CN113825142A - Intelligent optimization method for cooperative task area coverage of unmanned cluster system - Google Patents
Intelligent optimization method for cooperative task area coverage of unmanned cluster system Download PDFInfo
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Abstract
The invention discloses an intelligent optimization method for cooperative task area coverage of an unmanned cluster system, which comprises the step 1 of constructing a distributed cluster network with an unmanned aerial vehicle as a base station and establishing a cluster unmanned aerial vehicle sensing area. And 2, dividing the task area by a GV graph method, distributing the divided sub-areas to the clusters, and calculating a coverage quality target by the clusters through a coverage quality function and the importance factors distributed in the sub-areas to obtain cluster expected state information. And 3, adjusting the input control law of the unmanned aerial vehicle cluster system according to the expected state information to enable the cluster to reach the optimal state, obtaining the optimal state information and realizing the maximum range coverage of the target area. Compared with the prior art, the method has better flexibility and adaptability, and can cope with conditions such as single-point faults, cluster expansion and the like.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle clustering, in particular to an intelligent optimization method for cooperative task area coverage of an unmanned aerial vehicle clustering system.
Background
The problem of distributed coverage of unmanned aerial vehicle clusters is a hot problem in domestic and foreign research in recent years. The cluster carries out combined work through a large number of unmanned aerial vehicles with high speed and strong adaptability, and the demands of cost performance are met while the network communication and self-adaptive cooperative advantages are kept. Therefore, the unmanned aerial vehicle has cluster intelligence in the process of operating and executing tasks, and further has multiple advantages of economy, quantity, collaboration, intelligence, quick response and the like.
Along with the rapid development of relevant technologies such as communication, navigation, internet of things, big data and artificial intelligence, the cooperative task ability of the unmanned aerial vehicle cluster system is continuously enhanced, and due to the development of materials and structural strength of design, the cost performance of the unmanned aerial vehicle cluster system is also remarkably improved, so that the system can be applied to relevant fields more rapidly. However, the combination of the development conditions at home and abroad shows that the scale of the unmanned aerial vehicle system is mainly used for a small number of unmanned aerial vehicles to complete target tasks in a relatively simple environment. For tasks in a complex environment and large-scale cooperative situations of unmanned aerial vehicles, the problems of high task difficulty, complex adaptive design thought, complex functional algorithm and the like still exist. How to bring the advantages of flexibility and adaptability into play through coordination and coordination of clusters, and to satisfactorily complete tasks under various conditions of considering collision and obstacle avoidance, has become a hotspot of current research.
Disclosure of Invention
In order to solve the problems, the invention discloses an unmanned cluster system cooperative task area coverage intelligent optimization method, which solves the problem of large calculation amount of dynamics solving, is easy to adapt to a distributed area coverage optimization method of unmanned cluster topological structure change, does not depend on global information of central control, and only depends on information of adjacent unmanned aerial vehicles to guide an unmanned cluster to complete a coverage task.
An intelligent optimization method for cooperative task area coverage of an unmanned cluster system comprises the following steps:
step 1, determining a task area, building a cluster distributed network with an unmanned aerial vehicle as a base station, creating a cluster unmanned aerial vehicle sensing area and acquiring an importance factor of the task area. The importance factor represents important prior value information of some areas in the task area, such as target points of post-disaster material delivery, places needing to be included in communication coverage, and the like.
And 3, realizing the distributed control of the unmanned aerial vehicle cluster, and adjusting the input control law of the unmanned aerial vehicle cluster system according to the expected state information obtained in the step 2 to enable the cluster to reach the optimal state, thereby realizing the maximum coverage of the target area.
Preferably, step 1 comprises: step 1.1, acquiring unmanned aerial vehicle cluster state information and setting the cluster state information; step 1.2, the unmanned aerial vehicle cluster realizes communication connection, and each unmanned aerial vehicle carries out information interaction with an adjacent unmanned aerial vehicle through a 5G communication module; and 1.3, creating an unmanned aerial vehicle sensing area through a visual sensor module carried by the unmanned aerial vehicle in the cluster.
Preferably, the detection range of the cluster in the step 1.3 is an elliptical cross section obtained by intersecting a conical view field of the vision sensor module and a task area plane, and the center of the detection range is the projection center of the unmanned aerial vehicle in the task area; and considering the uncertainty of each unmanned aerial vehicle, creating a cluster unmanned aerial vehicle guarantee sensing area so that the unmanned aerial vehicle guarantee sensing area covers the uncertainty area.
Preferably, step 2 specifically comprises: step 2.1, dividing the task area into a plurality of sub-areas by using a GV diagram method, and distributing the sub-areas to each unmanned aerial vehicle according to the guaranteed sensing area and the coverage quality of the unmanned aerial vehicle to form a responsibility area of the unmanned aerial vehicle; when the sub-regions are distributed, whether information interaction is carried out between the unmanned aerial vehicles and the adjacent unmanned aerial vehicle set is judged, the information interaction means that interaction of state information of each unmanned aerial vehicle can be carried out between the cluster unmanned aerial vehicles, and the state information comprises information such as the positions of the unmanned aerial vehicles and the translation and inclination of the visual sensor. The specific judgment is to check whether the inter-cluster communication module can normally receive and send messages. If the judgment result is that the unmanned aerial vehicle is not overlapped with the adjacent unmanned aerial vehicle, the step 2.2 is carried out, otherwise, whether the unmanned aerial vehicle is overlapped with the sensing area guaranteed by the adjacent unmanned aerial vehicle is continuously judged, and if the judgment result is yes, the task subarea allocated to the unmanned aerial vehicle needs to be updated; dividing the whole task area into allocated and unallocated areas by a GV map method; and 2.2, constructing a sensor coverage quality function, distributing importance factors for each unmanned aerial vehicle through a GV (global v-map), and calculating a coverage quality target through the sensor coverage quality function and the importance factors to obtain cluster expected state information.
Preferably, the method for judging whether the unmanned aerial vehicle and the adjacent unmanned aerial vehicle guarantee that the sensing areas are overlapped is as follows: determining a coverage quality function of the unmanned aerial vehicle and the adjacent unmanned aerial vehicle, if the coverage quality of the unmanned aerial vehicle is less than or equal to that of the adjacent unmanned aerial vehicle, judging that the unmanned aerial vehicle is overlapped, updating a task sub-area, and reallocating the task sub-area to the cluster unmanned aerial vehicle; otherwise, the procedure goes to step 2.2.
Preferably, the coverage quality target reflects an area covered by the cluster and the coverage quality of the area, and also includes the importance of each unmanned aerial vehicle in the cluster, and the coverage quality target of the unmanned aerial vehicle is maximized, so that the maximum coverage of the cluster is realized.
Preferably, each drone is assigned to a sub-area importance factor
The sensor coverage quality function is expressed as:
Preferably, step 3 specifically comprises: step 3.1, setting the cluster rate and adjusting the cluster control law according to the expected state information; and 3.2, under the action of the cluster control law in the step 3.2, optimizing and converging the system state information of the unmanned aerial vehicles in the cluster to a local optimal state, and enabling the unmanned aerial vehicles to reach a final balance state to realize the maximum range coverage of the cluster to the task area.
The control law comprises control laws for the translation amount, the inclination angle and the view cone angle of a sensor of the unmanned aerial vehicle i, and the control laws respectively specifically comprise the following steps:
wherein A isi,θ,Ai,h,Ai,δRespectively representing a sensor translation coefficient, a sensor inclination coefficient and a sensor view cone angle coefficient of a cluster unmanned aerial vehicle in a task area, O representing a residual area which is not covered by a cluster guarantee perception area in a target area, k being an outward normal vector on a sub-area W, f being a coverage quality function of the unmanned aerial vehicle, and delta fij＝fi-fjRepresenting the coverage quality function difference of drone i from its neighboring drone j,
for the sensor translation variable phi to be the importance factor,
for the angular variation of the viewing cone of the sensor, deltaiFor half the cone range, subscript i and subscript j denote drone i and its neighboring drone j.
Preferably, the final state of equilibrium reached by the drone is:
where H is the coverage quality target, qiFor the position of the projected point on the task plane, ziFor height information, θiFor the amount of translation of the vision sensor, deltaiIs half cone range, hiFor the tilt angle of the vision sensor, the subscript i denotes drone i and the superscript indicates the desired state.
Has the advantages that:
(1) the unmanned aerial vehicle cluster provided by the invention optimizes the coverage of a task area, and uses a plurality of unmanned aerial vehicles as aerial base stations to carry out point-to-point connection respectively. The fault tolerance rate of a single unmanned aerial vehicle during task execution is improved. Even if some unmanned aerial vehicles break down, the cluster can still maintain the coverage effect on the task area through network reconfiguration;
(2) the change adaptability of the unmanned aerial vehicle cluster in the invention enhances the expandability of the cluster. When the coverage area needs to be enlarged and the coverage effect needs to be enhanced in the task area, unmanned aerial vehicles outside the cluster can be directly deployed to participate in the cluster task, and the cluster which is started and operated does not need to be stopped;
(3) in the invention, the deployment of the cluster unmanned aerial vehicle divides the task area by adopting the sensing area, so that the final state of the cluster unmanned aerial vehicle reaches the maximum value of the coverage target, and the optimization of the coverage effect of the cluster area is realized;
(4) the vision sensor carried by the cluster unmanned aerial vehicle can enlarge the view field range of a single unmanned aerial vehicle and improve the optimization speed of covering a task area. The cluster unmanned aerial vehicle area coverage can be applied to places with high requirements on coverage areas, such as disaster areas and battle areas. The method is high in flexibility and adaptability, and can be used for optimally covering a large-range area in scenes such as forest fire detection, communication network construction in disaster areas, city safety, infrastructure inspection and the like.
Drawings
FIG. 1 is a general flow diagram of a design optimization method according to one embodiment of the invention;
fig. 2 is a conceptual diagram of a cluster distributed network of drones according to an embodiment of the present invention;
FIG. 3 is a flowchart of a region partition optimization method according to an embodiment of the present invention;
fig. 4 is a flowchart of an unmanned aerial vehicle cluster distributed control method according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of unmanned aerial vehicle cluster area coverage simulation according to an embodiment of the present invention;
fig. 6 is a diagram of a change in coverage quality target for a cluster of drones according to an embodiment of the present invention;
fig. 7 is a diagram of the change of coverage of a target area by a cluster of drones according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method focuses on the dynamics solution and algorithm design of the input of mathematical model establishment, control law and the like, realizes the maximization of the cooperative self-adaptive coverage effect of all unmanned aerial vehicles in the target environment in the process of planning the area coverage tasks by the cluster, designs a reasonable algorithm to partition the target detection area of the unmanned aerial vehicle, plans the search path, and realizes the quick coverage search of the target area on the premise of ensuring the coverage quality. Wherein unmanned aerial vehicle's task is that the cluster perception covers target environment space, and every unmanned aerial vehicle has all been equipped with the sensor that is fit for the task, can carry out inter-communication in the cluster simultaneously, and then form a sensor network, collect information and explore the environment. The self-organizing network has good adaptability and can adapt to single-point faults, partial communication information loss and accommodation of heterogeneous teams.
With reference to fig. 1 to 3, specifically includes the following steps,
the unmanned aerial vehicle cluster distributed network building module comprises the following steps: step 1, determining a task area, building a cluster distributed network with an unmanned aerial vehicle as a base station, and creating a cluster unmanned aerial vehicle sensing area. In particular, the method comprises the following steps of,
step 1.1, acquiring unmanned aerial vehicle cluster state information, determining task area information and unmanned aerial vehicle cluster parameter information, and setting cluster state information. The task area information comprises a task area range, and the shape of the task area can be a regular graph or an irregular graph.
In the embodiment, the task area is set to be an area with a high requirement for site coverage, such as a disaster area or an operation environment, and the cluster unmanned aerial vehicle collaborative planning is performed on the task area by taking the optimal coverage rate as a target, so that the communication or inspection tour monitoring requirement is realized. And after the task area is confirmed, acquiring the position information of the task area and sending the position information to the unmanned aerial vehicle cluster.
Step 1.2, the unmanned aerial vehicle cluster realizes communication connection, a 5G communication module for information interaction is carried on the cluster unmanned aerial vehicle, and respective state information is transmitted with adjacent unmanned aerial vehicles by utilizing the characteristic of 5G high-speed movement switching. An aerial communication network is constructed by taking unmanned aerial vehicles in the cluster as base stations, communication in the maximum range of a communication limited area is achieved, and the unmanned aerial vehicles in the cluster are distributed according to the perception areas of the unmanned aerial vehicles.
And 1.3, creating an unmanned aerial vehicle sensing area through a visual sensor module carried by the unmanned aerial vehicle in the cluster. The unmanned aerial vehicle cluster parameter information comprises the number I of unmanned aerial vehicles in the clusternPredetermined height range [ z ]min,zmax]And a detection range for mounting a vision sensor. The detection range of the unmanned aerial vehicle is the detection range of the vision sensor, the detection range comprises an elliptical section obtained by intersecting a conical view field of the sensor and a task plane, and the detection rangeThe center of the enclosure is a projection center of the unmanned aerial vehicle in the task area. Sensor sensing area of each unmanned aerial vehicle
Expressed as:
wherein, XiPosition coordinate information, h, representing drone iiAnd thetaiRespectively representing the inclination angle and translation, delta, of the vision sensoriIs the visual angle of the visual sensor, R is a second order rotation matrix,
is the original sensing region of unmanned aerial vehicle i, qi,cFor the position of the projection point of the unmanned aerial vehicle I on the task plane, InNumber of unmanned aerial vehicle clusters.
qi，cCalculated by the following formula:
wherein, | | | | represents the Euclidean metric, aiAnd biThe major and minor semi-axes, h, of the cross-sectioniAnd thetaiRespectively representing the inclination angle and the translation amount of the vision sensor, and the viewing cone range is 2 deltai，ziIs altitude information of the drone i.
Because of the inherent inaccuracy of the GPS sensor in the cluster unmanned aerial vehicle, when the unmanned aerial vehicle sensing area is established, the uncertainty boundary of each unmanned aerial vehicle in the cluster is defined as ri＝[ri q,ri z]T，ri q，ri zWhich represent the boundary values of the uncertainty area in the horizontal and vertical directions, respectively. Position X of unmanned aerial vehicleiMay be located anywhere in the uncertainty region
Expressed as:
thus, the guaranteed sensing area of drone i takes into account the uncertainty of each drone
Representation of the area containing drone i in its uncertainty region
The area of all possible locations within. Thus, the guaranteed sensing area of drone i
The creation is represented as:
wherein the content of the first and second substances,
for the perception areas of the unmanned aerial vehicle i with different heights in the uncertain area, the perception areas are calculated according to the following formula:
wherein the content of the first and second substances,
the original guaranteed sensing area of drone i is represented, and Ω represents the task target area.
The distributed network of unmanned aerial vehicle clusters is constructed as shown in fig. 2. The figure shows drone i and its set of neighboring drones NiIs sensed byRegion for an angle of inclination h i0 and
the guaranteed perception area of drone i is shown in shadow.
And 2, task area division optimization. As shown in fig. 3, a flow of the task area partition optimization method is designed for the present invention. The method specifically comprises the following steps:
and 2.1, dividing the task area by using a GV graph method. Unlike a conventional Voronoi diagram, a GV diagram (guarded Voronoi) divides the whole task area into several sub-areas to allocate to the whole cluster by a cluster of drones with positioning uncertainty. Guaranteed sensing area according to unmanned aerial vehicle
And the coverage quality allocates a responsibility area for each drone. Sub-area W allocated by cluster unmanned aerial vehicleiExpressed as:
When the cluster unmanned aerial vehicle i is divided into sub-regions, checking all adjacent unmanned aerial vehicles j belonging to NiWhether the perception areas are overlapped with the unmanned aerial vehicle i or not, if the perception areas are overlapped, the coverage quality of the unmanned aerial vehicle i is less than or equal to that of the unmanned aerial vehicle j. And updating the sub-area divided to the unmanned aerial vehicle i.
Sub-region WiThe union of (a) cannot guarantee the sensing area of the cluster in total
And (4) carrying out complete mosaic, and when the task area is divided into the unmanned aerial vehicle cluster, the unmanned aerial vehicle i and the area with the same coverage quality in the adjacent unmanned aerial vehicle cluster cannot be distributed. But these areas still have an impact on the coverage quality target H, so we will have the same coverage quality f in drone i and its neighboring setlAndthe unmanned aerial vehicles with overlapped perception areas form a new set L, the number in the set is Ln, and the number is expressed as:
wherein, IL＝{1,2,...,Ln}。
Thus, unallocated area
For the unmanned aerial vehicle in set L guarantee that the perception area appears the partial region that overlaps, show as:
the GV map divides the entire task area into allocated and unallocated areas.
And 2.2, calculating a coverage optimization target H for the unmanned aerial vehicle in the cluster. The coverage optimization objective includes a sensor coverage quality function fiImportance factor to which each drone is assigned
Sensor coverage quality function fiDepending on the distance between the object to be photographed and the sensor, the object at a longer distance is photographed at a much lower quality than the object near the sensor. Coverage quality function fiExpressed as:
where 0 and 1 correspond to the lowest and highest quality of the sensor, respectively.
The projection q ∈ omega of each unmanned aerial vehicle in the cluster is distributed to an importance factor through a GV graph
Indicating information about the task region omegaA priori information of intrinsic importance. The coverage quality target is expressed as follows:
in the invention, the coverage quality target comprehensively considers the area covered by the cluster and the coverage quality of the area, and also includes the importance of each unmanned aerial vehicle in the cluster, so that the coverage quality target H of the unmanned aerial vehicle cluster is maximized, namely the coverage of the cluster in the maximized range is realized. According to the partitioning strategy W in the step 4iAnd
the coverage quality target representation is updated as:
as shown in fig. 3, the unmanned aerial vehicle cluster expectation state information (position, altitude, translation amount, tilt angle, and view angle) is output according to the input quantities such as the coverage quality function and the importance factor in the coverage quality target.
And 3, performing distributed control on the unmanned aerial vehicle cluster. As shown in fig. 4, a flow of a distributed control method for a cluster of drones is shown.
And 3.1, setting the cluster rate and adjusting the cluster control law according to the expected state information. And judging the conditions of the unmanned aerial vehicle i, and adjusting the cluster control law according to the expected state information of the unmanned aerial vehicle, so that the cluster distribution information is updated. Only guarantee that unmanned aerial vehicle and neighbouring unmanned aerial vehicle have the information interaction, just can carry out the status information transmission between the unmanned aerial vehicle to carry out the judgement that the perception region overlaps between, and then to the sub-region of cluster unmanned aerial vehicle distribution. And judging the coincidence of the sensing areas of the unmanned aerial vehicle i and the adjacent unmanned aerial vehicle set, and updating the responsibility area to obtain the traversed adjacent unmanned aerial vehicle set Ni.
The termination conditions were determined as follows: if the unmanned aerial vehicle i and the adjacent unmanned aerial vehicle keep communication connectionAnd then broadcasting the self state information to the adjacent unmanned aerial vehicle set NiAnd simultaneously receiving the state information of the adjacent unmanned aerial vehicles, judging the coincidence of the sensing areas of the unmanned aerial vehicle i and the adjacent unmanned aerial vehicle set, and updating the responsibility area to finish traversing the adjacent unmanned aerial vehicle set NiAnd updating the sub-area of the unmanned plane i. According to the sensing area and the unmanned aerial vehicle cluster with positioning uncertainty, in order to ensure that the coverage quality target H keeps monotonous increase, the time derivative is expressed as:
in the present invention, the selection control inputs are as follows:
wherein the coefficient Ai,q,Ai,z,Ai,θ,Ai,h,Ai,δAnd respectively representing a projection position coefficient, an unmanned aerial vehicle height coefficient, a sensor translation coefficient, a sensor inclination coefficient and a sensor view cone angle coefficient of the cluster unmanned aerial vehicle in the task area.
In the invention, the coefficient A is ensuredi,q,Ai,z,Ai,θ,Ai,h,Ai,δIs greater than or equal to 0, therefore
Non-negative, ensuring that the coverage quality target monotonically increases. The following control laws are set for the projection position, the height, the translation amount, the inclination angle and the view cone angle of the unmanned aerial vehicle i:
wherein A isi,q,Ai,z,Ai,θ,Ai,h,Ai,δFor a positive scale factor, O denotes that the sensing area in the target area is not covered by the cluster guarantee sensing areaTo the remaining region, kiIs in a sub-region WiUpward outward normal vector, Δ fij＝fi-fjParameters representing the difference of the coverage quality function of the unmanned aerial vehicle i and the adjacent unmanned aerial vehicle j, and respectively representing the projection position variable, the unmanned aerial vehicle height variable, the sensor translation variable, the sensor inclination variable and the sensor view cone angle variable
The jacobian matrices are respectively expressed as:
step 3.2, under the action of the control law, system state information q of the unmanned aerial vehicles in the clusteri，zi，θi，hi，δi，
After the optimization method, the final convergence to the local optimal state is achieved, and at this time, there are:
each unmanned aerial vehicle in the cluster reaches a final balance state
Such that:
the cooperative area coverage intelligent optimization method of the cluster unmanned aerial vehicle system can carry out maximum range communication or inspection on the task area. And establishing an unmanned aerial vehicle sensing area, obtaining cluster unmanned aerial vehicle state information, dividing a task area through a GV (global-virtual) diagram, calculating a cluster coverage quality target, outputting optimal distribution state information of unmanned aerial vehicles in a cluster, and realizing cluster coverage in the largest range of the task area.
The following simulation example is used to illustrate the feasibility of the method of the present patent.
Cluster (I) formed by six unmanned aerial vehiclesn6) performing area coverage, and setting the task area as an irregular graph. Cluster height set to [ z ]min，zmax]＝[0.3，2.3]The projection position coefficient, the unmanned plane height coefficient, the sensor translation coefficient, the sensor inclination coefficient and the sensor view angle coefficient of the cluster unmanned plane in the task area are set as follows:
Ai,q＝[0.25,0.25],Ai,z＝0.25,Ai,θ＝0.0005,Ai,h＝0.0005,Ai,δ0.0005, the range delta of the view angle of the sensor carried by the cluster unmanned aerial vehicle belongs to (15 degrees and 30 degrees), and the inclination angle limit value h of the sensor is set aslim30 ° is set. The simulation time is 150s, the simulation step length is set to be 0.1, as shown in fig. 5 to 7, a schematic coverage diagram of the cluster final area of the unmanned aerial vehicle, a coverage quality target change diagram and a coverage rate change diagram are obtained, the final coverage rate of the cluster unmanned aerial vehicle can reach 87.3% under the method, and the cluster coverage of the task area in the largest range is achieved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent optimization method for cooperative task area coverage of an unmanned cluster system is characterized by comprising the following steps:
step 1, determining a task area, building a cluster distributed network with an unmanned aerial vehicle as a base station, creating a cluster unmanned aerial vehicle sensing area and acquiring an importance factor of the task area;
step 2, based on the perception area of the cluster unmanned aerial vehicle and the importance factor of the task area, distributing the task area to the responsibility area of each unmanned aerial vehicle in the cluster by using a GV graph method; constructing a coverage quality function and calculating a coverage quality target of the unmanned aerial vehicle in the cluster according to the importance factor of the task area to obtain an expected state of the unmanned aerial vehicle;
and 3, realizing the distributed control of the unmanned aerial vehicle cluster, and adjusting the input control law of the unmanned aerial vehicle cluster system according to the expected state information obtained in the step 2 to enable the cluster to reach the optimal state, thereby realizing the maximum coverage of the target area.
2. The unmanned cluster system collaborative task area coverage intelligent optimization method of claim 1, characterized in that: the step 1 comprises the following steps:
step 1.1, acquiring unmanned aerial vehicle cluster state information and setting the cluster state information;
step 1.2, the unmanned aerial vehicle cluster realizes communication connection, and each unmanned aerial vehicle carries out information interaction with an adjacent unmanned aerial vehicle through a 5G communication module;
and 1.3, creating an unmanned aerial vehicle sensing area through a visual sensor module carried by the unmanned aerial vehicle in the cluster.
3. The unmanned cluster system collaborative task area coverage intelligent optimization method of claim 2, characterized in that: the detection range of the cluster in the step 1.3 is an elliptical section obtained by intersecting a conical view field of the vision sensor module and a task area plane, and the center of the detection range is the projection center of the unmanned aerial vehicle in the task area; and considering the uncertainty of each unmanned aerial vehicle, creating a cluster unmanned aerial vehicle guarantee sensing area so that the unmanned aerial vehicle guarantee sensing area covers the uncertainty area.
4. The unmanned cluster system collaborative task area coverage intelligent optimization method according to claim 1 or 3, characterized in that: the step 2 specifically comprises the following steps:
step 2.1, dividing the task area into a plurality of sub-areas by using a GV diagram method, and distributing the sub-areas to the responsibility areas of all unmanned aerial vehicles in the cluster according to the guaranteed sensing areas of the unmanned aerial vehicles and the importance factors of the task area; when sub-areas are allocated, judging whether information interaction is carried out between the unmanned aerial vehicle and the adjacent unmanned aerial vehicle set, if not, switching to the step 2.2, otherwise, continuously judging whether the unmanned aerial vehicle and the adjacent unmanned aerial vehicle guarantee that sensing areas are overlapped, and if so, updating the task sub-areas allocated to the unmanned aerial vehicle; dividing the whole task area into allocated and unallocated areas by a GV map method;
and 2.2, constructing a sensor coverage quality function, and calculating a coverage quality target through the sensor coverage quality function and the importance factor to obtain cluster expected state information.
5. The unmanned cluster system collaborative task area coverage intelligent optimization method of claim 4, characterized in that: the method for judging whether the unmanned aerial vehicle and the adjacent unmanned aerial vehicle guarantee that the sensing areas are overlapped comprises the following steps: determining a coverage quality function of the unmanned aerial vehicle and the adjacent unmanned aerial vehicle, if the coverage quality of the unmanned aerial vehicle is less than or equal to that of the adjacent unmanned aerial vehicle, judging that the unmanned aerial vehicle is overlapped, updating a task sub-area, and reallocating the task sub-area to the cluster unmanned aerial vehicle; otherwise, the procedure goes to step 2.2.
6. The unmanned cluster system collaborative task area coverage intelligent optimization method of claim 5, characterized in that: the coverage quality target reflects the area covered by the cluster and the coverage quality of the area, and also comprises the importance of each unmanned aerial vehicle in the cluster, and the coverage of the maximum range of the cluster is realized by maximizing the coverage quality target of the unmanned aerial vehicle.
7. The unmanned clustered system collaborative task area coverage intelligent optimization method of claim 6, wherein: the importance factor of each drone being assigned to a sub-area
Sensor with a sensor elementThe coverage quality function is expressed as:
wherein z isiHeight information, h, representing unmanned aerial vehicle iiIndicating the amount of visual sensor tilt, δiIndicating the viewing cone angle of the vision sensor,
representing a set minimum height of drone i, 0 and 1 corresponding to the minimum and maximum mass of the sensor, respectively; the coverage quality target is expressed as
Where Ω denotes the task area, InIndicates the number of cluster drones and phi indicates the importance factor to which the cluster drones are assigned.
8. The unmanned clustered system collaborative task area coverage intelligent optimization method of claim 7, wherein: the step 3 specifically comprises the following steps:
step 3.1, setting the cluster rate and adjusting the cluster control law according to the expected state information;
and 3.2, under the action of the cluster control law in the step 3.2, optimizing and converging the system state information of the unmanned aerial vehicles in the cluster to a local optimal state, and enabling the unmanned aerial vehicles to reach a final balance state to realize the maximum range coverage of the cluster to the task area.
9. The unmanned cluster system collaborative task area coverage intelligent optimization method according to claim 1 or 8, characterized in that: the control law comprises control laws for the translation amount, the inclination angle and the view cone angle of a sensor of the unmanned aerial vehicle i, and the control laws respectively comprise:
wherein A isi,θ,Ai,h,Ai,δRespectively representing a sensor translation coefficient, a sensor inclination coefficient and a sensor view cone angle coefficient of a cluster unmanned aerial vehicle in a task area, O representing a residual area which is not covered by a cluster guarantee perception area in a target area, k being an outward normal vector on a sub-area W, f being a coverage quality function of the unmanned aerial vehicle, and delta fij＝fi-fjRepresenting the coverage quality function difference of drone i from its neighboring drone j,
for the sensor translation variable phi to be the importance factor,
for the angular variation of the viewing cone of the sensor, deltaiFor half the cone range, subscript i and subscript j denote drone i and its neighboring drone j.
10. The unmanned clustered system collaborative task area coverage intelligent optimization method of claim 9, wherein: the final equilibrium state reached by the unmanned aerial vehicle is:
where H is the coverage quality target, qiFor the position of the projected point on the task plane, ziFor height information, θiFor the amount of translation of the vision sensor, deltaiIs half cone range, hiFor the inclination angle of the vision sensor, the index i indicates drone i and the superscript indicatesA desired state.
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