finally reflects the deviation of the communication speed.
The specific process is as follows:
1. unmanned aerial vehicle communication system model construction
The drone swarm performs a specific task, and due to the blockage of certain obstacles or channel fading, a connected drone is grouped into two parts that cannot communicate, i.e., there is no direct communication link. A relay unmanned aerial vehicle R is adopted, so that the relay unmanned aerial vehicle R helps to establish the connection of the unmanned aerial vehicle cluster again. Considering a two-hop system, as shown in fig. 1, first, the drone cluster is divided into two parts, and it is assumed that each part can select a cluster head node, which is a source node a and a destination node B for information transmission. Secondly, the connection of the whole unmanned aerial vehicle cluster can be realized as long as the source node A and the destination node B can carry out optimal communication. Due to the high-speed mobility of the unmanned aerial vehicle, the communication quality of the unmanned aerial vehicle cluster is guaranteed to be optimal in the moving process.
Assuming that the relay unmanned aerial vehicle only acts in a certain time, the total time of the whole motion process is T, the relay unmanned aerial vehicle is divided into N time slots, and the coordinates of the nth time slot node A, R, B are (ps)x[n],psy[n],psz[n])，(prx[n],pry[n],prz[n])，(pdx[n],pdy[n],pdz[n]). N is 1,2, the distance between the source unmanned aerial vehicle node a and the relay unmanned aerial vehicle node R is set as dsr[n]The distance between the relay unmanned aerial vehicle node R and the target unmanned aerial vehicle node B is set as drd[n]。
Figure GDA0003584643790000061
Figure GDA0003584643790000062
The height difference between the source unmanned aerial vehicle node A and the relay unmanned aerial vehicle node R is set as hdsr[n]The height difference between the relay unmanned aerial vehicle node R and the destination unmanned aerial vehicle node B is set as hdrd[n]，
hdsr[n]＝|psz[n]-prz[n]|n＝1,2,...,N (3)
hdrd[n]＝|prz[n]-pdz[n]|n＝1,2,...,N (4)
The unmanned aerial vehicle flying at low altitude and high speed is considered, the channel condition of the unmanned aerial vehicle flying at low altitude is complex, shelters of building buildings and the like may exist, and influences of shadows, fading and the like are mainly considered. Consider non line-of-sight (NLOS) transmission. The channel transmission model is:
Figure GDA0003584643790000071
wherein the content of the first and second substances,
Figure GDA0003584643790000072
wherein etaLOS，ηNLOSA, b are constants related to the propagation environment, f is the carrier frequency, and c is the speed of light.
The power loss of the nth slot is:
Figure GDA0003584643790000073
the channel transmission coefficient of the nth time slot is as follows:
Figure GDA0003584643790000074
sn represents small scale fading and is independent of the same distribution CN (0, 1).
Adopting a DF (decode and forward) forwarding mode, ensuring the optimal channel transmission rate according to the channel condition, and assuming that the transmitting power of a source unmanned aerial vehicle and a relay unmanned aerial vehicle are equal, the maximum communication rates of the source unmanned aerial vehicle and a target unmanned aerial vehicle are respectively as follows:
Figure GDA0003584643790000075
Figure GDA0003584643790000076
hsrchannel state matrix, P, for relaying to destination nodesTo transmit power, δ2Representing the noise variance. h isrdChannel state matrix, P, for relaying to a destination noderTo transmit power, δ2Representing the noise variance. B is the channel bandwidth.
And carrying out reasonable constraint on the transmission rate, the speed and the turning radius of the unmanned aerial vehicle.
Csr[n]-Crd[n]≥0 (10)
xr(n+1)-xr(n)≤mVrmax (11)
Figure GDA0003584643790000081
CsrChannel transmission rate, C, from the source drone to the relay dronerdRepresenting the channel transmission rate of the relay drone to the destination drone. x is the number ofr(n) represents the location of the relay drone for the nth discrete point.
And m is a proportionality coefficient. VrmaxThe unit is m/s for relaying the maximum flying speed of the unmanned aerial vehicle. RminIs the minimum turning radius, and g is the gravitational acceleration.
2. Source unmanned aerial vehicle and target unmanned aerial vehicle trajectory prediction
When the environment is more complicated, the navigation positioning system of unmanned aerial vehicle self can be destroyed, can't convey the real-time position to relay unmanned aerial vehicle, considers the real-time nature of unmanned aerial vehicle motion, utilizes the extended Kalman filtering algorithm to predict source unmanned aerial vehicle and relay unmanned aerial vehicle's position, and real-time and the validity of algorithm are guaranteed in the position of real-time planning relay unmanned aerial vehicle. And performing state estimation on the dynamic behavior of the mobile unmanned aerial vehicle based on a dynamic trajectory prediction algorithm of the extended Kalman filtering, updating estimation on a state variable by using an estimation value at the previous moment and an observation value at the current moment, and predicting the trajectory position at the next moment.
The state is updated according to the following formula:
X(k)＝fk-1(X(k-1)) (13)
Z(k)＝hk(X(k)) (14)
X(k|k-1)＝fk-1(X(k-1|k-1)) (15)
P(k|k-1)＝fk-1P(K-1|K-1)fk-1 T+fk-1Qfk-1 T (16)
kg(k)＝P(k|k-1)(hk)T/(hkP(k|k-1)hk T+hkRkhk T) (17)
X(k|k)＝X(k|k-1)+kg(k)[Z(k)-hkX(k|k-1)] (18)
where X (k | k-1) is the result of the prediction using the previous state, A is the system parameters, X (k-1| k-1) is the optimal result for the previous state, P (k | k-1) is the covariance matrix corresponding to X (k | k-1), P (k-1| k-1) is the covariance matrix corresponding to X (k-1| k-1), Q and R are the white Gaussian noise variances of the system process, k is the system parametersgIs an extended kalman gain. H is a parameter of the measurement system, and P (k | k) is a covariance matrix corresponding to X (k | k).
And respectively predicting the tracks of the source unmanned aerial vehicle and the target unmanned aerial vehicle by using an extended Kalman filtering algorithm, and predicting the position of the next moment at the current moment.
Algorithm 1: trajectory prediction algorithm based on extended Kalman filtering
Inputting: initial position value, initial measured value
And (3) outputting: predicted trajectory values, deviation values
1: preprocessing a track;
2: initializing parameters;
3: acquiring the current state and the position of the current moment;
4: circulating for given times;
5: recording the value predicted each time by the extended Kalman prediction method;
6, calculating the deviation between the predicted value and the actual value;
7, ending the circulation;
and 8, outputting the predicted value and the deviation.
3. PSO algorithm based on penalty function for solving optimal problem
After the motion path points of the source unmanned aerial vehicle and the target unmanned aerial vehicle at the next moment are predicted by using an extended Kalman filtering algorithm at the current moment, the motion path points are transmitted to the relay unmanned aerial vehicle in real time, so that the relay unmanned aerial vehicle can calculate the path point at the next moment.
Adopting DF forwarding mode, according to the channel condition, ensuring the optimal channel transmission rate, and assuming that the transmission power of the source unmanned aerial vehicle and the transmission power of the relay unmanned aerial vehicle are equal, the constructed optimization problem is as follows:
Figure GDA0003584643790000101
s.t.Csr[n]-Crd[n]≥0 (19a)
xr(n+1)-xr(n)≤mVrmax (19b)
Figure GDA0003584643790000102
hrdchannel state matrix, P, for relaying to a destination noderTo transmit power, delta2Representing the noise variance. And carrying out reasonable constraint on the transmission rate, the speed and the turning radius of the unmanned aerial vehicle. CsrChannel transmission rate, C, from the representative source drone to the relay dronerdRepresenting the channel transmission rate of the relay drone to the destination drone. x is the number ofr(n) represents the location of the relay drone for the nth discrete point.
And m is a proportionality coefficient. VrmaxThe unit is m/s for relaying the maximum flying speed of the unmanned aerial vehicle. RminIs the minimum turning radius, and g is the gravitational acceleration.
In order to obtain the maximum transmission rate, the obtained Optimization problem is a non-convex problem, a Particle Swarm Optimization (PSO) algorithm with a penalty function is adopted to solve the Optimization problem, the constrained Optimization problem is converted into an unconstrained Optimization problem, and an optimal solution is obtained. And constructing a fitness function of the particle swarm according to the established optimization problem to obtain:
Figure GDA0003584643790000103
Figure GDA0003584643790000111
u represents a penalty factor.
4. Relay unmanned aerial vehicle smooth trajectory generation
The relay unmanned aerial vehicle generates a smooth and continuous curve from generated discrete path points, a 7-time minisnap track generation method is adopted to obtain a group of path points, in order to guarantee the effectiveness of the obtained path points, the relay unmanned aerial vehicle is expected to fly through the optimal path points, M optimal points are generated according to the track prediction of a source unmanned aerial vehicle and a target unmanned aerial vehicle, M +1 points are shared from an initial position, the polynomial coefficient of M sections of tracks is calculated, and each section of track is obtained. And knowing the time t of each traceiThe total time is T.
l(t)＝α0+α1t+α2t2+α3t3+α4t4+α5t5+α6t6+α7t7 (21)
Figure GDA0003584643790000112
Where l (t) is the expression of the trajectory at time t. Alpha is alpha0,α1,α2,...,αkIs a polynomial coefficient, has uniform time distribution, has M polynomials,and solving the polynomial coefficient to obtain a continuous expression of each section. To ensure the continuity of the trajectory, an equality constraint equation is constructed.
The initial position and the target position of the relay unmanned aerial vehicle are fixed, the motion track of the unmanned aerial vehicle in a period of time is selected, in the period of time, the initial position, the speed and the acceleration of the track are equal to the initial position, the speed and the acceleration of the relay unmanned aerial vehicle, and the tail end position, the speed and the acceleration of the track are equal to the tail end position, the speed and the acceleration of the relay unmanned aerial vehicle in the period of time.
Figure GDA0003584643790000121
l(1)[0]＝(vrx[0]，vry[0],vrz[0]) (24)
l(1)[T]＝(vrx[T],vry[T],vrz[T]) (25)
l(2)[0]＝(arx[0]，ary[0],arz[0]) (26)
l(2)[T]＝(arx[T],ary[T],arz[T]) (27)
Parameter l [0 ]],l[T],l(1)[0],l(1)[T],l(2)[0],l(2)[T]Represent relay unmanned aerial vehicle initial position, T position of moment, initial velocity, T speed of moment, initial acceleration, T acceleration of moment in this period of time respectively.
The position, the speed and the acceleration of adjacent intermediate point are continuous, and the motion of unmanned aerial vehicle can not take place the sudden change, and its motion trajectory is the curve that can lead in succession.
Figure GDA0003584643790000122
Parameter(s)
Figure GDA0003584643790000123
Respectively represents a k-segment ending position, a k + 1-segment starting position, a k-segment ending speed, a k + 1-segment starting speed, a k-segment ending acceleration and a k + 1-segment starting acceleration. The objective of us is to find polynomial coefficients, there may be many curves satisfying constraints, we need to find the shortest curve between two points, and construct an optimization equation as follows:
Figure GDA0003584643790000124
s.t.
Aeqα＝beq (29a)
and solving the parameters to obtain a polynomial expression.
5. Control relay unmanned aerial vehicle to fly out of optimal track
In order to guarantee the feasibility of the scheme, the characteristics of the unmanned aerial vehicle body and the related angle requirements are combined, and the smaller the error between the actual flying track and the expected flying track is, the better the error is, namely the control errors of the three coordinate directions and the yaw angle at the lower side are 0. Namely:
(arx-ax,des)+kd1(vrx-vx,des)+kp1(prx-px,des)＝0 (30)
(ary-ay,des)+kd2(vry-vy,des)+kp2(pry-py,des)＝0 (31)
(arz-az,des)+kd3(vrz-vz,des)+kp3(prz-pz,des)＝0 (32)
ψ＝ψdes (33)
kp1、kp2、kp3respectively representing the proportional control parameters in the directions of the X, Y and Z positions of the coordinate system. Correspondingly, kd1、kd2、kd3Respectively representing differential control parameters in the directions of the X, Y and Z positions of the coordinate system. v. ofrx、vry、vrz、arx、ary、arz、prx,pry,przRepresenting velocity, acceleration and position in the X, Y, Z directions of the coordinate system, respectively. v. ofx,des,vy,des,vz,des、ax,des,ay,des,az,des、px,des,py,des,pz,desRepresenting the desired velocity, acceleration and position in the X, Y, Z directions of the coordinate system, respectively. Psi and psidesRespectively yaw angle and desired yaw angle.
6. Simulation result
The positions of a source unmanned aerial vehicle and a target unmanned aerial vehicle are predicted, and three coordinate direction trajectory equations of the source unmanned aerial vehicle are selected as follows:
psx＝3+10sin(3m)
psy＝2+10sin(2m)
psz＝10cos(4m)
the three coordinate direction trajectory equations of the target unmanned aerial vehicle are respectively as follows:
pdx＝7+10sin(3m)
pdy＝8+10sin(2m)
pdz＝20+10cos(4m)
the positions of 20 path points of a source unmanned aerial vehicle and a target unmanned aerial vehicle are predicted, the time interval is 1S, the predicted track values and the predicted error results of the source unmanned aerial vehicle in three coordinate axis directions are shown in figures 2 and 3, and the error between the position predicted by using the extended Kalman filtering algorithm and the actual position is very small through comparison of the figures. The predicted track values and the predicted error results of the target unmanned aerial vehicle in the three coordinate axis directions are shown in fig. 4 and 5, and the comparison of the graphs shows that the error between the position predicted by using the extended Kalman filtering algorithm and the actual position is very small.
In the experiment, the fact that large-scale fading exists in the low-altitude environment when the unmanned aerial vehicle flies is assumed, and eta is selectedLOS＝0.1，ηNLOS21, a-5.0188, b-0.3511, and a carrier frequency of 2.4 × 109HZ，c＝3×108m/s, the transmitting power of the source unmanned aerial vehicle and the transmitting power of the relay unmanned aerial vehicle are both 20dB, and the noise work used in the simulationThe rate value is-163 dBm, and the small scale fading present in the environment is independent co-distributed CN (0, 1). Maximum flight speed v of relay unmanned aerial vehiclemax＝50m/s，g＝9.81m/s2。
The predicted track value is substituted into an optimization function for calculating the optimal position of the relay unmanned aerial vehicle, so that the optimal position point and the maximum speed value in the three coordinate axis directions of the relay unmanned aerial vehicle shown in fig. 6 are obtained, and the maximum speed value can be seen to be 26.3bit/s, and tends to be stable along with the continuous movement of the unmanned aerial vehicle, which shows that the relay unmanned aerial vehicle can stably provide communication service for the unmanned aerial vehicle which continuously moves at a high speed. Fig. 7 is a diagram of the connection of three-dimensional path points of relay drones, which is not in line with the flight characteristics of drones, which require a smooth and continuous trajectory.
By adjusting the control parameters of the unmanned aerial vehicle, a trajectory diagram containing the expected trajectory and actually controlling the unmanned aerial vehicle to fly shown in fig. 8 is obtained, and it can be observed that within a fixed flight time of 20S, the planned expected trajectory and the actually flying trajectory have a certain deviation due to the influence of the control parameters or calculation errors. Fig. 9 further shows deviations of the desired trajectory from the actual flight trajectory in various directions of the coordinate axis, corresponding to deviations of the information transfer rate. FIG. 10 is a graph comparing desired speed to actual airspeed. Through fig. 7, 8 and 9, it can be seen that the method for reestablishing the communication link connection by using the relay unmanned aerial vehicle in the environment of communication interruption of the unmanned aerial vehicle, which is provided by the invention, is effective, and the error exhibited by actual flight of the unmanned aerial vehicle is acceptable, thereby proving the feasibility of the method.
The track generation and tracking method and system for unmanned aerial vehicle formation communication provided by the invention have the following advantages:
(1) the method for reestablishing the stable connection is provided for the conditions that shadow and fading exist in the low-altitude environment and the link connection of the two communication unmanned aerial vehicles is disconnected in the severe environment, and the assumption that the source node and the destination node of the traditional communication are fixed is broken through.
(2) The quad-rotor unmanned aerial vehicle has the characteristic of high-speed flight, and the adopted unmanned aerial vehicle position prediction method can reduce the deviation of the position calculation of the relay unmanned aerial vehicle caused by time delay and realize real-time position calculation.
(3) By considering the dynamic characteristics of the unmanned aerial vehicle, the path point of the relay unmanned aerial vehicle calculated by the invention is changed into a smooth and continuous feasible track of the unmanned aerial vehicle, and the unmanned aerial vehicle is controlled to fly out of the planning track by using an unmanned aerial vehicle control method, so that the scheme is more comprehensive and complete. The defect that in the prior art, only data is considered, and the characteristics of the unmanned aerial vehicle are not really considered by taking discrete points as the track of the unmanned aerial vehicle is overcome.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (6)

1. A trajectory generation and tracking method for formation communication of unmanned aerial vehicles is characterized by comprising the following steps:
s1, constructing an unmanned aerial vehicle communication system model, dividing a communicated inorganic cluster into a source unmanned aerial vehicle and a target unmanned aerial vehicle, and reestablishing communication between the source unmanned aerial vehicle and the target unmanned aerial vehicle by using a relay unmanned aerial vehicle when the source unmanned aerial vehicle and the target unmanned aerial vehicle cannot communicate with each other, namely no direct communication link exists;
s2, predicting the tracks of the source unmanned aerial vehicle and the target unmanned aerial vehicle, and transmitting the tracks to the relay unmanned aerial vehicle in real time;
s3, planning the track of the relay unmanned aerial vehicle in real time according to the predicted tracks of the source unmanned aerial vehicle and the target unmanned aerial vehicle;
wherein the content of the first and second substances,
step S3 includes the following substeps:
s31, solving the optimal problem by a PSO algorithm based on a penalty function;
s32, generating a smooth track of the relay unmanned aerial vehicle;
s33, controlling the relay unmanned aerial vehicle to fly out of the optimal track;
in step S1, falseIf the relay unmanned aerial vehicle only acts in the set time, the total time of the whole motion process is T, the time is divided into N time slots, and the coordinates of the source unmanned aerial vehicle node A, the relay unmanned aerial vehicle node R and the destination unmanned aerial vehicle node B of the nth time slot are respectively (ps)x[n],psy[n],psz[n])，(prx[n],pry[n],prz[n])，(pdx[n],pdy[n],pdz[n]) N is 1,2, the distance between the source drone node a and the relay drone node R is set to dsr[n]The distance between the relay unmanned aerial vehicle node R and the target unmanned aerial vehicle node B is set as drd[n]
Figure FDA0003584643780000021
Figure FDA0003584643780000022
The height difference between the source unmanned aerial vehicle node A and the relay unmanned aerial vehicle node R is set as hdsr[n]The height difference between the relay unmanned aerial vehicle node R and the destination unmanned aerial vehicle node B is set as hdrd[n]，
hdsr[n]＝|psz[n]-prz[n]|n＝1,2,...,N (3)
hdrd[n]＝|prz[n]-pdz[n]|n＝1,2,...,N (4)
Non line of sight (NLOS) transmission is adopted, and a channel transmission model is as follows:
Figure FDA0003584643780000023
wherein the content of the first and second substances,
A＝ηLOS-ηNLOS
Figure FDA0003584643780000024
Figure FDA0003584643780000025
wherein etaLOS，ηNLOSA, b are constants related to propagation environment, f is carrier frequency, c is speed of light, d [ n ]]Is the distance between two drones, hd [ n ]]Is the height difference between the two unmanned aerial vehicles;
the power loss of the nth slot is:
Figure FDA0003584643780000026
the channel transmission coefficient of the nth time slot is as follows:
Figure FDA0003584643780000031
sn represents small scale fading, which is independent and same distributed CN (0, 1);
adopting DF to forward the way, according to the channel condition, guaranteeing that the channel transmission rate is optimal, supposing that the transmitting power of the source unmanned aerial vehicle and the relay unmanned aerial vehicle are equal, the maximum communication rates of the source unmanned aerial vehicle and the target unmanned aerial vehicle are respectively:
Figure FDA0003584643780000032
Figure FDA0003584643780000033
hsrchannel state matrix, P, for relaying to a destination nodesTo transmit power, delta2Representing a noise variance; h isrdChannel state matrix, P, for relaying to a destination noderTo transmit power, delta2Representing a noise variance; b is the channel bandwidth;
reasonably constraining the transmission rate, the speed and the turning radius of the unmanned aerial vehicle;
Csr[n]-Crd[n]≥0 (10)
xr(n+1)-xr(n)≤mVrmax (11)
Figure FDA0003584643780000034
Csrchannel transmission rate, C, from the source drone to the relay dronerdRepresenting the channel transmission rate from the relay unmanned aerial vehicle to the target unmanned aerial vehicle; x is a radical of a fluorine atomr(n) represents the location of the relay drone for the nth discrete point; m is a proportionality coefficient; vrmaxThe unit of the maximum flying speed of the relay unmanned aerial vehicle is m/s; rminIs the minimum turning radius, g is the gravitational acceleration;
in step S31, a DF forwarding scheme is adopted to ensure that the channel transmission rate is optimal according to the channel conditions, and assuming that the transmission powers of the source drone and the relay drone are equal, the constructed optimization problem is:
Figure FDA0003584643780000041
s.t.Csr[n]-Crd[n]≥0 (19a)
xr(n+1)-xr(n)≤mVrmax (19b)
Figure FDA0003584643780000042
hrdchannel state matrix, P, for relaying to a destination noderTo transmit power, delta2Representing the noise variance, reasonably constraining the transmission rate, the speed and the turning radius of the unmanned aerial vehicle, CsrRepresenting source drone to relayChannel transmission rate of drone, CrdChannel transmission rate, x, on behalf of relaying drones to destination dronesr(n) represents the location of the relay drone for the nth discrete point;
m is a proportionality coefficient; vrmaxThe unit of the maximum flying speed of the relay unmanned aerial vehicle is m/s; rminIs the minimum turning radius, g is the acceleration of gravity;
in order to obtain the maximum transmission rate, the obtained optimization problem is a non-convex problem, a PSO algorithm with a penalty function is adopted to solve the optimization problem, the constrained optimization problem is converted into an unconstrained optimization problem, an optimal solution is solved, and a fitness function of a particle swarm is constructed according to the established optimization problem:
Figure FDA0003584643780000043
Figure FDA0003584643780000044
u represents a penalty factor;
in step S32, M optimal points are generated based on the trajectory predictions of the source drone and the destination drone, M +1 points are shared from the initial positions, the polynomial coefficients of M trajectory segments are obtained, each trajectory segment is obtained, and the time t of each trajectory segment is knowniTotal time is T;
l(t)＝α0+α1t+α2t2+α3t3+α4t4+α5t5+α6t6+α7t7 (21)
Figure FDA0003584643780000051
where l (t) is the expression of the trajectory at time t, α0,α1,α2,...,αkIs a polynomialThe coefficient is distributed with uniform time, M polynomials are provided, polynomial coefficients are solved, so that a continuous expression of each section is obtained, and an equality constraint equation is constructed in order to ensure the continuity of the track;
selecting a motion track of the unmanned aerial vehicle within a period of time, wherein the initial position, the speed and the acceleration of the track are equal to the initial position, the speed and the acceleration of the relay unmanned aerial vehicle within the period of time, and the tail end position, the speed and the acceleration of the track are equal to the tail end position, the speed and the acceleration of the relay unmanned aerial vehicle within the period of time;
Figure FDA0003584643780000052
l(1)[0]＝(vrx[0]，vry[0],vrz[0]) (24)
l(1)[T]＝(vrx[T],vry[T],vrz[T]) (25)
l(2)[0]＝(arx[0]，ary[0],arz[0]) (26)
l(2)[T]＝(arx[T],ary[T],arz[T]) (27)
parameter l [0 ]],l[T],l(1)[0],l(1)[T],l(2)[0],l(2)[T]Respectively representing the initial position, the T moment position, the initial speed, the T moment speed, the initial acceleration and the T moment acceleration of the relay unmanned aerial vehicle in the period of time;
the positions, the speeds and the accelerations of the adjacent intermediate points are continuous, the motion of the unmanned aerial vehicle cannot change suddenly, and the motion track of the unmanned aerial vehicle is a continuously-guided curve;
Figure FDA0003584643780000061
parameter lk[tf],lk+1[ts],
Figure FDA0003584643780000062
Respectively representing the end position of a section k, the start position of a section k +1, representing the end speed of the section k, the start speed of the section k +1, representing the end acceleration of the section k, and the start acceleration of the section k +1, solving polynomial coefficients, finding out the shortest curve between the two points, and constructing an optimization equation as follows:
Figure FDA0003584643780000063
s.t.
Aeqα＝beq (29a)
solving the parameters to obtain a polynomial expression;
controlling the relay unmanned aerial vehicle to fly out of an optimal track, and setting the control errors of the three coordinate directions and the yaw angle at the lower side as 0, namely:
(arx-ax,des)+kd1(vrx-vx,des)+kp1(prx-px,des)＝0 (30)
(ary-ay,des)+kd2(vry-vy,des)+kp2(pry-py,des)＝0 (31)
(arz-az,des)+kd3(vrz-vz,des)+kp3(prz-pz,des)＝0 (32)
ψ＝ψdes (33)
kp1、kp2、kp3respectively representing the proportional control parameters in the X, Y and Z position directions of a coordinate system; correspondingly, kd1、kd2、kd3Respectively representing differential control parameters, v, in the direction of the X, Y, Z position of the coordinate systemrx、vry、vrz、arx、ary、arz、prx,pry,przRepresenting the velocity, acceleration and position in the X, Y, Z coordinate system, respectively, vx,des,vy,des,vz,des、ax,des,ay,des,az,des、px,des,py,des,pz,desRepresenting the desired velocity, acceleration and position in the X, Y, Z coordinate system, phi and phi respectivelydesRespectively yaw angle and desired yaw angle.
2. The trajectory generation and tracking method for formation communication of unmanned aerial vehicles according to claim 1, wherein: in step S1, the relay drone reestablishes communication between the originating drone and the destination drone using the DF forwarding scheme.
3. The trajectory generation and tracking method for formation communication of unmanned aerial vehicles according to claim 2, wherein: in step S2, the trajectories of the source drone and the target drone are predicted using a trajectory prediction algorithm based on the extended kalman filter, and the position at the next time is predicted at the current time.
4. The trajectory generation and tracking method for formation communication of unmanned aerial vehicles according to claim 3, wherein: the trajectory prediction algorithm based on the extended Kalman filtering comprises the following steps:
inputting: an initial position value, an initial reported value;
and (3) outputting: predicted trajectory values, deviation values;
1): preprocessing a track;
2): initializing parameters;
3): acquiring the current state and the position of the current moment;
4): circulating for given times;
5): recording the value predicted each time by the extended Kalman filtering algorithm;
6) calculating the deviation between the predicted value and the actual value;
7) ending the cycle;
8) outputting the predicted value and the deviation.
5. The trajectory generation and tracking method for formation communication of unmanned aerial vehicles according to claim 1, wherein: in step S2, based on the trajectory prediction algorithm of the extended kalman filter, performing state estimation on the dynamic behavior of the mobile unmanned aerial vehicle, updating the estimation on the state variable by using the estimation value at the previous time and the observation value at the current time, and further predicting the trajectory position at the next time;
the state is updated according to the following formula:
X(k)＝fk-1(X(k-1)) (13)
Z(k)＝hk(X(k)) (14)
X(k|k-1)＝fk-1(X(k-1|k-1) (15)
P(k|k-1)＝fk-1P(K-1|K-1)fk-1 T+fk-1Qfk-1 T (16)
kg(k)＝P(k|k-1)(hk)T/(hkP(k|k-1)hk T+hkRkhk T) (17)
X(k|k)＝X(k|k-1)+kg(k)[Z(k)-hkX(k|k-1)] (18)
in equations (13) to (18), X (k | k-1) is the result of prediction using the previous state, A is the system parameter, X (k-1| k-1) is the optimal result of the previous state, P (k | k-1) is the covariance matrix corresponding to X (k | k-1), P (k-1| k-1) is the covariance matrix corresponding to X (k-1| k-1), Q and R are the white Gaussian noise variances of the system process and the measurement system, respectively, and k is the white noise variance of the system process and the measurement system, respectivelygFor extended Kalman gain, H is a parameter of the measurement system, and P (k | k) is a covariance matrix corresponding to X (k | k).
6. The utility model provides a track generation and tracking system towards unmanned aerial vehicle formation communication which characterized in that: comprising a readable storage medium having stored therein executable instructions for implementing the method of any one of claims 1 to 5 when executed by a processor.
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