CN113311867A - Motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking - Google Patents

Motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking Download PDF

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CN113311867A
CN113311867A CN202110589059.9A CN202110589059A CN113311867A CN 113311867 A CN113311867 A CN 113311867A CN 202110589059 A CN202110589059 A CN 202110589059A CN 113311867 A CN113311867 A CN 113311867A
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unmanned aerial
aerial vehicle
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drone
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CN113311867B (en
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吴杰宏
杨京辉
张维君
杨华
高利军
刘奎
周振辉
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Shenyang Aerospace University
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Abstract

The invention provides a motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking, which comprises the steps of firstly judging whether an unmanned aerial vehicle at each moment can sense target information, selecting a neighbor unmanned aerial vehicle of the unmanned aerial vehicle which cannot sense any target as a temporary follow leader, and designing a motion model of the unmanned aerial vehicle at the next moment and an escape model of the target, wherein the effective communication range of the unmanned aerial vehicle is considered, namely each unmanned aerial vehicle can only obtain the state information of the neighbor, and global communication does not exist, so that the complexity is lower; compared with a centralized control method, the distributed control based on the individual motion rule has stronger robustness and smaller calculated amount; the temporary leader selection method fully considers the number of the unmanned aerial vehicles and the distance between the unmanned aerial vehicles and the targets, can ensure that each target has a certain number of unmanned aerial vehicles to track, and has higher tracking efficiency.

Description

Motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking
Technical Field
The invention belongs to the technical field of multi-unmanned aerial vehicle cooperative control, and particularly relates to a multi-unmanned aerial vehicle cooperative multi-target tracking motion control method.
Background
The 21 st century is an era of rapid development of science and technology, and computer technology is one of the hot spots which is mainstream at present. Technologies such as big data, internet of things and artificial intelligence are widely applied, and great convenience is brought to activities of people on the ground; meanwhile, people also explore unknown fields, and the method is always the exploration field of cumin in human beings in the air. Under the large background that intellectuality and informationization become leading, unmanned aerial vehicle has appeared in people's the field of vision. Because of its features of flexibility, small size and free navigation, it can be seen in civil and military activities. In civil activities, more and more enterprises and individuals are focusing on creating a transportation mode combining unmanned aerial vehicles and manpower, and application scenarios of the transportation mode include express delivery, takeaway delivery, target search and the like. In military operations, unmanned aerial vehicles often have unexpected effects, especially in aspects of detecting enemy conditions, interfering communication, destroying operation targets in clusters and the like, so that much research on unmanned aerial vehicles is often carried out for military purposes.
While drones have achieved great success in military operations, research on drone clusters is in the launch phase. The unmanned aerial vehicle cluster needs to reach higher standards in the process of executing tasks, and mainly the autonomy and the cooperativity among the unmanned aerial vehicles. The autonomy is mainly embodied in that each unmanned aerial vehicle can effectively utilize key technologies such as sensor fusion, information processing, intelligent driving and the like, has certain processing capacity aiming at any sudden situation, and can obtain current environmental information through positioning equipment and sensing equipment loaded on a fuselage. The cooperativity is mainly embodied in that all unmanned aerial vehicles in the cluster cooperate to complete tasks, each unmanned aerial vehicle can exchange information with a ground base station or other unmanned aerial vehicles in a communication range through communication equipment, and the unmanned aerial vehicles can achieve the coordination. Clusters of drones face more problems in performing the target tracking task than other tasks, which also requires a higher degree of coordination between drones. In recent years, with the rapid development of computer technology, cooperative control of multiple drones is one of important issues for research. The idea of cooperative control mainly comes from the clustering behavior of organisms, and the phenomenon commonly occurs in nature, such as migratory birds migrating, ants foraging, fish school movement, and the like. Compared with the situation that a single unmanned aerial vehicle executes tasks, the multi-unmanned aerial vehicle cooperative task execution system has the characteristics of stability, expandability, safety and the like, and is widely applied to the fields of space search, military operation, target tracking and the like. The target tracking is one of common methods for examining the cooperative robustness of the unmanned aerial vehicle. In recent years, the problem has received wide attention from domestic and foreign scholars.
The essence of the unmanned aerial vehicle executing the target tracking task is to adjust the motion rule of the unmanned aerial vehicle according to the influence of the state of the target and the surrounding environment information, and finally realize effective control on the target. When the multi-unmanned aerial vehicle system executes tasks, target tracking is one of the most important links. From a macroscopic perspective, target tracking is a basic situation of an unmanned aerial vehicle for executing tasks, and the unmanned aerial vehicle needs to have the capability of handling sudden situations and have the basic qualities of self-organizing and coordinately executing tasks in view of aerial complex environments; meanwhile, the unmanned aerial vehicle should also have the ability to process the information sensed by the sensor. For the target tracking problem, the related research is mainly focused on formation control and aggregation behavior. Olafir-Saber proposes a theoretical framework for multi-agent control algorithm design and analysis, and studies the influence of the number of neighbors of topological interaction on population dynamics. Some scholars introduce consistency ideas on the basis of the algorithm and start research from the perspective of maintaining dynamic network connectivity. However, the research is based on single-target tracking development, and in practice, the commonly encountered situation is a multi-target problem, so that the deep research on the unmanned aerial vehicle cooperative multi-target tracking problem has very important theoretical significance and practical value.
Disclosure of Invention
Based on the problems, the invention provides a motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking aiming at the problem of multi-unmanned aerial vehicle cooperative multi-target tracking, and the aim of multi-target consistent tracking is fulfilled from the perspective of multi-unmanned aerial vehicle cooperative control by cooperatively controlling the motion states of the multi-unmanned aerial vehicles.
A motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking comprises the following steps:
step 1: initializing location information (X) of each unmanned aerial vehicle inodei0,Yi0,Zi0) And velocity Vi0Initializing location information (x) of each target k nodek0,yk0,zk0) And velocity vk0Setting a predetermined motion track of each unmanned aerial vehicle i, setting a predetermined motion track of each target k, i being 1,2, …, N, k being 1,2, …, NtN denotes the total number of drones, NtRepresenting the total number of targets;
step 2: the unmanned aerial vehicle and the target move according to a set motion trail;
and step 3: let i equal 1,2, …, N, k equal 1,2, …, NtAnd calculating the distance between each unmanned aerial vehicle and all targets at each moment
Figure BDA0003088749990000021
And 4, step 4: judging whether there is a distance li,kThe radius R is smaller than the sensing range of the unmanned aerial vehicle i, if so, step 5 is executed, otherwise, step 2 is executed;
and 5: calculating the distance dist from the unmanned aerial vehicle i to each target k(i,k)If there is dist(i,k)If the radius R is smaller than the sensing range of the unmanned aerial vehicle i, the unmanned aerial vehicle i can sense the target information, executing a step 6, and if the radius R is not smaller than the sensing range of the unmanned aerial vehicle i, the unmanned aerial vehicle i cannot sense the target information, executing a step 7;
step 6: let k equal 1,2, …, NtStatistics of
Figure BDA0003088749990000022
The minimum value min { dist (i, k) } in the time sequence, and a target corresponding to the minimum distance min { dist (i, k) } is taken as the following of the unmanned aerial vehicle i at the time tThe leader continues to execute the step 8;
and 7: if the unmanned aerial vehicle cannot sense the target information, the neighboring unmanned aerial vehicle of the unmanned aerial vehicle i is required to be taken as a following leader at the time t, and the step 8 is continuously executed;
and 8: updating the motion model u of each unmanned aerial vehicle at the next moment (t +1)i(t+1)：
Figure BDA0003088749990000031
Figure BDA0003088749990000032
Figure BDA0003088749990000033
Figure BDA0003088749990000034
In the formula (I), the compound is shown in the specification,
Figure BDA0003088749990000035
position and velocity vectors, q, of the following leader, respectively, of drone ijA position vector representing the neighbor drone j,
Figure BDA00030887499900000311
gain coefficients for repulsion, aggregation and grouping control, respectively; epsiloniIs a grouping control signal, is a cause for cluster splitting, and when the following leader of the unmanned aerial vehicle i is a target, epsiloni1 is ═ 1; when the follower leader of drone i is a neighbor drone, ∈i＝0；
And step 9: updating the escape model u of each target at the next time (t +1)k(t+1)：
Figure BDA0003088749990000036
Figure BDA0003088749990000037
Figure BDA0003088749990000038
In the formula (I), the compound is shown in the specification,
Figure BDA0003088749990000039
the gain factor is represented by a factor of gain,
Figure BDA00030887499900000310
representing a one-dimensional space, | | representing the euclidean norm, dkSensor detection range representing target k, ak,jIndicating whether target k perceives a flag, a, for tracking the dronek,tA flag, q, of whether target k perceives target tkIs the position vector of target k, qjIs the position vector of drone j, qtIs the position vector of the target t;
step 10: judging whether the unmanned aerial vehicle cluster completes the multi-target tracking task, namely, the number of unmanned aerial vehicle subgroups tracking each target is balanced, the speed of the unmanned aerial vehicle of each subgroup converges to the target speed, and if so, the operation is finished; if not, go to step 8.
The step 7 comprises the following steps:
step 7.1: determining a neighbor set of the unmanned aerial vehicle i according to the motion state and the communication range of the unmanned aerial vehicle at the moment t, and calculating an adjacency matrix A of the unmanned aerial vehicle ii0；
Step 7.2: calculating the distance dist between each neighboring unmanned aerial vehicle j and the unmanned aerial vehicle i in the neighboring set of the unmanned aerial vehicle i(i,j)；
Step 7.3: let j equal 1,2, …, NiStatistics of
Figure BDA0003088749990000041
Minimum value ofmin { dist (i, j) }, calculating the influence factor generated by the distance
Figure BDA0003088749990000042
Figure BDA0003088749990000043
In the formula, NiRepresents the total number of neighboring drones of drone i;
step 7.4: calculating quantity-derived influencing factors
Figure BDA0003088749990000044
Step 7.5: calculating an influence total factor of each neighbor drone j of drone i
Figure BDA0003088749990000045
Figure BDA0003088749990000046
Step 7.6: let j equal 1,2, …, NiStatistics of
Figure BDA0003088749990000047
Maximum value of
Figure BDA0003088749990000048
Will be maximum value
Figure BDA0003088749990000049
The corresponding neighbor drone j' acts as the follower leader for drone i at time t.
Said step 7.4 comprises:
1) initializing a number statistic for each target k
Figure BDA00030887499900000410
2) Calculate each of the neighbor sets of drone iDistance dist between neighbor unmanned aerial vehicle j and each target k(j,k)；
3) Let k equal 1,2, …, NtStatistics of
Figure BDA00030887499900000411
The minimum value min { dist (j, k) } is calculated according to the number statistical factor of the target k' corresponding to the minimum value min { dist (j, k) }
Figure BDA00030887499900000412
The value is increased by 1;
4) let j equal 1,2, …, NiRepeating the steps 2) to 3) to obtain the final value of the number statistical factor of each target
Figure BDA00030887499900000413
Statistics of
Figure BDA00030887499900000414
Minimum non-zero natural number
Figure BDA00030887499900000415
5) For the
Figure BDA00030887499900000416
Influence factor generated by target calculation number different from zero
Figure BDA00030887499900000417
Figure BDA00030887499900000418
When in use
Figure BDA00030887499900000419
When, define
Figure BDA00030887499900000420
The invention has the beneficial effects that:
the invention provides a motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking, which comprises the steps of firstly judging whether an unmanned aerial vehicle at each moment can sense target information, selecting a neighbor unmanned aerial vehicle of the unmanned aerial vehicle which cannot sense any target as a temporary follow leader, and designing a motion model of the unmanned aerial vehicle at the next moment and an escape model of the target, wherein the effective communication range of the unmanned aerial vehicle is considered, namely each unmanned aerial vehicle can only obtain the state information of the neighbor, and global communication does not exist, so that the complexity is lower; compared with a centralized control method, the distributed control based on the individual motion rule has stronger robustness and smaller calculated amount; the temporary leader selection method fully considers the number of the unmanned aerial vehicles and the distance between the unmanned aerial vehicles and the targets, can ensure that each target has a certain number of unmanned aerial vehicles to track, and has higher tracking efficiency.
Drawings
FIG. 1 is a flow chart of a motion control method for multi-UAV cooperative multi-target tracking according to the present invention;
FIG. 2 is a diagram of a target escape model in the present invention;
fig. 3 is a schematic diagram of motions at four times during the simulation process of the present invention, in which (a) represents a motion simulation diagram at time t1, (b) represents a motion simulation diagram at time t2, (c) represents a motion simulation diagram at time t3, and (d) represents a motion simulation diagram at time t 4.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
As shown in fig. 1, a motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking includes:
step 1: initializing location information (X) of each unmanned aerial vehicle inodei0,Yi0,Zi0) And velocity Vi0Initializing location information (x) of each target k nodek0,yk0,zk0) And velocity vk0Setting a predetermined motion track of each unmanned aerial vehicle i, setting a predetermined motion track of each target k, i being 1,2, …, N, k being 1,2, …, NtN denotes the total number of drones, NtRepresenting the total number of targets;
step 2: the unmanned aerial vehicle and the target move according to a set motion trail;
the target escape model diagram is shown in fig. 2, a target has a certain sensing range and a communication range, and when unmanned aerial vehicles approach the target, the target plans the next action according to the number and the positions of the unmanned aerial vehicles in the sensing range and the state information of other targets in the communication range; in fig. 2, t1 and t2 represent target 1 and target 2, respectively; r1, r2, r3 denote drone 1, drone 2 and drone 3, respectively, which enter within the target perception range; u _ t1 and u _ t2 represent the force of target 1 against target 2 and the force of target 2 against target 1, respectively; u1 and u2 respectively represent the total force experienced by target 1 and target 2.
Step 2: calculating the distance between each unmanned aerial vehicle i and all targets at each moment
Figure BDA0003088749990000051
And step 3: let i equal 1,2, …, N, k equal 1,2, …, NtAnd calculating the distance between each unmanned aerial vehicle and all targets at each moment
Figure BDA0003088749990000061
And 4, step 4: judging whether there is a distance li,kThe radius R is smaller than the sensing range of the unmanned aerial vehicle i, if so, step 5 is executed, otherwise, step 2 is executed;
and 5: calculating the distance dist from the unmanned aerial vehicle i to each target k(i,k)If there is dist(i,k)If the radius R is smaller than the sensing range of the unmanned aerial vehicle i, the unmanned aerial vehicle i can sense the target information, executing a step 6, and if the radius R is not smaller than the sensing range of the unmanned aerial vehicle i, the unmanned aerial vehicle i cannot sense the target information, executing a step 7;
step 6: let k equal 1,2, …, NtStatistics of
Figure BDA0003088749990000062
The minimum value min { dist (i, k) } is determined, and the target corresponding to the minimum distance min { dist (i, k) } is taken as the unmanned aerial vehicle i at tThe leader is followed at any moment, and the step 8 is continuously executed;
and 7: if the unmanned aerial vehicle cannot sense the target information, the neighboring unmanned aerial vehicle of the unmanned aerial vehicle i is required to be taken as a following leader at the time t, and the step 8 is continuously executed;
the neighbor unmanned aerial vehicle of the unmanned aerial vehicle i is taken as a follow leader at the moment t, and the following leader is specifically expressed as follows:
step 7.1: determining a neighbor set of the unmanned aerial vehicle i according to the motion state and the communication range of the unmanned aerial vehicle at the moment t, and calculating an adjacency matrix A of the unmanned aerial vehicle ii0；
Step 7.2: calculating the distance dist between each neighboring unmanned aerial vehicle j and the unmanned aerial vehicle i in the neighboring set of the unmanned aerial vehicle i(i，j)；
Step 7.3: let j equal 1,2, …, NiStatistics of
Figure BDA0003088749990000063
Min { dist (i, j) }, calculating the influence factor caused by the distance
Figure BDA0003088749990000064
Figure BDA0003088749990000065
In the formula, NiRepresents the total number of neighboring drones of drone i,
the neighbor set of drone i is defined as { j: | | q: |)i-qj||≤R,j＝1,2,…,N,j≠i}；
Step 7.4: calculating quantity-derived influencing factors
Figure BDA0003088749990000066
The method comprises the following steps:
1) initializing a number statistic for each target k
Figure BDA0003088749990000067
2) Calculating each neighbor in the neighbor set of drone iDistance dist between unmanned aerial vehicle j and each target k(j,k)；
3) Let k equal 1,2, …, NtStatistics of
Figure BDA0003088749990000068
The minimum value min { dist (j, k) } is calculated according to the number statistical factor of the target k' corresponding to the minimum value min { dist (j, k) }
Figure BDA0003088749990000071
The value is increased by 1;
4) let j equal 1,2, …, NiRepeating the steps 2) to 3) to obtain the final value of the number statistical factor of each target
Figure BDA0003088749990000072
Statistics of
Figure BDA0003088749990000073
Minimum non-zero natural number
Figure BDA0003088749990000074
5) For the
Figure BDA0003088749990000075
Influence factor generated by target calculation number different from zero
Figure BDA0003088749990000076
Figure BDA0003088749990000077
When in use
Figure BDA0003088749990000078
When, define
Figure BDA0003088749990000079
Step 7.5: calculating an influence total factor of each neighbor drone j of drone i
Figure BDA00030887499900000710
Figure BDA00030887499900000711
Step 7.6: let j equal 1,2, …, NiStatistics of
Figure BDA00030887499900000712
Maximum value of
Figure BDA00030887499900000713
Will be maximum value
Figure BDA00030887499900000714
The corresponding neighbor drone j' acts as the follower leader for drone i at time t.
And 8: updating the motion model u of each unmanned aerial vehicle at the next moment (t +1)i(t+1)：
Figure BDA00030887499900000715
Figure BDA00030887499900000716
The unmanned aerial vehicle position separation item is used for preventing collision between the unmanned aerial vehicles;
Figure BDA00030887499900000717
be position gathering item and speed matching item respectively, in order to satisfy the requirement of cluster dynamics control, still require the speed unanimity when unmanned aerial vehicle gathers, the concrete form is:
Figure BDA00030887499900000718
Figure BDA00030887499900000719
the grouping motion control item integrates the feedback effect of the position and speed information of the temporary leader, and self-organizes and adjusts the motion tendency of the unmanned aerial vehicle, so that the unmanned aerial vehicle tracking different targets has a split phenomenon;
in the formula (I), the compound is shown in the specification,
Figure BDA00030887499900000720
position and velocity vectors, q, of the following leader, respectively, of drone ijA position vector representing the neighbor drone j,
Figure BDA00030887499900000721
gain coefficients for repulsion, aggregation and grouping control, respectively; epsiloniIs a grouping control signal, is a cause for cluster splitting, and when the following leader of the unmanned aerial vehicle i is a target, epsiloni1 is ═ 1; when the follower leader of drone i is a neighbor drone, ∈i＝0；
And step 9: updating the escape model u of each target at the next time (t +1)k(t+1)：
Figure BDA0003088749990000081
Figure BDA0003088749990000082
Figure BDA0003088749990000083
In the formula (I), the compound is shown in the specification,
Figure BDA0003088749990000084
the gain factor is represented by a factor of gain,
Figure BDA0003088749990000085
representing a one-dimensional space, | | representing an European tableReed norm, dkSensor detection range representing target k, ak,jIndicating whether target k perceives a flag, a, for tracking the dronek,tA flag, q, of whether target k perceives target tkIs the position vector of target k, qjIs the position vector of drone j, qtIs the position vector of the target t;
step 10: judging whether the unmanned aerial vehicle cluster completes the multi-target tracking task, namely, the number of unmanned aerial vehicle subgroups tracking each target is balanced, the speed of the unmanned aerial vehicle of each subgroup converges to the target speed, and if so, the operation is finished; if not, go to step 8.
To illustrate the effectiveness of the method of the present invention, the simulation experiment environment is: windows 10, Intel 2.6Ghz dual-core CPU, memory 8GiB, Matlab 2016b simulation software; four moments in the simulation process of FIG. 3: t1 ═ 0s, t2 ═ 5s, t3 ═ 9s, t4 ═ 14 s; the simulation parameter settings are shown in table 1.
TABLE 1 simulation parameter values
Figure BDA0003088749990000086
The invention provides a multi-unmanned aerial vehicle cooperative multi-target tracking motion control method, which mainly comprises temporary leader selection and unmanned aerial vehicle motion control based on local target information, provides a new thought for solving the problem of multi-target tracking, finally realizes the fast distance of unmanned aerial vehicles tracking the same target, tracks the fast separation of unmanned aerial vehicles among different targets, and has the advantages that the method can keep roughly equal-scale grouping on solving the problem of multi-target tracking, ensures the speed consistency of each subgroup, and has better performance than the traditional algorithm.

Claims (3)

1. A motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking is characterized by comprising the following steps:
step 1: initializing location information (X) of each unmanned aerial vehicle inodei0,Yi0,Zi0) And velocity Vi0Initializing location information (x) of each target k nodek0,yk0,zk0) And velocity vk0Setting a predetermined motion track of each unmanned aerial vehicle i, setting a predetermined motion track of each target k, i being 1,2, …, N, k being 1,2, …, NtN denotes the total number of drones, NtRepresenting the total number of targets;
step 2: the unmanned aerial vehicle and the target move according to a set motion trail;
and step 3: let i equal 1,2, …, N, k equal 1,2, …, NtAnd calculating the distance between each unmanned aerial vehicle and all targets at each moment
Figure FDA0003088749980000011
And 4, step 4: judging whether there is a distance li,kThe radius R is smaller than the sensing range of the unmanned aerial vehicle i, if so, step 5 is executed, otherwise, step 2 is executed;
and 5: calculating the distance dist from the unmanned aerial vehicle i to each target k(i,k)If there is dist(i,k)If the radius R is smaller than the sensing range of the unmanned aerial vehicle i, the unmanned aerial vehicle i can sense the target information, executing a step 6, and if the radius R is not smaller than the sensing range of the unmanned aerial vehicle i, the unmanned aerial vehicle i cannot sense the target information, executing a step 7;
step 6: let k equal 1,2, …, NtStatistics of
Figure FDA0003088749980000012
Taking a target corresponding to the minimum distance min { dist (i, k) } as a following leader of the unmanned aerial vehicle i at the moment t, and continuously executing the step 8;
and 7: if the unmanned aerial vehicle cannot sense the target information, the neighboring unmanned aerial vehicle of the unmanned aerial vehicle i is required to be taken as a following leader at the time t, and the step 8 is continuously executed;
and 8: updating the motion model u of each unmanned aerial vehicle at the next moment (t +1)i(t+1)：
Figure FDA0003088749980000013
Figure FDA0003088749980000014
Figure FDA0003088749980000015
Figure FDA0003088749980000016
In the formula (I), the compound is shown in the specification,
Figure FDA0003088749980000017
position and velocity vectors, q, of the following leader, respectively, of drone ijPosition vector, k, representing neighbor drone jr,kc,l>0 is the gain coefficient for repulsion, aggregation and grouping control, respectively; epsiloniIs a grouping control signal and is a cause for cluster splitting; when the follower leader of drone i is the target, εi1 is ═ 1; when the follower leader of drone i is a neighbor drone, ∈i＝0；
And step 9: updating the escape model u of each target at the next time (t +1)k(t+1)：
Figure FDA0003088749980000021
Figure FDA0003088749980000022
Figure FDA0003088749980000023
k,t∈NtAnd k ≠ t
In the formula (I), the compound is shown in the specification,
Figure FDA0003088749980000024
the gain factor is represented by a factor of gain,
Figure FDA0003088749980000025
representing a one-dimensional space, | | representing the euclidean norm, dkSensor detection range representing target k, ak,jIndicating whether target k perceives a flag, a, for tracking the dronek,tA flag, q, of whether target k perceives target tkIs the position vector of target k, qjIs the position vector of drone j, qtIs the position vector of the target t;
step 10: judging whether the unmanned aerial vehicle cluster completes the multi-target tracking task, namely, the number of unmanned aerial vehicle subgroups tracking each target is balanced, the speed of the unmanned aerial vehicle of each subgroup converges to the target speed, and if so, the operation is finished; if not, go to step 8.
2. The motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking according to claim 1, wherein the step 7 comprises:
step 7.1: determining a neighbor set of the unmanned aerial vehicle i according to the motion state and the communication range of the unmanned aerial vehicle at the moment t, and calculating an adjacency matrix A of the unmanned aerial vehicle ii0；
Step 7.2: calculating the distance dist between each neighboring unmanned aerial vehicle j and the unmanned aerial vehicle i in the neighboring set of the unmanned aerial vehicle i(i,j)；
Step 7.3: let j equal 1,2, …, NiStatistics of
Figure FDA0003088749980000026
Min { dist (i, j) }, calculating the influence factor caused by the distance
Figure FDA0003088749980000027
Figure FDA0003088749980000028
In the formula, NiRepresents the total number of neighboring drones of drone i;
step 7.4: calculating quantity-derived influencing factors
Figure FDA0003088749980000029
Step 7.5: calculating an influence total factor of each neighbor drone j of drone i
Figure FDA00030887499800000210
Figure FDA00030887499800000211
Step 7.6: let j equal 1,2, …, NiStatistics of
Figure FDA00030887499800000212
Maximum value of
Figure FDA00030887499800000213
Will be maximum value
Figure FDA00030887499800000214
The corresponding neighbor drone j' acts as the follower leader for drone i at time t.
3. The motion control method for multi-unmanned aerial vehicle cooperative multi-target tracking according to claim 1, wherein the step 7.4 comprises:
1) initializing a number statistic for each target k
Figure FDA0003088749980000031
2) Calculating unmanned aerial vehicle iDistance dist between each neighbor unmanned aerial vehicle j and each target k in neighbor set(j,k)；
3) Let k equal 1,2, …, NtStatistics of
Figure FDA0003088749980000032
The minimum value min { dist (j, k) } is calculated according to the number statistical factor of the target k' corresponding to the minimum value min { dist (j, k) }
Figure FDA0003088749980000033
The value is increased by 1;
4) let j equal 1,2, …, NiRepeating the steps 2) to 3) to obtain the final value of the number statistical factor of each target
Figure FDA0003088749980000034
Statistics of
Figure FDA0003088749980000035
Minimum non-zero natural number
Figure FDA0003088749980000036
5) For the
Figure FDA0003088749980000037
Influence factor generated by target calculation number different from zero
Figure FDA0003088749980000038
Figure FDA00030887499800000311
j∈Ni,k＝1,2,…,NtAnd is and
Figure FDA00030887499800000312
when in use
Figure FDA0003088749980000039
When, define
Figure FDA00030887499800000310
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