CN112423304A - Multi-unmanned aerial vehicle scheduling communication frequency band allocation method and system - Google Patents
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Abstract
Description
Technical Field
The application relates to the technical field of communication, in particular to a method and a system for allocating communication frequency bands for dispatching of multiple unmanned aerial vehicles.
Background
With the increasing maturity of unmanned aerial vehicle technology, its usage is also more and more extensive, especially plays very important effect in the rescue and relief work process unmanned aerial vehicle, has irreplaceability, and this makes unmanned aerial vehicle obtain the application in various rescue and relief work occasions, becomes the indispensable important equipment in the work of information-based rescue.
In the prior art, the following defects exist in the scheduling process of the unmanned aerial vehicle: the efficiency and reliability of task execution of the unmanned aerial vehicle are low; the communication between the unmanned aerial vehicle and the scheduling server is easy to conflict or block, and the communication efficiency is low.
Disclosure of Invention
The application aims to provide a method and a system for allocating scheduling communication frequency bands of multiple unmanned aerial vehicles, and the method improves the task execution efficiency and reliability of the unmanned aerial vehicles and improves the communication efficiency between the unmanned aerial vehicles and a scheduling server.
In order to achieve the above object, the present application provides a method for allocating communication frequency bands for scheduling multiple unmanned aerial vehicles, including the following steps: acquiring characteristic parameters of an unmanned aerial vehicle and characteristic parameters of a task to be executed; calculating a task execution index of the unmanned aerial vehicle according to the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed; distributing the task to be executed to the unmanned aerial vehicle with the highest task execution index; and dividing the communication frequency spectrum resource of the scheduling server into a plurality of frequency bands, and setting a communication frequency band for acquiring the task to be executed for the unmanned aerial vehicle after the unmanned aerial vehicle passes the identity verification.
As above, obtaining the characteristic parameter of the task to be executed includes obtaining the target position coordinate of the task to be executed and an angle between the target position and the positive horizontal direction of the scheduling center point.
As above, wherein, obtain unmanned aerial vehicle's characteristic parameter includes: and acquiring a preset space threshold limit range of the unmanned aerial vehicle and the flying start point coordinates of the unmanned aerial vehicle.
As above, wherein, the formula for calculating the task performance index of the drone is:
wherein T represents a task execution index of the unmanned aerial vehicle; schIndicating to be performedThe target position of the task is located in a position index of the space threshold limit range of the unmanned aerial vehicle; (x1, y1) represents target position coordinates corresponding to performing the task; (x2, y2) represents the takeoff coordinates of the drone; theta 1 represents an included angle between the unmanned aerial vehicle and the horizontal positive direction of the scheduling central point, andtheta 2 represents an included angle between a target position corresponding to the executed task and the horizontal positive direction of the scheduling central point; | represents an absolute value symbol; pxrIndicating that the drone is compliant with the performance requirement value for performing the task.
As above, wherein the target location of the task to be performed is located at the location index S of the spatial threshold range of the dronechThe calculation formula of (2) is as follows:
wherein, if the target position is located in the space threshold limit range of the unmanned aerial vehicle, the parameter ZneiHas a value of 1; if the target position is outside the space threshold limit range of the unmanned aerial vehicle, ZneiIs 0; dysDistance in the Y-axis direction from the uppermost boundary of the drone space threshold range, d, representing the target locationyxA distance in the Y-axis direction from a lowermost boundary of the drone space threshold limit range representing a target location; dxzA distance in the X-axis direction from a leftmost boundary of the drone space threshold range representing a target location; dxyA distance in the X-axis direction from a rightmost boundary of the drone space threshold range representing the target location.
As above, wherein the drone complies with the performance requirement value P for performing the taskxrThe calculation formula of (a) is as follows:
wherein J represents the number of performance items required to complete the execution task; j represents the j-th performance required for completing the execution task; w is ajIndicating j-th performance required for completing execution task as total required performanceA weight; XWjA value representing a jth capability of the drone; XR (X ray diffraction)jA value representing the j-th performance required to complete the execution of the task.
As above, the method for verifying the identity of the unmanned aerial vehicle comprises the following steps: in response to the instruction for acquiring the data packet, the scheduling server acquires a second tag of the unmanned aerial vehicle, verifies the second tag, judges whether the second tag is matched with a corresponding tag in a second authorization list stored in the scheduling server, and if the second tag is matched with the corresponding tag, the verification is passed; otherwise, the verification is not passed.
As above, the method for setting the communication frequency band for acquiring the task to be executed for the drone includes: responding to a communication request signal sent by the unmanned aerial vehicle, and acquiring a second label of the unmanned aerial vehicle; and detecting whether the preset optimal frequency band of the second tag is an idle frequency band, if so, authorizing the optimal frequency band corresponding to the second tag to the unmanned aerial vehicle for communication connection, and otherwise, selecting the second-best idle frequency band to authorize the unmanned aerial vehicle for communication connection.
As above, wherein, the method for presetting the optimal frequency band for the unmanned aerial vehicle is as follows: starting different frequency bands, and receiving the unmanned aerial vehicle signals after filtering; respectively calculating the total energy of the unmanned aerial vehicle signals in the sensing time in different frequency bands; the frequency band with the maximum total energy of the single unmanned aerial vehicle is used as an optimal frequency band, the second large frequency band is used as a second optimal frequency band, and the excellent degree of the frequency band is determined for the single unmanned aerial vehicle in sequence according to the same principle; and establishing a corresponding relation between the second label of the unmanned aerial vehicle and the corresponding optimal frequency band.
The application also provides a many unmanned aerial vehicle dispatch communication frequency channel distribution system, and this system includes:
the acquisition module is used for acquiring the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed;
the calculation module is used for calculating a task execution index of the unmanned aerial vehicle according to the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed;
the task allocation module is used for allocating the tasks to be executed to the unmanned aerial vehicle with the highest task execution index;
and the communication frequency band setting module is used for dividing the communication frequency spectrum resources of the scheduling server into a plurality of frequency bands, and setting the communication frequency band for acquiring the task to be executed for the unmanned aerial vehicle after the unmanned aerial vehicle passes the identity verification.
The beneficial effect that this application realized is as follows:
(1) this application is a plurality of unmanned aerial vehicle preset space domain limit scope to make unmanned aerial vehicle have the flight range of injecing, and carry out the task at the within range of injecing, do benefit to and manage unmanned aerial vehicle, according to the geographical position of carrying out the task and the space threshold limit scope that unmanned aerial vehicle set for, calculate unmanned aerial vehicle's task performance index, with the task allocation to the biggest unmanned aerial vehicle of task performance index, thereby the efficiency and the reliability of task execution have been improved.
(2) This application divides the communication frequency spectrum resource of dispatch server into a plurality of frequency channels, sets for the best frequency channel of communication for unmanned aerial vehicle for a plurality of unmanned aerial vehicle homoenergetic obtain the best matching frequency channel, and prevent the communication conflict or block between unmanned aerial vehicle and the dispatch server, improve communication efficiency.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a flowchart of a method for allocating communication frequency bands for scheduling multiple unmanned aerial vehicles according to an embodiment of the present application.
Fig. 2 is a flowchart of a method for setting a communication frequency band for acquiring a task to be executed for an unmanned aerial vehicle according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a multi-drone scheduling communication frequency band allocation system according to an embodiment of the present application.
Reference numerals: 10-an acquisition module; 20-a calculation module; 30-a task allocation module; 40-a communication frequency band setting module; 100-communication frequency band distribution system.
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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 application.
Example one
As shown in fig. 1, the present application provides a method for allocating communication frequency bands for scheduling multiple drones, including the following steps:
and step S1, acquiring the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed.
Wherein, step S1 includes the following steps:
and step S110, acquiring a preset space threshold limit range of the unmanned aerial vehicle and the flying start point coordinates of the unmanned aerial vehicle. Wherein, the space domain limits the space range that unmanned aerial vehicle can detect.
And step S120, acquiring the target position coordinates of the task to be executed.
And step S2, calculating the task execution index of the unmanned aerial vehicle according to the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed.
Specifically, the formula for calculating the task execution index of the unmanned aerial vehicle is as follows:
wherein T represents a task execution index of the unmanned aerial vehicle; schA position index indicating that a target position of a task to be executed is within a spatial threshold range of the unmanned aerial vehicle; (x1, y1) represents target position coordinates corresponding to performing the task; (x2, y2) represents the takeoff coordinates of the drone; theta 1 represents an included angle between the unmanned aerial vehicle and the horizontal positive direction of the scheduling central point, andtheta 2 represents water of a target position corresponding to the execution task and the scheduling central pointAn included angle between the normal directions; | represents an absolute value symbol; pxrIndicating that the drone is compliant with the performance requirement value for performing the task.
Wherein, the target position of the task to be executed is positioned at the position index S of the space threshold limit range of the unmanned aerial vehiclechThe calculation formula of (2) is as follows:
wherein, if the target position is located in the space threshold limit range of the unmanned aerial vehicle, the parameter ZneiHas a value of 1; if the target position is outside the space threshold limit range of the unmanned aerial vehicle, ZneiIs 0; dysDistance in the Y-axis direction from the uppermost boundary of the drone space threshold range, d, representing the target locationyxA distance in the Y-axis direction from a lowermost boundary of the drone space threshold limit range representing a target location; dxzA distance in the X-axis direction from a leftmost boundary of the drone space threshold range representing a target location; dxyA distance in the X-axis direction from a rightmost boundary of the drone space threshold range representing the target location.
Wherein, an X-axis and a Y-axis coordinate system are established at the target position.
Specifically, the unmanned aerial vehicle conforms to the performance requirement value P of the executive taskxrThe calculation formula of (a) is as follows:
wherein J represents the number of performance items required to complete the execution task; j represents the j-th performance required for completing the execution task; w is ajRepresenting the weight of the j item performance required for completing the execution task to the total required performance; XWjA value representing a jth capability of the drone; XR (X ray diffraction)jA value representing the j-th performance required to complete the execution of the task.
And step S3, distributing the task to be executed to the unmanned aerial vehicle with the highest task execution index.
Specifically, the unmanned aerial vehicle corresponding to the maximum execution index is selected, and the task to be executed is distributed to the unmanned aerial vehicle.
And packaging the tasks to be executed into a data packet, and distributing the data packet to the unmanned aerial vehicle based on the negotiated distribution protocol.
The unmanned aerial vehicles are in communication connection in a star topology mode and are uniformly commanded and controlled by the scheduling server.
And when receiving the data packet containing the execution task, the unmanned aerial vehicle needs to verify the data packet sent by the scheduling server. The unmanned aerial vehicle obtains a first label in a data packet sent by the scheduling server, verifies the first label, judges whether the first label is matched with a corresponding label in a first authorization list stored in the unmanned aerial vehicle, receives the data packet sent by the scheduling server if the first label is matched with the corresponding label in the first authorization list, and does not receive the data packet sent by the scheduling server if the first label is not matched with the corresponding label in the first authorization list.
And step S4, dividing the communication frequency spectrum resource of the scheduling server into a plurality of frequency bands, and setting a communication frequency band for acquiring the task to be executed for the unmanned aerial vehicle after the unmanned aerial vehicle passes the identity verification.
The scheduling server sets a first label for the data packet, the unmanned aerial vehicle is set with a second label, a second label list of the unmanned aerial vehicle scheduled by the scheduling server is arranged in the scheduling server, and a first label list of the scheduling server of the task received by the unmanned aerial vehicle is set in the unmanned aerial vehicle. Wherein, the data packet is encrypted by the first key.
The method for verifying the identity of the unmanned aerial vehicle comprises the following steps: in response to the instruction for acquiring the data packet, the scheduling server acquires a second tag of the unmanned aerial vehicle, verifies the second tag, judges whether the second tag is matched with a corresponding tag in a second authorization list stored in the scheduling server, if the second tag is matched with the corresponding tag, the verification is passed, the data packet is sent to the unmanned aerial vehicle, and a decryption key corresponding to the first key is sent to the unmanned aerial vehicle; otherwise, the verification is not passed, and the data packet is forbidden to be sent to the unmanned aerial vehicle.
The communication spectrum resources of the scheduling server are distributed to the unmanned aerial vehicles in a collision-free mode, and the communication efficiency of the unmanned aerial vehicles and the scheduling server and the utilization rate of the communication spectrum resources are improved.
As shown in fig. 2, the method for setting the communication band for acquiring the task to be executed for the drone in step S4 includes the following sub-steps:
and step S410, responding to the request communication signal sent by the unmanned aerial vehicle, and acquiring a second label of the unmanned aerial vehicle.
Step S420, detecting whether the preset optimal frequency band of the second tag is an idle frequency band, if so, authorizing the optimal frequency band corresponding to the second tag to the unmanned aerial vehicle for communication connection, otherwise, selecting a second best idle frequency band to authorize the unmanned aerial vehicle for communication connection.
The method for presetting the optimal frequency band for the unmanned aerial vehicle comprises the following steps:
and step S421, starting different frequency bands, and receiving the unmanned aerial vehicle signals after filtering processing.
Step S422, respectively calculating the total energy of the unmanned aerial vehicle signals in different frequency bands within preset sensing time.
The total energy is calculated by the formula:
wherein Y represents the total energy; t represents a duration; w represents a bandwidth; w is aiRepresenting the energy of the received drone signal.
Step S423, determining the frequency band with the largest total energy of the single unmanned aerial vehicle as the optimal frequency band, and determining the good degree of the frequency band for the single unmanned aerial vehicle in sequence according to the same principle, with the frequency band with the largest total energy of the single unmanned aerial vehicle as the second optimal frequency band and the second largest frequency band as the second optimal frequency band.
Step S424, a corresponding relationship between the second tag of the drone and the corresponding optimal frequency band is established. Namely, the optimal frequency band can be found according to the second label through the corresponding relation.
Step S430, after the unmanned aerial vehicle completes data interaction with the scheduling server through the frequency band, the frequency band is set as an idle frequency band.
The method further comprises the step of calculating the utilization rate of the frequency band, wherein a formula for calculating the utilization rate of the qth frequency band is as follows:
Pqwangkouindicating frequency band utilization; u represents a data packet total receiving and sending queue; u represents the u-th transceiving queue; t1 represents time; t2 represents time; t2 is greater than t 1;
the data packet quantity in the u-th transceiving queue which is received through the q-th frequency band at the time t is represented;
the number of data packets in the u-th transceiving queue sent out through the q-th frequency band at the time t is represented; Δ Γ represents a time difference between time t1 andtime t 2.
Example two
As shown in fig. 3, the present application provides asystem 100 for allocating communication frequency bands for scheduling multiple drones, which includes:
the acquiringmodule 10 is configured to acquire characteristic parameters of the unmanned aerial vehicle and characteristic parameters of a task to be executed.
And the calculatingmodule 20 is configured to calculate a task execution index of the unmanned aerial vehicle according to the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed.
And thetask allocation module 30 is configured to allocate the task to be executed to the unmanned aerial vehicle with the highest task execution index.
And the communication frequencyband setting module 40 is used for dividing the communication frequency spectrum resources of the scheduling server into a plurality of frequency bands, and setting a communication frequency band for acquiring the task to be executed for the unmanned aerial vehicle after the unmanned aerial vehicle passes the identity verification.
The beneficial effect that this application realized is as follows:
(1) this application is a plurality of unmanned aerial vehicle preset space domain limit scope to make unmanned aerial vehicle have the flight range of injecing, and carry out the task at the within range of injecing, do benefit to and manage unmanned aerial vehicle, according to the geographical position of carrying out the task and the space threshold limit scope that unmanned aerial vehicle set for, calculate unmanned aerial vehicle's task performance index, with the task allocation to the biggest unmanned aerial vehicle of task performance index, thereby the efficiency and the reliability of task execution have been improved.
(2) This application divides the communication frequency spectrum resource of dispatch server into a plurality of frequency channels, sets for the best frequency channel of communication for unmanned aerial vehicle for a plurality of unmanned aerial vehicle homoenergetic obtain the best matching frequency channel, and prevent the communication conflict or block between unmanned aerial vehicle and the dispatch server, improve communication efficiency.
The above description is only an embodiment of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method for allocating communication frequency bands for scheduling multiple unmanned aerial vehicles is characterized by comprising the following steps:
acquiring characteristic parameters of an unmanned aerial vehicle and characteristic parameters of a task to be executed;
calculating a task execution index of the unmanned aerial vehicle according to the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed;
distributing the task to be executed to the unmanned aerial vehicle with the highest task execution index;
and dividing the communication frequency spectrum resource of the scheduling server into a plurality of frequency bands, and setting a communication frequency band for acquiring the task to be executed for the unmanned aerial vehicle after the unmanned aerial vehicle passes the identity verification.
2. The method of claim 1, wherein the obtaining of the characteristic parameters of the task to be executed comprises obtaining coordinates of a target location of the task to be executed and an angle between the target location and a horizontal positive direction of a scheduling center point.
3. The method of claim 1, wherein the obtaining characteristic parameters of the drones comprises: and acquiring a preset space threshold limit range of the unmanned aerial vehicle and the flying start point coordinates of the unmanned aerial vehicle.
4. The method for allocating the communication frequency bands for scheduling of multiple unmanned aerial vehicles according to claim 1, wherein the formula for calculating the task performance index of the unmanned aerial vehicle is as follows:
wherein T represents a task execution index of the unmanned aerial vehicle; schA position index indicating that a target position of a task to be executed is within a spatial threshold range of the unmanned aerial vehicle; (x1, y1) represents target position coordinates corresponding to performing the task; (x2, y2) represents the takeoff coordinates of the drone; theta 1 represents an included angle between the unmanned aerial vehicle and the horizontal positive direction of the scheduling central point, and theta 2 represents an included angle between a target position corresponding to the executed task and the horizontal positive direction of the scheduling central point; | represents an absolute value symbol; pxrIndicating that the drone is compliant with the performance requirement value for performing the task.
5. The method of claim 4, wherein the target location of the task to be executed is at a location index S of the spatial threshold range of the UAVschThe calculation formula of (2) is as follows:
wherein, if the target position is located in the space threshold limit range of the unmanned aerial vehicle, the parameter ZneiHas a value of 1; if the target position is outside the space threshold limit range of the unmanned aerial vehicle, ZneiIs 0; dysDistance in the Y-axis direction from the uppermost boundary of the drone space threshold range representing the target locationFrom, dyxA distance in the Y-axis direction from a lowermost boundary of the drone space threshold limit range representing a target location; dxzA distance in the X-axis direction from a leftmost boundary of the drone space threshold range representing a target location; dxyA distance in the X-axis direction from a rightmost boundary of the drone space threshold range representing the target location.
6. The method as claimed in claim 4, wherein the UAV meets the performance requirement P of the taskxrThe calculation formula of (a) is as follows:
wherein J represents the number of performance items required to complete the execution task; j represents the j-th performance required for completing the execution task; w is ajRepresenting the weight of the j item performance required for completing the execution task to the total required performance; XWjA value representing a jth capability of the drone; XR (X ray diffraction)jA value representing the j-th performance required to complete the execution of the task.
7. The method for allocating the communication frequency bands for scheduling of multiple unmanned aerial vehicles according to claim 4, wherein the method for verifying the identity of the unmanned aerial vehicle comprises the following steps: in response to the instruction for acquiring the data packet, the scheduling server acquires a second tag of the unmanned aerial vehicle, verifies the second tag, judges whether the second tag is matched with a corresponding tag in a second authorization list stored in the scheduling server, and if the second tag is matched with the corresponding tag, the verification is passed; otherwise, the verification is not passed.
8. The method for allocating the communication frequency bands for scheduling of multiple unmanned aerial vehicles according to claim 1, wherein the method for setting the communication frequency band for acquiring the task to be executed for the unmanned aerial vehicle comprises:
responding to a communication request signal sent by the unmanned aerial vehicle, and acquiring a second label of the unmanned aerial vehicle;
and detecting whether the preset optimal frequency band of the second tag is an idle frequency band, if so, authorizing the optimal frequency band corresponding to the second tag to the unmanned aerial vehicle for communication connection, and otherwise, selecting the second-best idle frequency band to authorize the unmanned aerial vehicle for communication connection.
9. The method of claim 8, wherein the method for pre-setting the optimal frequency band for the drones is as follows:
starting different frequency bands, and receiving the unmanned aerial vehicle signals after filtering;
respectively calculating the total energy of the unmanned aerial vehicle signals in the sensing time in different frequency bands;
the frequency band with the maximum total energy of the single unmanned aerial vehicle is used as an optimal frequency band, the second large frequency band is used as a second optimal frequency band, and the excellent degree of the frequency band is determined for the single unmanned aerial vehicle in sequence according to the same principle;
and establishing a corresponding relation between the second label of the unmanned aerial vehicle and the corresponding optimal frequency band.
10. The utility model provides a many unmanned aerial vehicle dispatch communication frequency channel distribution system which characterized in that, this system includes:
the acquisition module is used for acquiring the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed;
the calculation module is used for calculating a task execution index of the unmanned aerial vehicle according to the characteristic parameters of the unmanned aerial vehicle and the characteristic parameters of the task to be executed;
the task allocation module is used for allocating the tasks to be executed to the unmanned aerial vehicle with the highest task execution index;
and the communication frequency band setting module is used for dividing the communication frequency spectrum resources of the scheduling server into a plurality of frequency bands, and setting the communication frequency band for acquiring the task to be executed for the unmanned aerial vehicle after the unmanned aerial vehicle passes the identity verification.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104867357A (en)* | 2015-01-21 | 2015-08-26 | 中南大学 | Multi-unmanned aerial vehicle scheduling and task planning method for earthquake emergency response |
| CN108171315A (en)* | 2017-12-27 | 2018-06-15 | 南京邮电大学 | Multiple no-manned plane method for allocating tasks based on SMC particle cluster algorithms |
| US20180375568A1 (en)* | 2017-06-22 | 2018-12-27 | At&T Intellectual Property I, L.P. | Maintaining network connectivity of aerial devices during unmanned flight |
| CN110300417A (en)* | 2019-05-09 | 2019-10-01 | 清华大学 | The energy efficiency optimization method and device of Communication Network for UAVS |
| CN110766254A (en)* | 2018-07-27 | 2020-02-07 | 深圳市白麓嵩天科技有限责任公司 | Multi-unmanned aerial vehicle cooperative task allocation method based on improved genetic algorithm |
| CN111127956A (en)* | 2019-12-31 | 2020-05-08 | 长江空间信息技术工程有限公司(武汉) | A flood disaster UAV emergency response scheduling method |
| CN111884740A (en)* | 2020-06-08 | 2020-11-03 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle channel optimal allocation method and system based on frequency spectrum cognition |
- 2020
- 2020-11-06CNCN202011228026.3Apatent/CN112423304B/enactiveActive
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104867357A (en)* | 2015-01-21 | 2015-08-26 | 中南大学 | Multi-unmanned aerial vehicle scheduling and task planning method for earthquake emergency response |
| US20180375568A1 (en)* | 2017-06-22 | 2018-12-27 | At&T Intellectual Property I, L.P. | Maintaining network connectivity of aerial devices during unmanned flight |
| CN108171315A (en)* | 2017-12-27 | 2018-06-15 | 南京邮电大学 | Multiple no-manned plane method for allocating tasks based on SMC particle cluster algorithms |
| CN110766254A (en)* | 2018-07-27 | 2020-02-07 | 深圳市白麓嵩天科技有限责任公司 | Multi-unmanned aerial vehicle cooperative task allocation method based on improved genetic algorithm |
| CN110300417A (en)* | 2019-05-09 | 2019-10-01 | 清华大学 | The energy efficiency optimization method and device of Communication Network for UAVS |
| CN111127956A (en)* | 2019-12-31 | 2020-05-08 | 长江空间信息技术工程有限公司(武汉) | A flood disaster UAV emergency response scheduling method |
| CN111884740A (en)* | 2020-06-08 | 2020-11-03 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle channel optimal allocation method and system based on frequency spectrum cognition |
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