CN116627179A - Unmanned aerial vehicle formation control method and device - Google Patents
Unmanned aerial vehicle formation control method and device Download PDFInfo
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Abstract
The application discloses an unmanned aerial vehicle formation control method and device, wherein the method comprises the following steps: acquiring task positions and cluster information of unmanned aerial vehicle clusters; the cluster information comprises the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster and initial information of each unmanned aerial vehicle; determining initial weights of all unmanned aerial vehicles according to the task positions and the cluster information to generate initial weight sequences; determining a long machine and a wing machine in the unmanned aerial vehicle cluster according to the initial weight sequence; if the direction vector of any unmanned aerial vehicle in the unmanned aerial vehicle cluster is changed, generating a process weight sequence according to the current weight updating process weight of each unmanned aerial vehicle; and re-determining the long plane and the wing plane in the unmanned plane cluster according to the process weight sequence. By implementing the method, unmanned aerial vehicles beneficial to completing tasks can be selected as long machines, the situation that unmanned aerial vehicle cluster tasks fail due to the fact that long machines are not available can be avoided, and superposition errors possibly occurring among unmanned aerial vehicles can be eliminated.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle cluster control, in particular to an unmanned aerial vehicle formation control method and device.
Background
The single unmanned aerial vehicle has limitations in the aspects of perception range, task load, computing power and the like, and multi-machine formation cooperative execution is required for some complex tasks. In the process of executing the cooperative flight tasks, the unmanned aerial vehicle cluster keeps reasonable formation, so that the multi-unmanned aerial vehicle system can more effectively complete the tasks, and the cooperative efficiency of formation is improved. The most widely used formation control algorithm is a Leader-Follower method at present, which is relatively simple and easy to expand, and is widely applied to different formation control problems. The main content of the method is that in the unmanned aerial vehicle formation flight process, one unmanned aerial vehicle is designated as a long plane, and the rest unmanned aerial vehicles are used as the plane. The long machine is responsible for track tracking, the task of the plane is to keep the relative positions of the plane and the long machine and the plane adjacent to the long machine unchanged, and the plane is used as a follower to finish flying along with the long machine.
The Leader-Follower method adopts the design idea of double loops, takes the steady-state control of a single machine as an inner loop and the track control of formation as an outer loop, designs a PI controller, and realizes the following flight of the plane. The obstacle avoidance strategy and the control method of the long aircraft are provided with different obstacle scenes, and the obstacle avoidance and recombination strategies of the assistant aircraft are respectively provided by using the formation dismissal and recombination modes. But once the long machine is destroyed, the entire formation task fails. In addition, if the formation adopts a chain structure, errors can be accumulated and overlapped among layers, and when the formation is greatly disturbed, the formation of the trickplay can be failed.
Disclosure of Invention
The embodiment of the application solves the problem that the whole formation task fails after the Leader-Follower formation method loses the long machine in the prior art, and the chain structure formation method can accumulate superposition errors among layers by providing the unmanned aerial vehicle formation control method. The unmanned aerial vehicle formation control method can solve the problems.
In a first aspect, an embodiment of the present application provides a method for controlling formation of an unmanned aerial vehicle, including: acquiring task positions and cluster information of unmanned aerial vehicle clusters; the cluster information comprises the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster and initial information of each unmanned aerial vehicle; determining initial weights of all unmanned aerial vehicles according to the task positions and the cluster information to generate initial weight sequences; determining a long machine and a plane in the unmanned aerial vehicle cluster according to the initial weight sequence; if the direction vector of any unmanned aerial vehicle in the unmanned aerial vehicle cluster is changed, generating a process weight sequence according to the current weight updating process weight of each unmanned aerial vehicle; and re-determining the long aircraft and the plane in the unmanned plane cluster according to the process weight sequence.
With reference to the first aspect, in a first possible implementation manner, the initial information includes an initial position, an initial speed and an initial attitude angle of the unmanned aerial vehicle.
With reference to the first aspect, in a second possible implementation manner, any one of the unmanned aerial vehicles in the unmanned aerial vehicle cluster satisfies the following relation:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the application,for the position coordinates of the unmanned aerial vehicle, rand represents a random number between (0, 1),、respectively the maximum value and the minimum value of the x coordinate in the unmanned aerial vehicle cluster,、respectively the maximum value and the minimum value of the y coordinates in the unmanned aerial vehicle cluster,、and the maximum value and the minimum value of the z coordinate in the unmanned aerial vehicle cluster are respectively.
With reference to the first possible implementation manner of the first aspect, in a third possible implementation manner, the determining according to the task location and the cluster informationDetermining initial weights of all unmanned aerial vehicles to generate initial weight sequences, wherein the initial weight sequences comprise: calculating the initial weight of each unmanned aerial vehicle according to the task position and the initial information, wherein a calculation formula is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the application,representing said initial weight of the drone i,、、and (3) withThe prize coefficients for each of the factors respectively,for the distance of the drone i from the mission location,for said initial attitude angle of the drone i,for said initial speed of the drone i,an included angle of the average initial speed direction of the unmanned aerial vehicle i and other unmanned aerial vehicles is formed; and sequencing the initial weights of the unmanned aerial vehicles to obtain the initial weight sequence of the unmanned aerial vehicle.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner, the update formula of the process weight sequence generated according to the current weight update process weight of each unmanned aerial vehicle is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the application,for the process weight of the drone i,for said current weight of drone i, rand is a random number between (0, 1),for the maximum value of the current weights in the drone cluster,the minimum value of the current weight in the unmanned aerial vehicle cluster is set; and sequencing the process weights of the unmanned aerial vehicles to obtain the process weight sequence of the unmanned aerial vehicle.
With reference to the first aspect, in a fifth possible implementation manner, the determining a plane and a plane in the unmanned aerial vehicle cluster further includes: determining the expected position of each wing aircraft according to the positions of the long aircraft, the number of unmanned aerial vehicles and the set formation of the unmanned aerial vehicle cluster; determining the expected position corresponding to the initial information of the plane according to the initial information of the plane; controlling the plane to fly to the corresponding expected position and following the plane.
With reference to the first aspect, in a sixth possible implementation manner, a reason why the direction vector of the unmanned aerial vehicle changes includes: unmanned aerial vehicle keeps away barrier and/or unmanned aerial vehicle trouble.
In a second aspect, an embodiment of the present application provides an unmanned aerial vehicle formation control device, including: the acquisition module is used for acquiring the task position and cluster information of the unmanned aerial vehicle clusters; the cluster information comprises the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster and initial information of each unmanned aerial vehicle; the initial weight module is used for determining the initial weight of each unmanned aerial vehicle according to the task position and the cluster information to generate an initial weight sequence; the determining module is used for determining the plane and the plane in the unmanned aerial vehicle cluster according to the initial weight sequence; the updating module is used for generating a process weight sequence according to the current weight updating process weight of each unmanned aerial vehicle if the direction vector of any unmanned aerial vehicle in the unmanned aerial vehicle cluster is changed; and the process weight module is used for redetermining the long plane and the bureau in the unmanned aerial vehicle cluster according to the process weight sequence.
With reference to the second aspect, in a first possible implementation manner, the initial information includes an initial position, an initial speed and an initial attitude angle of the unmanned aerial vehicle.
With reference to the second aspect, in a second possible implementation manner, any one of the unmanned aerial vehicles in the unmanned aerial vehicle cluster satisfies the following relation:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the application,for the position coordinates of the unmanned aerial vehicle, rand represents a random number between (0, 1),、respectively the maximum value and the minimum value of the x coordinate in the unmanned aerial vehicle cluster,、respectively the maximum value and the minimum value of the y coordinates in the unmanned aerial vehicle cluster,、and the maximum value and the minimum value of the z coordinate in the unmanned aerial vehicle cluster are respectively.
With reference to the first possible implementation manner of the second aspect, in a third possible implementation manner, the determining, according to the task position and the cluster information, an initial weight of each unmanned aerial vehicle to generate an initial weight sequence includes: according to the task position and the positionThe initial information calculates the initial weight of each unmanned aerial vehicle, and the calculation formula is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the application,representing said initial weight of the drone i,、、and (3) withThe prize coefficients for each of the factors respectively,for the distance of the drone i from the mission location,for said initial attitude angle of the drone i,for said initial speed of the drone i,an included angle of the average initial speed direction of the unmanned aerial vehicle i and other unmanned aerial vehicles is formed; and sequencing the initial weights of the unmanned aerial vehicles to obtain the initial weight sequence of the unmanned aerial vehicle.
With reference to the third possible implementation manner of the second aspect, in a fourth possible implementation manner, the update formula of the process weight sequence generated according to the current weight update process weight of each unmanned aerial vehicle is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the application,for the process weight of the drone i,for said current weight of drone i, rand is a random number between (0, 1),for the maximum value of the current weights in the drone cluster,the minimum value of the current weight in the unmanned aerial vehicle cluster is set; and sequencing the process weights of the unmanned aerial vehicles to obtain the process weight sequence of the unmanned aerial vehicle.
With reference to the second aspect, in a fifth possible implementation manner, the determining a long plane and a assistant plane in the unmanned aerial vehicle cluster further includes: determining the expected position of each wing aircraft according to the positions of the long aircraft, the number of unmanned aerial vehicles and the set formation of the unmanned aerial vehicle cluster; determining the expected position corresponding to the initial information of the plane according to the initial information of the plane; controlling the plane to fly to the corresponding expected position and following the plane.
With reference to the second aspect, in a sixth possible implementation manner, a reason why the direction vector of the unmanned aerial vehicle changes includes: unmanned aerial vehicle keeps away barrier and/or unmanned aerial vehicle trouble.
In a third aspect, an embodiment of the present application provides an apparatus, including: a processor; a memory for storing processor-executable instructions; the processor, when executing the executable instructions, implements a method as described in the first aspect or any one of the possible implementations of the first aspect.
In a fourth aspect, embodiments of the present application provide a non-transitory computer readable storage medium comprising instructions for storing a computer program or instructions which, when executed, cause a method as described in the first aspect or any one of the possible implementations of the first aspect to be implemented.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
according to the embodiment of the application, the long machine is selected according to the task position and the unmanned aerial vehicle cluster information, the weight in the updating process is updated, and the long machine is determined according to the weight sequence after the direction vector of the unmanned aerial vehicle is changed, so that the problems that the whole formation task fails after the long machine is lost by a Leader-impeller formation method in the prior art and the stacking errors are accumulated among layers by a chain structure formation method are effectively solved. And then, the unmanned aerial vehicle formation control method is realized, unmanned aerial vehicles which are favorable for completing tasks can be selected from unmanned aerial vehicle clusters to serve as long machines, the long machines are updated after the direction vector of any unmanned aerial vehicle is changed, the situation that the unmanned aerial vehicle cluster tasks fail due to the fact that the long machines are not available is avoided, and the possible superposition errors among the unmanned aerial vehicles can be eliminated.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the embodiments of the present application or the drawings used in the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an unmanned aerial vehicle formation control method provided by an embodiment of the present application;
fig. 2 is a flowchart after determining a long machine and a bureau in an unmanned plane cluster according to an embodiment of the present application;
fig. 3 is a structural diagram of an unmanned aerial vehicle formation control device provided by an embodiment of the present application;
fig. 4 is a diagram of an example of formation of an unmanned aerial vehicle cluster.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Some of the techniques involved in the embodiments of the present application are described below to aid understanding, and they should be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, for the sake of clarity and conciseness, descriptions of well-known functions and constructions are omitted in the following description.
And (3) a long machine: the long machine is a core director and a decision maker in the unmanned aerial vehicle cluster, and is responsible for track tracking, coordination and control of actions of the whole unmanned aerial vehicle cluster when the long machine executes tasks. The leader communicates instructions, data and information by communicating with other bureaus.
The wing plane: the bureau is an assistant and executor of the long machine, and its task is to maintain the relative positions with the long machine and the adjacent machines. The long machine and other bureau machines cooperate with each other to complete complex tasks, thereby improving the efficiency and safety of the whole cluster.
Fig. 1 is a flowchart of an unmanned aerial vehicle formation control method provided by an embodiment of the present application, including steps 101 to 105. In this embodiment, fig. 1 is only one execution sequence shown in the embodiment of the present application, and does not represent the only execution sequence of the unmanned aerial vehicle formation control method, and in the case that the final result can be achieved, the steps shown in fig. 1 may be executed in parallel or upside down.
Step 101: and acquiring the task position and cluster information of the unmanned aerial vehicle clusters. The cluster information comprises the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster and initial information of each unmanned aerial vehicle. In the embodiment of the application, the initial information of the unmanned aerial vehicle comprises an initial position, an initial speed and an initial attitude angle of the unmanned aerial vehicle. In addition, any unmanned aerial vehicle in the unmanned aerial vehicle cluster satisfies the following relation:
。
in the method, in the process of the application,for the position coordinates of the unmanned aerial vehicle, rand represents a random number between (0, 1),、respectively the maximum value and the minimum value of the x coordinate in the unmanned aerial vehicle cluster,、the maximum value and the minimum value of the y coordinates in the unmanned aerial vehicle cluster are respectively,、the maximum value and the minimum value of the z coordinate in the unmanned aerial vehicle cluster are respectively.
Step 102: and determining the initial weight of each unmanned aerial vehicle according to the task position and the cluster information to generate an initial weight sequence. In the embodiment of the application, the initial weight of each unmanned aerial vehicle is calculated according to the task position and the initial information, and the calculation formula is as follows:
。
in the method, in the process of the application,representing the initial weight of the drone i,、、and (3) withThe prize coefficients for each of the factors respectively,for the distance of the unmanned aerial vehicle i from the mission location,for the initial attitude angle of the drone i,for the initial speed of the drone i,is the included angle between the average initial speed direction of the unmanned aerial vehicle i and other unmanned aerial vehicles. Wherein the reward coefficient is determined by the ordering of the factors of each drone in the drone cluster. For example, the closer the drone is to the mission location, the greater its corresponding reward coefficient. Conversely, the farther the unmanned aerial vehicle is from the mission location, the smaller its corresponding reward coefficient. And sequencing the initial weights of the unmanned aerial vehicles to obtain an initial weight sequence of the unmanned aerial vehicle. In the embodiment of the application, the order of the initial weights is from big to small. The order from small to large may also be used by those skilled in the art without limiting the scope of the application.
Step 103: and determining the long aircraft and the wing aircraft in the unmanned aerial vehicle cluster according to the initial weight sequence. In the embodiment of the application, according to the sequence of each initial weight in the initial weight sequence, the unmanned aerial vehicle corresponding to the initial weight which is the largest initial weight and is arranged at the forefront of the initial weight sequence is used as the long machine of the whole unmanned aerial vehicle cluster, and the rest unmanned aerial vehicles are used as the plane. After the determination of the chang and bureau, further adjustments may be made to the bureau, as shown in fig. 2, including steps 201 to 203, as follows.
Step 201: and determining the expected positions of the various unmanned aerial vehicles according to the positions of the long aerial vehicles, the number of the unmanned aerial vehicles and the set formation of the unmanned aerial vehicle cluster. In the embodiment of the application, before the initial long aircraft is determined, each unmanned aircraft independently flies and can communicate with a ground control system respectively. After the long plane is determined, the number of the bureau planes is determined according to the number of the unmanned planes in the unmanned plane cluster. And calculating the expected positions of the various wings in the set formation according to the current position of the long machine and the set formation used for completing the task. For the calculation of the expected position, those skilled in the art may calculate the expected position in combination with external environments, such as wind speed, wind direction, air pressure, etc., to facilitate reducing the flight resistance and keeping the unmanned aerial vehicle cluster stable, which is not used as a limitation of the protection scope of the present application. As shown in fig. 4, in the embodiment of the present application, the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster is exemplarily set to eight, and several set formations corresponding to eight unmanned aerial vehicles are exemplarily shown.
Step 202: and determining the expected position corresponding to the initial information of the plane. In one embodiment of the present application, according to step 201, the desired positions are calculated, each desired position is taken as the target point, the initial weight of each of the bureaus and each desired position is calculated by adopting the calculation method for calculating the initial weight in step 102, and the bureaus corresponding to the value with the largest initial weight is taken as the bureaus at the desired position. Thereby calculating the corresponding expected position of each plane. In another embodiment of the application, the distance from each of the bureaus to each of the desired positions is calculated, and the desired position closest to the bureaus is taken as the desired position corresponding to the calculated distance. The above shows two embodiments with different calculation amounts and final effects, and those skilled in the art can choose according to the actual situation. The present application is not limited to the scope of protection, and those skilled in the art may also use the optimal path algorithm, the path finding algorithm, and other methods to determine the expected positions corresponding to each machine.
Step 203: controlling the plane to fly to the corresponding expected position and following the plane. Specifically, after each of the bureaus determines its corresponding desired position, it autonomously flies to the corresponding desired position or flies to the corresponding desired position in response to control of the long machine. After each bureau flies to the corresponding expected position, the unmanned aerial vehicle clusters form a set formation, and the long machine can take the cluster task.
Step 104: if the direction vector of any unmanned aerial vehicle in the unmanned aerial vehicle cluster is changed, a process weight sequence is generated according to the current weight updating process weight of each unmanned aerial vehicle. In the embodiment of the application, when the unmanned aerial vehicle cluster executes the cluster task, the heading of the unmanned aerial vehicle is oriented or approximately oriented to the task position, and the direction vector of the unmanned aerial vehicle cannot be changed easily. If the direction vector is changed, special conditions are necessarily met, and the unmanned aerial vehicle is required to change the original direction vector. The changing of the direction vector of the unmanned aerial vehicle comprises: factors that can change the direction vector of the unmanned aerial vehicle, such as obstacle avoidance and/or unmanned aerial vehicle failure. Wherein the unmanned aerial vehicle failure comprises a unmanned aerial vehicle component failure and/or an unmanned aerial vehicle communication failure. In one embodiment of the present application, after a component failure occurs, if the component failure causes the corresponding unmanned aerial vehicle to be unable to continue to execute the flight task, the number of real wing machines and the set formation need to be re-determined after the new long machine is determined. It should be noted that in the embodiment of the application, each unmanned aerial vehicle is provided with the identification device, so that the change of the direction vector of the unmanned aerial vehicle can be identified, and meanwhile, the change of the pseudo direction vector of the unmanned aerial vehicle caused by the external environment can be identified. The false direction vector change comprises the condition that the unmanned aerial vehicle oscillates due to wind blowing, airflow or the like, so that the direction vector of the unmanned aerial vehicle is changed frequently for a plurality of times.
The update formula of the process weight sequence generated according to the current weight update process weight of each unmanned aerial vehicle is as follows:
。
in the method, in the process of the application,is the process weight of the unmanned aerial vehicle i,for the current weight of the unmanned aerial vehicle i, rand is a random number between (0, 1),is the current in the unmanned aerial vehicle clusterThe maximum value of the weight is calculated,is the minimum value of the current weight in the unmanned aerial vehicle cluster. The current weight is the weight of each unmanned aerial vehicle when the unmanned aerial vehicle direction vector changes in the unmanned aerial vehicle cluster. In the embodiment of the application, when the long aircraft is determined for the second time, the current weight is the initial weight of each unmanned aerial vehicle, and when the long aircraft is determined after the second time, the current weight is the process weight of each unmanned aerial vehicle at the moment. And updating the process weights through the calculation formula, and sequencing the process weights of the unmanned aerial vehicles to obtain a process weight sequence of the unmanned aerial vehicle.
Step 105: and re-determining the long plane and the wing plane in the unmanned plane cluster according to the process weight sequence. Specifically, according to the process weight sequence determined in step 104, the unmanned aerial vehicle corresponding to the process weight that is the greatest in the forefront is regarded as the new long machine, and the remaining unmanned aerial vehicles are regarded as the plane. After the long plane and the bureau plane are determined, the expected positions of the bureau planes are determined according to the positions of the long planes, the number of unmanned aerial vehicles and the set formation of the unmanned aerial vehicle cluster. And determining the expected position corresponding to the initial information of the plane. Controlling the plane to fly to the corresponding expected position and following the plane.
Although the application provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive labor. The order of steps recited in the present embodiment is only one way of performing the steps in a plurality of steps, and does not represent a unique order of execution. When implemented by an actual device or client product, the method of the present embodiment or the accompanying drawings may be performed sequentially or in parallel (e.g., in a parallel processor or a multithreaded environment).
As shown in fig. 3, the embodiment of the application further provides an unmanned aerial vehicle formation control device 300. The device comprises: the acquisition module 301, the initial weight module 302, the determination module 303, the update module 304, and the process weight module 305 are specifically as follows.
The acquiring module 301 is configured to acquire cluster information of a task location and a cluster of the unmanned aerial vehicle. The cluster information comprises the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster and initial information of each unmanned aerial vehicle. The obtaining module 301 is specifically configured to, in an embodiment of the present application, include an initial position, an initial speed, and an initial attitude angle of the unmanned aerial vehicle. In addition, any unmanned aerial vehicle in the unmanned aerial vehicle cluster satisfies the following relation:
。
in the method, in the process of the application,for the position coordinates of the unmanned aerial vehicle, rand represents a random number between (0, 1),、respectively the maximum value and the minimum value of the x coordinate in the unmanned aerial vehicle cluster,、the maximum value and the minimum value of the y coordinates in the unmanned aerial vehicle cluster are respectively,、the maximum value and the minimum value of the z coordinate in the unmanned aerial vehicle cluster are respectively.
The initial weight module 302 is configured to obtain initial weights of each unmanned aerial vehicle according to the task position and cluster information of the unmanned aerial vehicle cluster, and generate an initial weight sequence. The initial weight module 302 is specifically configured to calculate, according to the task position and the initial information, an initial weight of each unmanned aerial vehicle, where a calculation formula is as follows:
。
in the method, in the process of the application,representing the initial weight of the drone i,、、and (3) withThe prize coefficients for each of the factors respectively,for the distance of the unmanned aerial vehicle i from the mission location,for the initial attitude angle of the drone i,for the initial speed of the drone i,is the included angle between the average initial speed direction of the unmanned aerial vehicle i and other unmanned aerial vehicles. Wherein the reward coefficient is determined by the ordering of the factors of each drone in the drone cluster. For example, the closer the drone is to the mission location, the greater its corresponding reward coefficient. Conversely, the farther the unmanned aerial vehicle is from the mission location, the smaller its corresponding reward coefficient. And sequencing the initial weights of the unmanned aerial vehicles to obtain an initial weight sequence of the unmanned aerial vehicle. In the embodiment of the application, the order of the initial weights is from big to small. The order from small to large may also be used by those skilled in the art without limiting the scope of the application.
The determining module 303 is configured to determine a long aircraft in the unmanned aerial vehicle cluster according to the initial weight sequence, and take the remaining unmanned aerial vehicles as the plane. The determining module 303 is specifically configured to, in the embodiment of the present application, take, as the long plane of the entire unmanned plane cluster, the unmanned plane corresponding to the initial weight that is the initial weight with the largest initial weight and is arranged at the forefront of the initial weight sequence according to the sequence of each initial weight in the initial weight sequence, and take the remaining unmanned planes as the assistant planes. After the long plane and the bureau plane are determined, the bureau plane can be further adjusted. Comprising the following steps: and determining the expected positions of the various unmanned aerial vehicles according to the positions of the long aerial vehicles, the number of the unmanned aerial vehicles and the set formation of the unmanned aerial vehicle cluster. And determining the expected position corresponding to the initial information of the plane. Controlling the plane to fly to the corresponding expected position and following the plane. Specifically, after each of the bureaus determines its corresponding desired position, it autonomously flies to the corresponding desired position or flies to the corresponding desired position in response to control of the long machine. After each plane flies to the corresponding expected position, the unmanned plane clusters form a set formation, and the long plane can take the cluster task.
The updating module 304 is configured to generate a process weight sequence according to the initial weight updating process weights of the unmanned aerial vehicles if the direction vector of any unmanned aerial vehicle in the unmanned aerial vehicle cluster is changed. The updating module 304 is specifically configured to, in the embodiment of the present application, when the unmanned aerial vehicle cluster executes the cluster task, orient or approximately orient the heading of the unmanned aerial vehicle to the task position, and the direction vector of the unmanned aerial vehicle cannot be easily changed. If the direction vector is changed, special conditions are necessarily met, and the unmanned aerial vehicle is required to change the original direction vector. The changing of the direction vector of the unmanned aerial vehicle comprises: factors that can change the direction vector of the unmanned aerial vehicle, such as obstacle avoidance and/or unmanned aerial vehicle failure. Wherein the unmanned aerial vehicle failure comprises a unmanned aerial vehicle component failure and/or an unmanned aerial vehicle communication failure. In one embodiment of the present application, after a component failure occurs, if the component failure causes the corresponding unmanned aerial vehicle to be unable to continue to execute the flight task, the number of real wing machines and the set formation need to be re-determined after the new long machine is determined. It should be noted that in the embodiment of the application, each unmanned aerial vehicle is provided with the identification device, so that the change of the direction vector of the unmanned aerial vehicle can be identified, and meanwhile, the change of the pseudo direction vector of the unmanned aerial vehicle caused by the external environment can be identified. The false direction vector change comprises the condition that the unmanned aerial vehicle oscillates due to wind blowing, airflow or the like, so that the direction vector of the unmanned aerial vehicle is changed frequently for a plurality of times.
The update formula of the process weight sequence generated according to the current weight update process weight of each unmanned aerial vehicle is as follows:
. In the method, in the process of the application,is the process weight of the unmanned aerial vehicle i,for the current weight of the unmanned aerial vehicle i, rand is a random number between (0, 1),for the maximum value of the current weights in the drone cluster,is the minimum value of the current weight in the unmanned aerial vehicle cluster. The current weight is the weight of each unmanned aerial vehicle when the unmanned aerial vehicle direction vector changes in the unmanned aerial vehicle cluster. In the embodiment of the application, when the long aircraft is determined for the second time, the current weight is the initial weight of each unmanned aerial vehicle, and when the long aircraft is determined after the second time, the current weight is the process weight of each unmanned aerial vehicle at the moment. And updating the process weights through the calculation formula, and sequencing the process weights of the unmanned aerial vehicles to obtain a process weight sequence of the unmanned aerial vehicle.
The process weight module 305 is configured to redetermine the long machines in the unmanned aerial vehicle cluster according to the process weight sequence, and take the remaining unmanned aerial vehicles as the computers. The process weight module 305 is specifically configured to take the unmanned aerial vehicle corresponding to the largest process weight in the process weight sequence as a new long machine, and the remaining unmanned aerial vehicles as the bureau machines. After the long plane and the bureau plane are determined, the expected positions of the bureau planes are determined according to the positions of the long planes, the number of unmanned aerial vehicles and the set formation of the unmanned aerial vehicle cluster. And determining the expected position corresponding to the initial information of the plane. Controlling the plane to fly to the corresponding expected position and following the plane.
Some of the modules of the apparatus of the present application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The apparatus or module set forth in the embodiments of the application may be implemented in particular by a computer chip or entity, or by a product having a certain function. For convenience of description, the above devices are described as being functionally divided into various modules, respectively. The functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present application. Of course, a module that implements a certain function may be implemented by a plurality of sub-modules or a combination of sub-units.
The methods, apparatus or modules described in this application may be implemented in computer readable program code means and the controller may be implemented in any suitable way, for example, the controller may take the form of a microprocessor or processor and a computer readable medium storing computer readable program code (e.g. software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (english: application Specific Integrated Circuit; abbreviated: ASIC), programmable logic controller and embedded microcontroller, examples of the controller including but not limited to the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller can be regarded as a hardware component, and means for implementing various functions included therein can also be regarded as a structure within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The embodiment of the application also provides equipment, which comprises: a processor; a memory for storing processor-executable instructions; the processor, when executing the executable instructions, implements the method according to the embodiments of the present application.
Embodiments of the present application also provide a non-transitory computer readable storage medium having stored thereon a computer program or instructions which, when executed, cause a method as described in embodiments of the present application to be implemented.
In addition, each functional module in the embodiments of the present application may be integrated into one processing module, each module may exist alone, or two or more modules may be integrated into one module.
The storage medium includes, but is not limited to, a random access Memory (English: random Access Memory; RAM), a Read-Only Memory (ROM), a Cache Memory (English: cache), a Hard Disk (English: hard Disk Drive; HDD), or a Memory Card (English: memory Card). The memory may be used to store computer program instructions.
From the above description of embodiments, it will be apparent to those skilled in the art that the present application may be implemented in software plus necessary hardware. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product or may be embodied in the implementation of data migration. The computer software product may be stored on a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., comprising instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the methods described in the various embodiments or portions of the embodiments of the application.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment is mainly described as a difference from other embodiments. All or portions of the present application are operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the present application; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (10)
1. The unmanned aerial vehicle formation control method is characterized by comprising the following steps of:
acquiring task positions and cluster information of unmanned aerial vehicle clusters; the cluster information comprises the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster and initial information of each unmanned aerial vehicle;
determining initial weights of all unmanned aerial vehicles according to the task positions and the cluster information to generate initial weight sequences;
determining a long machine and a plane in the unmanned aerial vehicle cluster according to the initial weight sequence;
if the direction vector of any unmanned aerial vehicle in the unmanned aerial vehicle cluster is changed, generating a process weight sequence according to the current weight updating process weight of each unmanned aerial vehicle;
and re-determining the long aircraft and the plane in the unmanned plane cluster according to the process weight sequence.
2. The method of claim 1, wherein the initial information comprises an initial position, an initial speed, and an initial attitude angle of the drone.
3. The method of claim 1, wherein any one of the drones in the cluster satisfies the following relationship:
；
in the method, in the process of the application,for the position coordinates of the unmanned aerial vehicle, rand represents a random number between (0, 1), +.>、/>Respectively obtaining maximum value and minimum value of x coordinate in the unmanned aerial vehicle cluster, and +.>、/>Respectively obtaining a maximum value and a minimum value of y coordinates in the unmanned aerial vehicle cluster, and performing +.>、/>Respectively the maximum of the z coordinates in the unmanned aerial vehicle clusterValues and minimums.
4. The method of claim 2, wherein determining initial weights for each drone based on the task locations and the cluster information to generate an initial weight sequence comprises:
calculating the initial weight of each unmanned aerial vehicle according to the task position and the initial information, wherein a calculation formula is as follows:
；
in the method, in the process of the application,representing said initial weight of unmanned plane i, < ->、/>、/>And->The prize coefficients for each of the factors respectively,for the distance of unmanned plane i from the task location,/->For said initial attitude angle of unmanned plane i, < >>For said initial speed of unmanned aerial vehicle i, < >>An included angle of the average initial speed direction of the unmanned aerial vehicle i and other unmanned aerial vehicles is formed;
and sequencing the initial weights of the unmanned aerial vehicles to obtain the initial weight sequence of the unmanned aerial vehicle.
5. The method of claim 4, wherein the update formula for generating the sequence of process weights from the current weight update process weights for each drone is as follows:
；
in the method, in the process of the application,for said procedure weight of unmanned plane i, < ->For said current weight of drone i, rand is a random number between (0, 1,) is->For the maximum value of the current weight in the unmanned aerial vehicle cluster, < >>The minimum value of the current weight in the unmanned aerial vehicle cluster is set;
and sequencing the process weights of the unmanned aerial vehicles to obtain the process weight sequence of the unmanned aerial vehicle.
6. The method of claim 1, wherein the determining the kiosks and kiosks in the drone cluster further comprises:
determining the expected position of each wing aircraft according to the positions of the long aircraft, the number of unmanned aerial vehicles and the set formation of the unmanned aerial vehicle cluster;
determining the expected position corresponding to the initial information of the plane according to the initial information of the plane;
controlling the plane to fly to the corresponding expected position and following the plane.
7. The method of claim 1, wherein the cause of the change in the direction vector of the drone comprises: unmanned aerial vehicle keeps away barrier and/or unmanned aerial vehicle trouble.
8. An unmanned aerial vehicle formation controlling means, characterized by comprising:
the acquisition module is used for acquiring the task position and cluster information of the unmanned aerial vehicle clusters; the cluster information comprises the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster and initial information of each unmanned aerial vehicle;
the initial weight module is used for determining the initial weight of each unmanned aerial vehicle according to the task position and the cluster information to generate an initial weight sequence;
the determining module is used for determining the plane and the plane in the unmanned aerial vehicle cluster according to the initial weight sequence;
the updating module is used for generating a process weight sequence according to the current weight updating process weight of each unmanned aerial vehicle if the direction vector of any unmanned aerial vehicle in the unmanned aerial vehicle cluster is changed;
and the process weight module is used for redetermining the long plane and the bureau in the unmanned aerial vehicle cluster according to the process weight sequence.
9. An apparatus for performing a method of unmanned aerial vehicle formation control, comprising:
a processor;
a memory for storing processor-executable instructions;
the processor, when executing the executable instructions, implements the method of any one of claims 1 to 7.
10. A non-transitory computer readable storage medium comprising instructions for storing a computer program or instructions which, when executed, cause the method of any one of claims 1 to 7 to be implemented.
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