CN109669357A - Path planning verification and multi-platform control system and the method for multitask unmanned plane - Google Patents
Path planning verification and multi-platform control system and the method for multitask unmanned plane Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
Present invention relates particularly to a kind of verifications of the path planning of multitask unmanned plane and multi-platform control system and method, belong to air vehicle technique field.The control system includes unmanned plane and earth station；The control method includes: 1) to carry out clustering as data object to spot for N number of；2) path planning is carried out to spot in each cluster using ant group algorithm；3) earth station's operating system carries out clustering to obtain maximum detection target total value and the smallest total flying distance as target；4) optimal path is generated；5) ground station control system carries out realistically displayed to optimal path, and controls unmanned plane and carry out practical flight according to optimal path.The present invention can automatically plan the flight path of unmanned plane, so that unmanned plane during execution task, obtains maximum value, while can reduce flying distance to the greatest extent, shorten the flight time, improve the efficiency that unmanned plane executes task.
Description
Technical field
The invention belongs to air vehicle technique fields, and in particular to a kind of path planning verification of multitask unmanned plane with it is mostly flat
Bench control system and method.
Background technique
Four-axle aircraft is answered in fields such as agricultural plant protection, scouting detection, rescue, information relayings extensively in recent years
With.However traditional single four-axle aircraft has many limitations in the task of execution:
Such as in the field of taking photo by plane, but when single aircraft is taken photo by plane, can only obtain the image at a visual angle, cannot be comprehensive
Ground is monitored；Field is transported in express delivery, single rack four-axle aircraft freight volume is insufficient, if being made into the four axis flight of heavy load
Device will cause the wasting of resources when being transported in a small amount again；The monitoring efficiency of single four-axle aircraft in forest fire protection monitoring
It is low, monitoring range and precision deficiency etc..
Summary of the invention
For the above technical problems, the path planning that the present invention provides a kind of multitask unmanned plane is verified and is put down more
Bench control system, comprising: including unmanned plane and earth station；
The unmanned plane includes flying control plate, airborne computer, GPS module, communication module and aircraft；The winged control plate point
It is not connect with airborne computer, GPS module and aircraft；The communication module is connect with control plate is flown；
When carrying out the realistically displayed of optimal path, the winged control plate and airborne computer of the earth station and unmanned plane pass through
Data line connection；
When carrying out multi-platform control, communication link bus is established between the earth station and the communication module of unmanned plane.
The earth station is PC, includes goal programming system, path planning system, multi-platform control system in control system
System and the analogue system based on jMAVSim simulator and unmanned plane mathematical model.
The communication module uses 433M wireless module.
The control method of the path planning verification and multi-platform control system of a kind of multitask unmanned plane, using above-mentioned more
Path planning verification and the multi-platform control system of task unmanned plane, comprising the following steps:
Step 1, the goal programming system based on K-Means method is established in the control system of earth station；By N number of wait detect
The observation of eyes is denoted as carrying out clustering for data object, using K-Means method, comprising the following steps:
Step 1.1, arbitrarily select L data object as cluster centre from N number of data object；
Step 1.2, using L-N data object other than cluster centre as clustering object, calculate each clustering object with
The distance of each initial cluster center, and each clustering object is ranged away from nearest initial cluster center, obtain L
Cluster；
Step 1.3, to each cluster, the mean center of all data objects in cluster is sought；
Step 1.4, the mean center of cluster and the distance of cluster centre are calculated；If distance is less than threshold value, meter is terminated
It calculates, obtains L cluster；Otherwise, using mean center as cluster centre, return step 1.2；
Step 2, the path planning system based on ant group algorithm is established in the control system of earth station；Using ant group algorithm
Path planning is carried out to spot in each cluster；
Step 2.1, a to spot for any one cluster l in L cluster, including C, by each to target of investication
Position be arranged in path planning system；
In path planning system, line is generated two-by-two between all targets, it can when as unmanned plane from a certain target
The path of selection, i.e. two o'clock path；Corresponding pheromone concentration initial value D is assigned for the pathi；Pheromone concentration initial value DiWith two
Self-value V on point path as the target of starting pointjIt is proportional, i.e. Di=k × Vj, wherein k is proportionality coefficient；
Step 2.2, the cluster l includes D unmanned plane Departed Station and the E frame for being arranged in each unmanned plane Departed Station
Unmanned plane；
The target nearest apart from each unmanned plane Departed Station is set as initial target, obtains the base that takes off with each unmanned plane
The corresponding D initial target in ground；
It controls on the E frame unmanned plane during flying to corresponding each initial target of each unmanned plane Departed Station；
Step 2.3, control target of each unmanned plane where itself it is random to other target flights, and set it not
It can fly the target having been subjected into this step, specific control method are as follows:
Unmanned plane is from initial target on, flight to next target；Unmanned plane has C-1 item can on arbitrary target
The two o'clock path of choosing, the selection wherein any one two o'clock path for being directed toward the target that it was not yet reached；
When unmanned plane traversed whole C after spot, the C-1 two o'clock path passed it through is recorded simultaneously
It is integrated into a fullpath, returns initial target；
Above content is executed repeatedly, obtains several fullpaths, until experiment terminates；
In the flight course of unmanned plane, the release pheromone on the two o'clock path of process, as time goes by, pheromones meeting
It dribbles；
The actual information element concentration in this section of two o'clock path is pheromone concentration initial value DiIt is total in the release and dissipation of pheromones
Result after same-action；Therefore, unmanned plane is more on a certain two o'clock path, and practical pheromone concentration is higher；Path planning system
According to actual information element concentration levels, controls each unmanned plane and preferentially fly along the highly concentrated path of actual information element；
Step 2.5, the highest fullpath of pheromone concentration in cluster l, i.e. preferred path p are obtained；
According to this step the method, the preferred path of each cluster is obtained；
The content of step 2 all complete in path planning system by operation, without practical flight；
Step 3, the control system of earth station is to obtain maximum detection target total value and the smallest total flying distance is
Target carries out clustering；
Step 3.1, the detection target total value minf of preferred path p is calculated1:
Detect target total value minf1Constituent element include each target to be detected self-value and detect successfully
Rate, statement are as follows:
In formula, PRlpDetection when taking action to cluster the unmanned plane of l according to preferred path p, to j-th of target on the path
Success rate；
J-th of target on the p of path is denoted as jp, VjFor jpSelf-value, VjpFor jpValue on the p of path；Vmax
For VjpMaximum value；
xlpFor the unmanned plane quantity for passing through preferred path p in cluster l；
NaFor the summation of unmanned plane quantity in each cluster；
Step 3.2, total flying distance minf of preferred path p is calculated2:
In formula, DlpTo cluster the flying distance summation when unmanned plane of l is taken action according to path p；
RlFor UAV combat radius；
For the length of longest path,
λ1, λ2For weight, λ1+λ2=1, λ1,λ2≥0；
1/RlNaFor planning factors；
According to the above method, obtain the preferred path of each cluster detection target total value and the smallest total flight away from
From；
Step 3.3, target exercise value f is calculated3；
The generating process of optimal path is by detection target total value minf1With total flying distance minf2Influence；It calculates
The target exercise value f of each preferred path3:
f3=k1*minf1+k2minf2
K1, k2 are weight, k1+k2=1, k1,k2≥0；
Step 4, optimal path is generated；
To realize maximum target value, G value calculation is carried out, that is, repeats G step 2- step 3；Every time into
When row value calculation, different proportionality coefficient k is given, calculates different k bring G group target exercise value f3；
The control system of earth station takes target exercise value f3The smallest preferred path is optimal path, the knot being calculated
Fruit is uploaded to the flight control system of unmanned plane, the practical flight path as unmanned plane；
Step 5, ground station control system to optimal path carry out realistically displayed, and control unmanned plane according to optimal path into
Row practical flight.
In the step 5, realistically displayed is carried out to optimal path, method particularly includes:
The analogue system based on jMAVSim simulator and unmanned plane mathematical model is established in the control system of earth station,
Hardware in loop semi-physical simulation is carried out to unmanned plane；
The winged control plate and airborne computer of unmanned plane are removed from aircraft, and connect with earth station by data line；Ground
The analogue system at face station provides different flight environment of vehicle parameters, by flying control plate and the signal that receives of airborne computer and given
Instruction, simulate the flight condition of unmanned plane；
Fly control plate and airborne computer and the winged control of aircraft is instructed according to practical flight coordinates measurement, i.e., to each propeller
The control instruction of revolving speed；
The analogue system of earth station reads the winged control instruction of flight control system in real time, and according to unmanned plane mathematical model, resolves
Flight path of the unmanned plane in the case where flying control instruction, and the information such as the unmanned plane position, speed, the direction that calculate are returned in real time and are flown
Plate and airborne computer are controlled, is generated by it and flies control instruction in next step, thus verify the reliability in practical flight path, until artificial
Terminate simulation process.
The step 5 controls unmanned plane movement according to multi-platform control system during unmanned plane practical flight,
Specifically:
Each frame unmanned plane is by self-position, velocity information and camera shooting information real-time delivery to communication link bus, simultaneously
From the relevant information of the other unmanned planes of communication link bus Real-Time Sharing, at the same earth station according to the information carry out each frame nobody
The path planning of machine constantly updates the flight path instruction of each frame unmanned plane in unmanned plane cluster flight course, realizes nobody
The optimization of machine formation flight effect.
Beneficial effects of the present invention:
The present invention proposes a kind of paths planning method of multiple no-manned plane task, can automatically to the flight path of unmanned plane into
Professional etiquette is drawn, so that unmanned plane obtains maximum value during execution task, while can reduce flight to the greatest extent
Distance shortens the flight time, improves the efficiency that unmanned plane executes task.
The present invention has rational design, it is easy to accomplish, there is good practical value.
Detailed description of the invention
Fig. 1 is the verification of path planning described in the specific embodiment of the invention and multi-platform control system in optimal path
Connection schematic diagram when realistically displayed；
Fig. 2 is that the verification of path planning described in the specific embodiment of the invention and multi-platform control system are multi-platform in progress
Connection schematic diagram when control.
In figure: 1, earth station；2, fly control plate；3, airborne computer；4, GPS module；5, communication module；6, aircraft；7,
Communication link bus.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing and embodiment,
Further description is made to the present invention.It should be appreciated that described herein, specific examples are only used to explain the present invention, and
It is not used in the restriction present invention.
The present invention proposes the path planning verification and multi-platform control system of a kind of multitask unmanned plane, including unmanned plane and
Earth station 1；
The unmanned plane includes flying control plate 2, airborne computer 3, GPS module 4, communication module 5 and aircraft 6；It is described to fly
Control plate 2 is connect with airborne computer 3, GPS module 4 and aircraft 6 respectively；The communication module 5 is connect with control plate 2 is flown；
The earth station 1 is PC, includes goal programming system, path planning system, multi-platform control in control system
System and analogue system based on jMAVSim simulator and unmanned plane mathematical model；
When carrying out the realistically displayed of optimal path, as shown in Figure 1, the earth station 1 and the winged control plate 2 of unmanned plane pass through
Data line connection；
When carrying out multi-platform control, as shown in Fig. 2, being established between the earth station 1 and the communication module 5 of unmanned plane logical
Believe junctor highway 7；
The communication module 5 uses 433M wireless module；
The present invention proposes the control method of the path planning verification and multi-platform control system of a kind of multitask unmanned plane, adopts
With the path planning verification and multi-platform control system of above-mentioned multitask unmanned plane, comprising the following steps:
Step 1, the goal programming system based on K-Means method is established in the control system of earth station 1；By it is N number of to
Spot carries out clustering as data object, using K-Means method, comprising the following steps:
Step 1.1, arbitrarily select L data object as cluster centre from N number of data object；
Step 1.2, using L-N data object other than cluster centre as clustering object, calculate each clustering object with
The distance of each initial cluster center, and each clustering object is ranged away from nearest initial cluster center, obtain L
Cluster；
Step 1.3, to each cluster, the mean center of all data objects in cluster is sought；
Step 1.4, the mean center of cluster and the distance of cluster centre are calculated；If distance is less than threshold value, meter is terminated
It calculates, obtains L cluster；Otherwise, using mean center as cluster centre, return step 1.2；
Step 2, the path planning system based on ant group algorithm is established in the control system of earth station 1；It is calculated using ant colony
Method carries out path planning to spot in each cluster；
Step 2.1, a to spot for any one cluster l in L cluster, including C, by each to target of investication
Position be arranged in path planning system；
In path planning system, line is generated two-by-two between all targets, it can when as unmanned plane from a certain target
The path of selection, i.e. two o'clock path；Corresponding pheromone concentration initial value D is assigned for the pathi；Pheromone concentration initial value DiWith two
Self-value V on point path as the target of starting pointjIt is proportional, i.e. Di=k × Vj, wherein k is proportionality coefficient；
Step 2.2, the cluster l includes D unmanned plane Departed Station and the E frame for being arranged in each unmanned plane Departed Station
Unmanned plane；
The target nearest apart from each unmanned plane Departed Station is set as initial target, obtains the base that takes off with each unmanned plane
The corresponding D initial target in ground；
It controls on the E frame unmanned plane during flying to corresponding each initial target of each unmanned plane Departed Station；
Step 2.3, control target of each unmanned plane where itself it is random to other target flights, and set it not
It can fly the target having been subjected into this step, specific control method are as follows:
Unmanned plane is from initial target on, flight to next target；Unmanned plane has C-1 item can on arbitrary target
The two o'clock path of choosing, the selection wherein any one two o'clock path for being directed toward the target that it was not yet reached；
When unmanned plane traversed whole C after spot, the C-1 two o'clock path passed it through is recorded simultaneously
It is integrated into a fullpath, returns initial target；
Above content is executed repeatedly, obtains several fullpaths, until experiment terminates；
In the flight course of unmanned plane, the release pheromone on the two o'clock path of process, as time goes by, pheromones meeting
It dribbles；
The actual information element concentration in this section of two o'clock path is pheromone concentration initial value DiIt is total in the release and dissipation of pheromones
Result after same-action；Therefore, unmanned plane is more on a certain two o'clock path, and practical pheromone concentration is higher；Path planning system
According to actual information element concentration levels, controls each unmanned plane and preferentially fly along the highly concentrated path of actual information element；
Step 2.5, the highest fullpath of pheromone concentration in cluster l, i.e. preferred path p are obtained；
According to this step the method, the preferred path of each cluster is obtained；
The content of step 2 all complete in path planning system by operation, without practical flight；
Step 3, the control system of earth station 1 is to obtain maximum detection target total value and the smallest total flying distance is
Target carries out clustering；
Step 3.1, the detection target total value minf of preferred path p is calculated1:
Detect target total value minf1Constituent element include each target to be detected self-value and detect successfully
Rate, statement are as follows:
In formula, PRlpDetection when taking action to cluster the unmanned plane of l according to preferred path p, to j-th of target on the path
Success rate；
J-th of target on the p of path is denoted as jp, VjFor jpSelf-value, VjpFor jpValue on the p of path；Vmax
For VjpMaximum value；
xlpFor the unmanned plane quantity for passing through preferred path p in cluster l；
NaFor the summation of unmanned plane quantity in each cluster；
Step 3.2, total flying distance minf of preferred path p is calculated2:
In formula, DlpTo cluster the flying distance summation when unmanned plane of l is taken action according to path p；
RlFor UAV combat radius；
For the length of longest path,
λ1, λ2For weight, λ1+λ2=1, λ1,λ2≥0；
1/RlNaFor planning factors；
According to the above method, obtain the preferred path of each cluster detection target total value and the smallest total flight away from
From；
Step 3.3, target exercise value f is calculated3；
The generating process of optimal path is by detection target total value minf1With total flying distance minf2Influence；It calculates
The target exercise value f of each preferred path3:
f3=k1*minf1+k2minf2
K1, k2 are weight, k1+k2=1, k1,k2≥0；；
Step 4, optimal path is generated；
To realize maximum target value, G value calculation is carried out, that is, repeats G step 2- step 3；Every time into
When row value calculation, different proportionality coefficient k is given, calculates different k bring G group target exercise value f3；
The control system of earth station 1 takes target exercise value f3The smallest preferred path is optimal path, is calculated
As a result it is uploaded to the flight control system of unmanned plane, the practical flight path as unmanned plane；
Step 5, the control system of earth station 1 carries out realistically displayed to optimal path, and controls unmanned plane according to best road
Diameter carries out practical flight.
In the step 5, realistically displayed is carried out to optimal path, method particularly includes:
The analogue system based on jMAVSim simulator and unmanned plane mathematical model is established in the control system of earth station 1,
Hardware in loop semi-physical simulation is carried out to unmanned plane；
The winged control plate 2 and airborne computer 3 of unmanned plane are removed from aircraft, and connect with earth station 1 by data line；
The analogue system of earth station 1 provides different flight environment of vehicle parameters, by fly control plate 2 and the signal that receives of airborne computer 3 and
The flight condition of unmanned plane is simulated in given instruction；
Fly control plate 2 and airborne computer 3 and the winged control of aircraft 6 is instructed according to practical flight coordinates measurement, i.e., to each spiral shell
Revolve the control instruction of paddle revolving speed；
The analogue system of earth station 1 reads the winged control instruction of flight control system in real time, and according to unmanned plane mathematical model, resolves
Flight path of the unmanned plane in the case where flying control instruction, and the information such as the unmanned plane position, speed, the direction that calculate are returned in real time and are flown
Plate 2 and airborne computer 3 are controlled, is generated by it and flies control instruction in next step, thus verify the reliability in practical flight path, until people
To terminate simulation process；
The step 5 controls unmanned plane movement according to multi-platform control system during unmanned plane practical flight,
Specifically:
Each frame unmanned plane is by self-position, velocity information and camera shooting information real-time delivery to communication link bus 7, simultaneously
From the relevant information of the other unmanned planes of 7 Real-Time Sharing of communication link bus, at the same earth station 1 according to the information carry out each frame without
Man-machine path planning constantly updates the flight path instruction of each frame unmanned plane in unmanned plane cluster flight course, realizes nothing
The optimization of man-machine formation flight effect.
Claims (6)
1. path planning verification and the multi-platform control system of a kind of multitask unmanned plane characterized by comprising including nobody
Machine and earth station；
The unmanned plane includes flying control plate, airborne computer, GPS module, communication module and aircraft；The winged control plate respectively with
Airborne computer, GPS module are connected with aircraft；The communication module is connect with control plate is flown；
When carrying out the realistically displayed of optimal path, the winged control plate and airborne computer of the earth station and unmanned plane pass through data
Line connection；
When carrying out multi-platform control, communication link bus is established between the earth station and the communication module of unmanned plane.
2. path planning verification and the multi-platform control system of multitask unmanned plane according to claim 1, feature exist
Be PC in, the earth station, include in control system goal programming system, path planning system, multi-platform control system and
Analogue system based on jMAVSim simulator and unmanned plane mathematical model.
3. path planning verification and the multi-platform control system of multitask unmanned plane according to claim 1, feature exist
In the communication module uses 433M wireless module.
4. the control method of the path planning verification and multi-platform control system of a kind of multitask unmanned plane, which is characterized in that adopt
With the path planning verification and multi-platform control system of multitask unmanned plane as claimed in claim 2, comprising the following steps:
Step 1, the goal programming system based on K-Means method is established in the control system of earth station；By N number of mesh to be scouted
It is denoted as carrying out clustering for data object, using K-Means method, comprising the following steps:
Step 1.1, arbitrarily select L data object as cluster centre from N number of data object；
Step 1.2, using L-N data object other than cluster centre as clustering object, calculate each clustering object with it is each
The distance of initial cluster center, and each clustering object is ranged away from nearest initial cluster center, it obtains L and gathers
Class；
Step 1.3, to each cluster, the mean center of all data objects in cluster is sought；
Step 1.4, the mean center of cluster and the distance of cluster centre are calculated；If distance is less than threshold value, calculating is terminated, is obtained
It is clustered to L；Otherwise, using mean center as cluster centre, return step 1.2；
Step 2, the path planning system based on ant group algorithm is established in the control system of earth station；Using ant group algorithm to each
Path planning is carried out to spot in cluster；
Step 2.1, a to spot for any one cluster l in L cluster, including C, by each position to target of investication
It installs in path planning system；
In path planning system, line is generated two-by-two between all targets, may be selected when as unmanned plane from a certain target
Path, i.e. two o'clock path；Corresponding pheromone concentration initial value D is assigned for the pathi；Pheromone concentration initial value DiWith two o'clock road
As the self-value V of the target of starting point on diameterjIt is proportional, i.e. Di=k × Vj, wherein k is proportionality coefficient；
Step 2.2, the cluster l include D unmanned plane Departed Station and be arranged in each unmanned plane Departed Station E frame nobody
Machine；
The target nearest apart from each unmanned plane Departed Station is set as initial target, is obtained and each unmanned plane Departed Station pair
The D initial target answered；
It controls on the E frame unmanned plane during flying to corresponding each initial target of each unmanned plane Departed Station；
Step 2.3, control target of each unmanned plane where itself it is random to other target flights, and set it and will not fly
To the target being had been subjected in this step, specific control method are as follows:
Unmanned plane is from initial target on, flight to next target；Unmanned plane has C-1 item optional on arbitrary target
Two o'clock path, the selection wherein any one two o'clock path for being directed toward the target that it was not yet reached；
When unmanned plane traversed whole C after spot, the C-1 two o'clock path passed it through is recorded and is integrated
For a fullpath, initial target is returned；
Above content is executed repeatedly, obtains several fullpaths, until experiment terminates；
In the flight course of unmanned plane, the release pheromone on the two o'clock path of process, as time goes by, pheromones can be gradually
It dissipates；
The actual information element concentration in this section of two o'clock path is pheromone concentration initial value DiIn the release and dissipation collective effect of pheromones
Result afterwards；Therefore, unmanned plane is more on a certain two o'clock path, and practical pheromone concentration is higher；Path planning system is according to reality
Border pheromone concentration situation controls each unmanned plane and preferentially flies along the highly concentrated path of actual information element；
Step 2.5, the highest fullpath of pheromone concentration in cluster l, i.e. preferred path p are obtained；
According to this step the method, the preferred path of each cluster is obtained；
The content of step 2 all complete in path planning system by operation, without practical flight；
Step 3, the control system of earth station to be to obtain maximum detection target total value and the smallest total flying distance as target,
Carry out clustering；
Step 3.1, the detection target total value minf of preferred path p is calculated1:
Detect target total value minf1Constituent element include each target to be detected self-value and detection success rate, table
It states are as follows:
In formula, PRlpDetection success when taking action to cluster the unmanned plane of l according to preferred path p, to j-th of target on the path
Rate；
J-th of target on the p of path is denoted as jp, VjFor jpSelf-value, VjpFor jpValue on the p of path；VmaxFor Vjp
Maximum value；
xlpFor the unmanned plane quantity for passing through preferred path p in cluster l；
NaFor the summation of unmanned plane quantity in each cluster；
Step 3.2, total flying distance minf of preferred path p is calculated2:
In formula, DlpTo cluster the flying distance summation when unmanned plane of l is taken action according to path p；
RlFor UAV combat radius；
For the length of longest path,
λ1, λ2For weight, λ1+λ2=1, λ1,λ2≥0；
1/RlNaFor planning factors；
According to the above method, the detection target total value and the smallest total flying distance of the preferred path of each cluster are obtained；
Step 3.3, target exercise value f is calculated3；
The generating process of optimal path is by detection target total value minf1With total flying distance minf2Influence；It calculates each
The target exercise value f of preferred path3:
f3=k1*minf1+k2minf2
K1, k2 are weight, k1+k2=1, k1,k2≥0；
Step 4, optimal path is generated；
To realize maximum target value, G value calculation is carried out, that is, repeats G step 2- step 3；Valence is carried out every time
When value calculates, different proportionality coefficient k is given, different k bring G group target exercise value f is calculated3；
The control system of earth station takes target exercise value f3The smallest preferred path is optimal path, in the result being calculated
The flight control system for reaching unmanned plane, the practical flight path as unmanned plane；
Step 5, ground station control system carries out realistically displayed to optimal path, and controls unmanned plane and carried out in fact according to optimal path
Border flight.
5. the controlling party of the path planning verification and multi-platform control system of multitask unmanned plane according to claim 4
Method, which is characterized in that in the step 5, realistically displayed is carried out to optimal path, method particularly includes:
The analogue system based on jMAVSim simulator and unmanned plane mathematical model is established in the control system of earth station, to nothing
Man-machine carry out hardware in loop semi-physical simulation；
The winged control plate and airborne computer of unmanned plane are removed from aircraft, and connect with earth station by data line；Earth station
Analogue system provide different flight environment of vehicle parameters, pass through and fly control plate and the airborne computer signal and given finger that receive
It enables, simulates the flight condition of unmanned plane；
Fly control plate and airborne computer and the winged control of aircraft is instructed according to practical flight coordinates measurement, i.e., to each revolution speed of propeller
Control instruction；
The analogue system of earth station reads the winged control instruction of flight control system in real time, and according to unmanned plane mathematical model, resolves nobody
Flight path of the machine in the case where flying control instruction, and the information such as the unmanned plane position, speed, the direction that calculate are returned in real time and fly control plate
And airborne computer, it is generated by it and flies control instruction in next step, thus verify the reliability in practical flight path, until artificial terminate
Simulation process.
6. the controlling party of the path planning verification and multi-platform control system of multitask unmanned plane according to claim 4
Method, which is characterized in that the step 5 controls unmanned plane according to multi-platform control system during unmanned plane practical flight
Movement, specifically:
Each frame unmanned plane by self-position, velocity information and camera shooting information real-time delivery to communication link bus, while from logical
Believe the relevant information of the other unmanned planes of junctor highway Real-Time Sharing, while earth station carries out each frame unmanned plane according to the information
Path planning constantly updates the flight path instruction of each frame unmanned plane in unmanned plane cluster flight course, realizes that unmanned plane is compiled
The optimization of team's flight effect.
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