CN114840023A - Unmanned aerial vehicle multi-path planning method and system, computer equipment and storage medium - Google Patents
Unmanned aerial vehicle multi-path planning method and system, computer equipment and storage medium Download PDFInfo
- Publication number
- CN114840023A CN114840023A CN202210543902.4A CN202210543902A CN114840023A CN 114840023 A CN114840023 A CN 114840023A CN 202210543902 A CN202210543902 A CN 202210543902A CN 114840023 A CN114840023 A CN 114840023A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- path
- grid
- flight
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention discloses an unmanned aerial vehicle multipath planning method and system, computer equipment and a storage medium, wherein the method comprises the steps of constructing a two-dimensional grid model of an unmanned aerial vehicle cluster; acquiring unmanned plane grid planning path consumption data; acquiring information of optimal flight paths of a plurality of unmanned aerial vehicles; and sending the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicles in the information of the optimal flight paths of the plurality of unmanned aerial vehicles, and enabling the unmanned aerial vehicles to fly to the grid area corresponding to the target point according to the received information of the optimal flight path of the unmanned aerial vehicles. According to the method, the mesh corresponding to the initial point and the mesh corresponding to the target point in the two-dimensional mesh model of the unmanned aerial vehicle cluster are used for acquiring the mesh planning path consumption data of the unmanned aerial vehicle, the route planning is carried out on the flight path of the unmanned aerial vehicle based on the target evolution algorithm, the optimal path information of the flight of a plurality of unmanned aerial vehicles is acquired, the unmanned aerial vehicle can conveniently carry out diversity selection on the flight path, and the completion degree of the flight task of the unmanned aerial vehicle is effectively improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle multi-path planning method and system, computer equipment and a storage medium.
Background
The unmanned aerial vehicle is widely applied to the fields of military, civil use and the like due to the characteristics of low cost, high maneuverability, flexible deployment, zero casualties and the like. When the unmanned aerial vehicle executes tasks such as military monitoring and reconnaissance, large-scale disaster scene search and rescue and the like, the unmanned aerial vehicle faces adverse factors such as wide task area, complex and changeable environment, limited sensor perception capability and inconsistent flight risk degree, and the tasks are easy to fail.
The existing unmanned aerial vehicle path planning method cannot consider a plurality of optimal paths existing in the same environmental condition, wherein the shortest path is always the first consideration target of the problem, and a decision maker can only passively select the optimal path planned by the system.
Therefore, it is desirable to provide a multi-path planning method for an unmanned aerial vehicle to solve the problem.
Disclosure of Invention
Based on this, it is necessary to provide an unmanned aerial vehicle multipath planning method and system, a computer device, and a storage medium for overcoming the defects in the prior art, so as to obtain multiple unmanned aerial vehicle flight optimal paths, facilitate the diversity selection of flight paths by the unmanned aerial vehicle, and improve the completion of the flight mission of the unmanned aerial vehicle.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in a first aspect, a method for multipath planning of an unmanned aerial vehicle is provided, which includes the following steps:
step S110, constructing a two-dimensional grid model of the unmanned aerial vehicle cluster, and obtaining a danger coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle cluster i,j (ii) a Wherein each grid is represented by a set of indices (i, j), R i,j ∈[0,1]；
Step S120, acquiring unmanned aerial vehicle grid planning path consumption data according to grids corresponding to an initial point and grids corresponding to a target point in a two-dimensional grid model of the unmanned aerial vehicle cluster;
s130, carrying out route planning on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm, and acquiring information of optimal paths for the flight of the unmanned aerial vehicles;
and S140, sending the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicles in the information of the optimal flight paths of the plurality of unmanned aerial vehicles, and enabling the unmanned aerial vehicles to fly to the grid area corresponding to the target point according to the received information of the optimal flight path of the unmanned aerial vehicles.
In a second aspect, there is provided an unmanned aerial vehicle multi-path planning system, comprising:
the spatial grid modeling module is used for constructing a two-dimensional grid model of the unmanned aerial vehicle group and acquiring the risk coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle group i,j ；
The planning path consumption module is used for acquiring unmanned aerial vehicle mesh planning path consumption data according to a mesh corresponding to an initial point and a mesh corresponding to a target point in a two-dimensional mesh model of the unmanned aerial vehicle cluster;
the path planning module is used for carrying out path planning on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm and acquiring the optimal path information of the flight of the unmanned aerial vehicles;
and the flight path execution module is used for sending the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicle in the information of the optimal flight paths of the plurality of unmanned aerial vehicles, and the unmanned aerial vehicle flies to a grid area corresponding to the target point according to the received information of the optimal flight paths of the unmanned aerial vehicle.
In a third aspect, an apparatus is provided, which includes a memory and a processor, where the memory stores a computer program thereon, and the processor implements the above-mentioned unmanned aerial vehicle multipath planning method when executing the computer program.
In a fourth aspect, there is provided a storage medium storing a computer program comprising program instructions which, when executed, implement the above-described unmanned aerial vehicle multi-path planning method.
In summary, the unmanned aerial vehicle multi-path planning method and system, the computer device and the storage medium of the present invention obtain unmanned aerial vehicle mesh planning path consumption data through a mesh corresponding to an initial point and a mesh corresponding to a target point of an unmanned aerial vehicle in a two-dimensional mesh model of an unmanned aerial vehicle cluster, perform route planning on an unmanned aerial vehicle flight path based on a target evolution algorithm, obtain optimal path information for flight of multiple unmanned aerial vehicles, facilitate the unmanned aerial vehicle to perform diverse selection of flight paths, and effectively improve the completion degree of a flight task of the unmanned aerial vehicle.
Drawings
Fig. 1 is a schematic flowchart of a first method for multi-path planning of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a second method for planning multiple paths of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a third method for planning multiple paths of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of a fourth method for planning multiple paths of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 5 is a schematic flowchart of a fifth method for multi-path planning of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 6 is a block diagram of a first unmanned aerial vehicle multipath planning system according to an embodiment of the present invention;
fig. 7 is a block diagram of a second unmanned aerial vehicle multipath planning system according to an embodiment of the present invention;
fig. 8 is a block diagram of a third unmanned aerial vehicle multipath planning system according to an embodiment of the present invention;
FIG. 9 is a block diagram of a computer device according to an embodiment of the present invention;
fig. 10 is a schematic diagram of two optimal paths for the unmanned aerial vehicle to fly according to the embodiment of the present invention.
Detailed Description
For further understanding of the features and technical means of the present invention, as well as the specific objects and functions attained by the present invention, the present invention will be described in further detail with reference to the accompanying drawings and detailed description.
Fig. 1 is a schematic flow chart of a first unmanned aerial vehicle multipath planning method provided in an embodiment of the present invention, and as shown in fig. 1, the unmanned aerial vehicle multipath planning method includes steps S110 to S140, which specifically include the following steps:
step S110, constructing a two-dimensional grid model of the unmanned aerial vehicle cluster, and obtaining a danger coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle cluster i,j (ii) a Wherein each grid is represented by a set of indices (i, j), R i,j ∈[0,1]。
In this embodiment, the risk factor R of the passing grid i,j Obtaining the passing grade P corresponding to the grid i,j ，P i,j ∈[0,1]。
The method of step S110 specifically includes:
the method comprises the steps of obtaining risk information of the unmanned aerial vehicle group at each position in a task execution area, wherein the risk information comprises the range of buildings, the distance between the buildings, the communication signal intensity and the like in the task execution area, surveying and mapping the geographic space where the task execution area of the unmanned aerial vehicle group is located, and detecting the signal intensity of the corresponding communication wave band in the task execution area of the unmanned aerial vehicle group.
Dividing a task execution area into a plurality of grids according to the preset grid size, and constructing a two-dimensional grid model of the unmanned aerial vehicle cluster; the size of the grid is determined according to needs, and in the embodiment, the size of the grid area of the task execution area is 1m × 1 m;
risk coefficient R of each grid in two-dimensional grid model of unmanned aerial vehicle group is obtained based on risk information i,j (ii) a Each grid is represented by a group of indexes (i, j), based on judgment of risk information at each position in a task execution area, corresponding risk coefficients when an unmanned aerial vehicle passes through different grids are obtained, the risk information corresponding to the grids is a grid central point sampling result, the grid passing grade refers to the probability that the unmanned aerial vehicle passes through the grids, the grid risk coefficients refer to the danger degree that the unmanned aerial vehicle passes through the grids, areas corresponding to the grids are divided into impassable areas when buildings, high-strength reconnaissance and the like exist, the passing grade strength of the grids is recorded as 0, the risk coefficients of the grids are infinite, and in the embodiment, the risk coefficient strength of the grids can be recorded as 1.
Specifically, the unmanned aerial vehicle for identification is moved within the task execution area, it is assumed that the unmanned aerial vehicle can pass through a blank area without hindrance on a horizontal plane where a fixed height of the space is located, a risk area and a risk coefficient exist in a grid space of a two-dimensional grid model of the unmanned aerial vehicle fleet, and whether a passing level is allowed or not can be added at the fixed height horizontal plane of the two-dimensional grid model of the unmanned aerial vehicle fleet.
Step S120, acquiring unmanned aerial vehicle grid planning path consumption data according to grids corresponding to an initial point and grids corresponding to a target point in a two-dimensional grid model of the unmanned aerial vehicle cluster; wherein, the initial point of unmanned aerial vehicle is unmanned aerial vehicle's flight starting point, and unmanned aerial vehicle's target point is unmanned aerial vehicle's that the user set for flight terminal point, and unmanned aerial vehicle planning route consumes data and has represented the degree of difficulty that unmanned aerial vehicle flies from initial grid point to target grid point, and unmanned aerial vehicle planning route consumes data and is unmanned aerial vehicle flight path f 1 Sigma path and unmanned aerial vehicle flight grid region danger coefficient f 2 ＝∑path*R ij The method comprises the steps of accumulating, wherein a path represents a grid point set corresponding to grids passed by a flight path of the unmanned aerial vehicle, namely, the grids passed by the flight path of the unmanned aerial vehicle are set as grid points to form the grid point set, the given grid points are represented by indexes (i, j), and risk coefficients corresponding to the grids passed by the flight path of the unmanned aerial vehicle are accumulated to obtain a risk coefficient of a flight grid area of the unmanned aerial vehicle.
Specifically, the method of step S120 specifically includes:
initializing the multi-objective evolutionary algorithm according to a grid corresponding to an initial point and a grid corresponding to a target point of the unmanned aerial vehicle in a two-dimensional grid model of the unmanned aerial vehicle cluster based on the multi-objective evolutionary algorithm, and performing preliminary exploration on a population by the multi-objective evolutionary algorithm so as to optimize a flight path of the unmanned aerial vehicle and a danger coefficient of a flight grid area of the unmanned aerial vehicle and obtain a grid planning path consumption value of the unmanned aerial vehicle; the multi-objective evolutionary algorithm is initialized by using heuristic rules, wherein the heuristic rules are completed by a traditional A-algorithm, which is a known technology and need not be described herein in detail.
Because most unmanned aerial vehicles are all at constant flying height, unmanned aerial vehicle flight space sets up on the horizontal plane of fixed altitude, assumes that the unmanned aerial vehicle flight area is a blank region, and the obstacle that appears in the space must first mark to unmanned aerial vehicle can avoid colliding it, if the obstacle is a big object, such as the building, it is regarded as a continuous obstacle, and the grade intensity of passing through of a plurality of grids that this obstacle corresponds is 0, and danger coefficient intensity is 1.
According to the target location set by the user, the starting point coordinates are assumed to be in the upper left corner of the two-dimensional grid map in this embodiment, as shown in fig. 10, two optimal path schematic diagrams in this embodiment are given, where a small black point represents that the area can pass through, a large black point represents that the area has a risk, and a large gray point corresponds to an initial point and a target point on the flight path of the unmanned aerial vehicle. In both of these possible paths, the path length is 41 and the risk zones traversed are 4, however, the two paths are completely different.
Initializing a multi-target evolutionary algorithm by using information such as risk preference; specifically, the multi-objective evolutionary algorithm initially explores the population by using an initialization method including heuristic rules, and can simultaneously optimize two objective functions: the unmanned aerial vehicle flight path objective function and the unmanned aerial vehicle flight grid area danger coefficient objective function are used for acquiring unmanned aerial vehicle grid planning path consumption data through accumulation of the unmanned aerial vehicle flight path and the unmanned aerial vehicle flight grid area danger coefficients, and the multi-mode multi-target evolutionary algorithm is adopted.
Step S130, route planning is carried out on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm, information of the optimal paths of the flight of the multiple unmanned aerial vehicles is obtained, the unmanned aerial vehicles can conveniently carry out diversity selection on the flight paths, and the completion degree of flight tasks of the unmanned aerial vehicles is improved.
For a user, different risk levels often correspond to different path lengths and task costs, and when the user wants to know multiple paths with the same target value, in such a case, it is very important to improve the diversity of the obtained solutions; on the other hand, with the same risk level and path length, if a plurality of different paths can be obtained, great help is provided for the completion of the task. If a plurality of optimal paths are planned in advance through a calculation method, and then a user selects a preferred path to provide for the unmanned aerial vehicle, the task completion degree can be greatly improved.
Specifically, the method of step S130 specifically includes:
s131, in a searching stage of the target evolution algorithm, the target evolution algorithm pays attention to diversity indexes of a population, potential optimal individuals can be reserved according to current population characteristics in each iterative calculation, different individuals with similar objective functions are reserved, and finally, information of optimal paths of flight of multiple unmanned aerial vehicles is output; the path consumption value is composed of two objective functions, namely an unmanned aerial vehicle flight path objective function and a flight area risk coefficient objective function, and is respectively corresponding to a grid point number and a risk coefficient, for the same path consumption value, if the grid point number is 10 and the risk coefficient is 0.8, a plurality of different unmanned aerial vehicle flight paths may exist to meet the requirement, and the target evolution algorithm can automatically obtain the unmanned aerial vehicle flight paths with the same path consumption value.
Further, the method for reserving the potentially optimal individual according to the current population characteristics in each iteration calculation in step S131 specifically includes:
in each generation of iterative evolution, the target evolution algorithm selects and sorts individuals by adopting an improved population crowding degree calculation method, wherein the population crowding degree f i d The calculation method is expressed as
Wherein, Li and L j The length of the path is indicated by,
indicating the common path length of the two paths, CD max Is a CD i,j Maximum value of (1), CD i,j Expressed as the magnitude of the difference between individual i and individual j.
In each generation of iterative evolution, a target evolution algorithm selects offspring populations by using a special environment selection strategy, specifically, all individuals are repeatedly checked, the same individuals are deleted, then, Pareto dominant sorting and population crowding are used as sorting criteria to sort the individuals, and the offspring individuals are selected; an individual refers to a solution in a population; deleting the identical individuals (solutions), namely deleting one of the two individuals if the paths represented by the two individuals are identical; pareto dominating and sorting into the existing algorithm, and the population crowding degree is the index f i d The individuals are sorted according to two criteria of Pareto dominance sorting and population crowding degree, and the top N (population size) individuals are selected to enter the next generation.
As shown in fig. 2, in an embodiment, before the step S130, the method further includes
S130-1, setting a risk coefficient threshold value of a flight grid area of the unmanned aerial vehicle; according to the risk preference information provided by the user, the risk coefficient threshold value of the unmanned aerial vehicle flight grid region is set, the numerical value between 0 and 1 represents that the higher the risk coefficient threshold value of the unmanned aerial vehicle flight grid region is, the larger the unmanned aerial vehicle flight risk that the user can bear is, and the more selection possibility exists in the unmanned aerial vehicle flight path.
Step S140, sending the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicle from the information of the optimal flight paths of the plurality of unmanned aerial vehicles, and enabling the unmanned aerial vehicle to fly to a grid area corresponding to a target point according to the received information of the optimal flight path of the unmanned aerial vehicle, so as to realize automatic flight operation of the unmanned aerial vehicle; specifically, the user selects one of the optimal path information of the unmanned aerial vehicle according to the demand, the unmanned aerial vehicle automatically reaches the grid area where the target point is located according to the path planning result, the unmanned aerial vehicle is convenient to carry out diversity selection of the flight path, and the completion degree of the flight task of the unmanned aerial vehicle is improved.
The information of the optimal flight paths of the multiple unmanned aerial vehicles obtained in the step S130 is provided for a user to select, in the process, the user can select to send out more than one unmanned aerial vehicle to execute the task, different unmanned aerial vehicles automatically reach the target point according to the information of the optimal flight paths of the different unmanned aerial vehicles selected by the user, the task can be executed on the optimal flight paths by deploying more than one unmanned aerial vehicle, and the probability of success of the task of the unmanned aerial vehicles is greatly improved.
As shown in fig. 3, in an embodiment, after the step S140, the method further includes
Step S150, when the unmanned aerial vehicle executes a task according to the received information of the optimal flight path of the unmanned aerial vehicle, judging whether obstacles exist on the path from the current grid point to the next grid point of the unmanned aerial vehicle, if so, identifying the obstacles which cannot fly, and acquiring the information of the optimal flight path of the next unmanned aerial vehicle from the computer equipment by using the communication unit; if not, go to step S140; the communication unit is an unmanned aerial vehicle self-contained device so as to establish a wireless communication network between the unmanned aerial vehicle and the computer equipment.
As shown in fig. 4, in an embodiment, after the step S140, the method further includes
Step S160, when the unmanned aerial vehicle executes a task according to the received information of the optimal flight path of the unmanned aerial vehicle, judging whether the current optimal flight path of the unmanned aerial vehicle has a traffic condition, if so, executing step S140; if not, acquiring the next optimal flight path information of the unmanned aerial vehicle from the computer equipment by using the communication unit; wherein, the communication unit is unmanned aerial vehicle from taking the setting to establish unmanned aerial vehicle and computer equipment's wireless communication network.
According to the unmanned aerial vehicle multi-path planning method, the mesh corresponding to the initial point and the mesh corresponding to the target point of the unmanned aerial vehicle in the two-dimensional mesh model of the unmanned aerial vehicle cluster are used for acquiring the unmanned aerial vehicle mesh planning path consumption data, the route planning is carried out on the unmanned aerial vehicle flight path based on the target evolution algorithm, the optimal path information of the flight of a plurality of unmanned aerial vehicles is acquired, the unmanned aerial vehicle can conveniently carry out diversity selection on the flight path, and the completion degree of the flight task of the unmanned aerial vehicle is effectively improved.
As shown in fig. 5, in order to make the technical solution of the present invention more clear, the following describes a preferred embodiment.
Step S110, constructing a two-dimensional grid model of the unmanned aerial vehicle cluster, and obtaining a danger coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle cluster i,j ；
Step S120, acquiring unmanned aerial vehicle grid planning path consumption data according to grids corresponding to an initial point and grids corresponding to a target point in a two-dimensional grid model of the unmanned aerial vehicle cluster;
s130-1, setting a risk coefficient threshold value of a flight grid area of the unmanned aerial vehicle;
s130, carrying out route planning on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm, and acquiring information of optimal paths for the flight of the unmanned aerial vehicles;
step S140, sending information of the optimal flight path of one of the unmanned planes to the unmanned plane in the information of the optimal flight paths of the plurality of unmanned planes, and enabling the unmanned plane to fly to a grid area corresponding to a target point according to the received information of the optimal flight path of the unmanned plane;
step S150, when the unmanned aerial vehicle executes a task according to the received information of the optimal flight path of the unmanned aerial vehicle, judging whether obstacles exist on the path from the current grid point to the next grid point of the unmanned aerial vehicle, if so, identifying the obstacles which cannot fly, and acquiring the information of the optimal flight path of the next unmanned aerial vehicle from the computer equipment by using the communication unit; if not, go to step S140.
Fig. 6 is a block diagram of a first unmanned aerial vehicle multipath planning system provided by an embodiment of the present invention, as shown in fig. 6, corresponding to the unmanned aerial vehicle multi-path planning method, the invention also provides an unmanned aerial vehicle multi-path planning system, the unmanned aerial vehicle multi-path planning system comprises a module for executing the unmanned aerial vehicle multi-path planning method, the system can be configured at terminals such as computer equipment, and the unmanned aerial vehicle multipath planning system of the invention is applied, the mesh corresponding to the initial point and the mesh corresponding to the target point in the two-dimensional mesh model of the unmanned aerial vehicle cluster are used for acquiring the mesh planning path consumption data of the unmanned aerial vehicle, the path planning is carried out on the flight path of the unmanned aerial vehicle based on the target evolution algorithm, the optimal path information of the flight of the unmanned aerial vehicle is acquired, the unmanned aerial vehicle is convenient to carry out diversity selection on the flight path, and the completion degree of the flight task of the unmanned aerial vehicle is effectively improved.
Specifically, as shown in fig. 6, the unmanned aerial vehicle multi-path planning system includes a space grid modeling module, a planned path consumption module, a path planning module, and a flight path execution module.
The spatial grid modeling module is used for constructing a two-dimensional grid model of the unmanned aerial vehicle group and acquiring the risk coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle group i,j ；
The planning path consumption module is used for acquiring unmanned aerial vehicle mesh planning path consumption data according to a mesh corresponding to an initial point and a mesh corresponding to a target point in a two-dimensional mesh model of the unmanned aerial vehicle cluster;
the path planning module is used for carrying out path planning on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm and acquiring the optimal path information of the flight of the unmanned aerial vehicles;
and the flight path execution module is used for sending the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicle in the information of the optimal flight paths of the plurality of unmanned aerial vehicles, and the unmanned aerial vehicle flies to a grid area corresponding to the target point according to the received information of the optimal flight paths of the unmanned aerial vehicle.
Fig. 7 is a block diagram of a second unmanned aerial vehicle multipath planning system provided in the embodiment of the present invention. As shown in fig. 7, in the multi-path planning system for an unmanned aerial vehicle according to the embodiment, a barrier determination module is added on the basis of the multi-path planning system for an unmanned aerial vehicle, and the barrier determination module is used for determining whether a barrier exists in a path from a current mesh point to a next mesh point of the unmanned aerial vehicle when the unmanned aerial vehicle executes a task according to received optimal path information of flight of the unmanned aerial vehicle, and if so, acquiring optimal path information of flight of the next unmanned aerial vehicle from a computer device by using a communication unit.
Fig. 8 is a block diagram of a second unmanned aerial vehicle multipath planning system provided in the embodiment of the present invention. As shown in fig. 8, in the multi-path planning system for the unmanned aerial vehicle provided by this embodiment, a threshold setting module is added on the basis of the multi-path planning system for the unmanned aerial vehicle, and the threshold setting module is used for setting a risk coefficient threshold of a flight grid area of the unmanned aerial vehicle; according to the risk preference information provided by the user, the risk coefficient threshold value of the flight grid region of the unmanned aerial vehicle is set, the numerical value between 0 and 1 represents, the higher the risk coefficient threshold value of the flight grid region of the unmanned aerial vehicle is, the larger the flight risk of the unmanned aerial vehicle which can be borne by the user is, and the more selection possibility exists in the flight path of the unmanned aerial vehicle.
It should be noted that, as can be clearly understood by those skilled in the art, the specific implementation process of the above unmanned aerial vehicle multipath planning system and each module may refer to the corresponding description in the foregoing method embodiment, and for convenience and conciseness of description, no further description is provided herein.
Fig. 9 is a block diagram illustrating an internal structure of a computer device according to an embodiment of the present invention, where, as shown in fig. 9, the computer device according to the present invention includes a memory, a processor, and a network interface connected via a system bus; the storage is stored with a computer program, the processor is used for providing calculation and control capability to support the operation of the whole computer equipment, and the processor realizes the unmanned aerial vehicle multipath planning method when executing the computer program.
The memory may include a non-volatile storage medium storing an operating system and an internal memory, and may also store a computer program that, when executed by the processor, may cause the processor to implement the drone multipath planning method.
The internal memory may also have a computer program stored therein, which when executed by the processor, causes the processor to perform a method for multi-path planning for a drone. The network interface is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 9 is a block diagram of only a portion of the configuration relevant to the present teachings and is not intended to limit the applicability of the present teachings to other computing devices that may include more or less components than those shown, or combine certain components, or have a different arrangement of components.
In one embodiment, the unmanned aerial vehicle multipath planning method provided by the present application may be implemented as a computer program, and the computer program may be run on a computer device as shown in fig. 9. The memory of the computer device may store various program modules constituting the unmanned aerial vehicle multipath planning system, such as the acquisition module 110, the transformation module 120, the construction module 130, and the determination module 140 shown in fig. 6. The computer program of each program module makes the processor execute the steps of the unmanned aerial vehicle multipath planning system of each embodiment of the present application described in the present specification. For example, the computer device shown in fig. 6 may construct a two-dimensional grid model of the unmanned aerial vehicle group through a spatial grid modeling module in the unmanned aerial vehicle multipath planning system shown in fig. 4, and obtain a risk coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle group i,j (ii) a The planning path consumption module acquires unmanned aerial vehicle mesh planning path consumption data according to a mesh corresponding to an initial point and a mesh corresponding to a target point in a two-dimensional mesh model of the unmanned aerial vehicle cluster; the path planning module carries out path planning on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm to obtain information of optimal paths for the flight of the unmanned aerial vehicles; the flight path execution module sends the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicle in the information of the optimal flight paths of the multiple unmanned aerial vehicles, and the unmanned aerial vehicle flies to a grid area corresponding to the target point according to the received information of the optimal flight paths of the unmanned aerial vehicle.
In one embodiment, a computer device is presented, comprising a memory and a processor, the memory and the processor storing a computer program which, when executed by the processor, causes the processor to perform the steps of: step S110, constructing a two-dimensional grid model of the unmanned aerial vehicle cluster, and obtaining a danger coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle cluster i,j (ii) a Step S120, acquiring unmanned aerial vehicle grid planning path consumption data according to grids corresponding to an initial point and grids corresponding to a target point in a two-dimensional grid model of the unmanned aerial vehicle cluster; step (ii) ofS130, carrying out route planning on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm, and acquiring information of optimal paths for the flight of the unmanned aerial vehicles; and S140, sending the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicles in the information of the optimal flight paths of the plurality of unmanned aerial vehicles, and enabling the unmanned aerial vehicles to fly to the grid area corresponding to the target point according to the received information of the optimal flight path of the unmanned aerial vehicles.
It should be understood that in the embodiments of the present Application, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program comprises program instructions. The program instructions, when executed by the processor, cause the processor to perform the steps of: step S110, constructing a two-dimensional grid model of the unmanned aerial vehicle cluster, and obtaining a danger coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle cluster i,j (ii) a Step S120, acquiring unmanned aerial vehicle grid planning path consumption data according to grids corresponding to an initial point and grids corresponding to a target point in a two-dimensional grid model of the unmanned aerial vehicle cluster; step S130, based on the target evolutionary algorithm pairCarrying out route planning on the human-machine flight path to obtain information of a plurality of optimal flight paths of the unmanned aerial vehicle; and S140, sending the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicles in the information of the optimal flight paths of the plurality of unmanned aerial vehicles, and enabling the unmanned aerial vehicles to fly to the grid area corresponding to the target point according to the received information of the optimal flight path of the unmanned aerial vehicles.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.
In summary, the unmanned aerial vehicle multi-path planning method, the system, the device and the storage medium of the invention acquire unmanned aerial vehicle mesh planning path consumption data through a mesh corresponding to an initial point and a mesh corresponding to a target point of an unmanned aerial vehicle in a two-dimensional mesh model of an unmanned aerial vehicle cluster, perform route planning on an unmanned aerial vehicle flight path based on a target evolution algorithm, acquire optimal path information of flight of multiple unmanned aerial vehicles, facilitate the unmanned aerial vehicle to perform diversity selection of the flight path, and effectively improve the completion degree of a flight task of the unmanned aerial vehicle.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated in another system or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing an apparatus (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
The above examples are merely illustrative of several embodiments of the present invention, and the description thereof is more specific and detailed, but not to be construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.
Claims (10)
1. An unmanned aerial vehicle multi-path planning method is characterized by comprising the following steps:
step S110, constructing a two-dimensional grid model of the unmanned aerial vehicle cluster, and obtaining a danger coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle cluster i，j (ii) a Wherein each grid is represented by a set of indices (i, j), R i，j ∈[0，1]；
Step S120, acquiring unmanned aerial vehicle grid planning path consumption data according to grids corresponding to an initial point and grids corresponding to a target point in a two-dimensional grid model of the unmanned aerial vehicle cluster;
s130, carrying out route planning on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm, and acquiring information of optimal paths for the flight of the unmanned aerial vehicles;
and S140, sending the information of the optimal flight path of one of the unmanned planes to the unmanned plane in the information of the optimal flight paths of the plurality of unmanned planes, and enabling the unmanned plane to fly to a grid area corresponding to the target point according to the received information of the optimal flight path of the unmanned plane.
2. An unmanned aerial vehicle multi-path planning method according to claim 1, wherein: before the step S130, the method further comprises
And S130-1, setting a danger coefficient threshold value of the unmanned aerial vehicle flight grid area.
3. An unmanned aerial vehicle multi-path planning method according to claim 1, wherein: after the step S140, the method further comprises
Step S150, when the unmanned aerial vehicle executes a task according to the received information of the optimal flight path of the unmanned aerial vehicle, judging whether obstacles exist on the path from the current grid point to the next grid point of the unmanned aerial vehicle, if so, identifying the obstacles which cannot fly, and acquiring the information of the optimal flight path of the next unmanned aerial vehicle from the computer equipment by using the communication unit; if not, go to step S140.
4. An unmanned aerial vehicle multi-path planning method according to claim 1, wherein: after the step S140, the method further comprises
Step S160, when the unmanned aerial vehicle executes a task according to the received information of the optimal flight path of the unmanned aerial vehicle, judging whether the current optimal flight path of the unmanned aerial vehicle has a traffic condition, if so, executing step S140; and if not, acquiring the next optimal flight path information of the unmanned aerial vehicle from the computer equipment by using the communication unit.
5. An unmanned aerial vehicle multi-path planning method according to claim 1, wherein the method of step S110 includes:
acquiring risk information of the unmanned aerial vehicle group at each position in a task execution area;
dividing a task execution area into a plurality of grids according to the preset grid size, and constructing a two-dimensional grid model of the unmanned aerial vehicle cluster;
and acquiring the risk coefficient of each grid in the two-dimensional grid model of the unmanned aerial vehicle cluster based on the risk information.
6. An unmanned aerial vehicle multi-path planning method according to claim 1, wherein the method of step S120 specifically operates as follows:
based on the multi-objective evolutionary algorithm, initializing the multi-objective evolutionary algorithm according to a grid corresponding to an initial point and a grid corresponding to a target point of the unmanned aerial vehicle in a two-dimensional grid model of the unmanned aerial vehicle cluster, and preliminarily exploring a population by using the multi-objective evolutionary algorithm so as to optimize a flight path of the unmanned aerial vehicle and a danger coefficient of a flight grid area of the unmanned aerial vehicle and obtain a consumption value of a grid planning path of the unmanned aerial vehicle.
7. The multipath planning method for unmanned aerial vehicle of claim 1, wherein the method of step S130 specifically comprises:
in the searching stage of the target evolution algorithm, the target evolution algorithm pays attention to the diversity index of the population, potential optimal individuals can be reserved according to the current population characteristics in each iteration calculation, different individuals with similar objective functions are reserved, and finally the optimal flight path information of the unmanned aerial vehicles is output; the method for reserving the potential optimal individual according to the current population characteristics in each iteration calculation comprises the following specific operations:
in each generation of iterative evolution, the target evolution algorithm selects and sorts individuals by adopting an improved population crowding degree calculation method, wherein the population crowding degree f i d The calculation method is expressed as
Wherein L is i 、L j The length of the path is indicated by,
indicating the common path length of the two paths, CD max Is a CD i，j Maximum value of (1), CD i，j Expressed as the difference between individual i and individual j.
8. An unmanned aerial vehicle multi-path planning system, comprising:
the spatial grid modeling module is used for constructing a two-dimensional grid model of the unmanned aerial vehicle group and acquiring the risk coefficient R of each grid in the two-dimensional grid model of the unmanned aerial vehicle group i，j ；
The planning path consumption module is used for acquiring unmanned aerial vehicle mesh planning path consumption data according to a mesh corresponding to an initial point and a mesh corresponding to a target point in a two-dimensional mesh model of the unmanned aerial vehicle cluster;
the path planning module is used for carrying out path planning on the flight paths of the unmanned aerial vehicles based on a target evolution algorithm and acquiring the optimal path information of the flight of the unmanned aerial vehicles;
and the flight path execution module is used for sending the information of the optimal flight path of one of the unmanned aerial vehicles to the unmanned aerial vehicle in the information of the optimal flight paths of the plurality of unmanned aerial vehicles, and the unmanned aerial vehicle flies to a grid area corresponding to the target point according to the received information of the optimal flight paths of the unmanned aerial vehicle.
9. An apparatus, characterized by: the apparatus comprises a memory having stored thereon a computer program which, when executed by a processor, implements the unmanned aerial vehicle multi-path planning method of any of claims 1-7.
10. A storage medium, characterized by: the storage medium stores a computer program comprising program instructions that, when executed, implement the unmanned aerial vehicle multi-path planning method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210543902.4A CN114840023A (en) | 2022-05-19 | 2022-05-19 | Unmanned aerial vehicle multi-path planning method and system, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210543902.4A CN114840023A (en) | 2022-05-19 | 2022-05-19 | Unmanned aerial vehicle multi-path planning method and system, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114840023A true CN114840023A (en) | 2022-08-02 |
Family
ID=82569782
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210543902.4A Pending CN114840023A (en) | 2022-05-19 | 2022-05-19 | Unmanned aerial vehicle multi-path planning method and system, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114840023A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115860292A (en) * | 2022-11-21 | 2023-03-28 | 武汉坤达安信息安全技术有限公司 | Fishing administration monitoring-based optimal path planning method and device for unmanned aerial vehicle |
-
2022
- 2022-05-19 CN CN202210543902.4A patent/CN114840023A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115860292A (en) * | 2022-11-21 | 2023-03-28 | 武汉坤达安信息安全技术有限公司 | Fishing administration monitoring-based optimal path planning method and device for unmanned aerial vehicle |
| CN115860292B (en) * | 2022-11-21 | 2023-08-04 | 武汉坤达安信息安全技术有限公司 | Unmanned aerial vehicle optimal planning path method and device based on fishery monitoring |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109828607B (en) | Unmanned aerial vehicle path planning method and system for irregular obstacles | |
| Azpúrua et al. | Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys | |
| US20180308371A1 (en) | Joint search method for uav multiobjective path planning in urban low altitude environment | |
| CN107314772B (en) | Unmanned aerial vehicle self-learning waypoint track flight method and system thereof | |
| US20090210109A1 (en) | Computing Flight Plans for UAVs While Routing Around Obstacles Having Spatial and Temporal Dimensions | |
| Wang et al. | A bayesian approach to building footprint extraction from aerial lidar data | |
| CN110770791A (en) | Image boundary acquisition method and device based on point cloud map and aircraft | |
| JP6980289B2 (en) | Learning method and learning device that can detect lanes using a lane model, and test method and test device using this {LEARNING METHOD, LEARNING DEVICE FOR DETECTING LANE USING LANE MODEL AND TEST METHOD, TEST DEVICE | |
| CN108801268A (en) | Localization method, device and the robot of target object | |
| CN109506654A (en) | Low latitude Route planner and device, aircraft | |
| CN110222767A (en) | Three-dimensional point cloud classification method based on nested neural and grating map | |
| CN110471426A (en) | Unmanned intelligent vehicle automatic Collision Avoidance method based on quantum wolf pack algorithm | |
| CN111694356A (en) | Driving control method and device, electronic equipment and storage medium | |
| CN111310992B (en) | Multi-unmanned aerial vehicle path optimization method for rapid evaluation after earthquake disaster | |
| CN113848987B (en) | Dynamic path planning method and system in search of cooperative target of unmanned aerial vehicle cluster | |
| Trujillo et al. | Optimized flight path for 3D mapping of an area with structures using a multirotor | |
| CN112444263A (en) | Global path planning method and device | |
| EP3764190B1 (en) | Landing an unmanned aerial vehicle in a contingency scenario | |
| CN111874007A (en) | Knowledge and data drive-based unmanned vehicle hierarchical decision method, system and device | |
| Castagno et al. | Comprehensive risk-based planning for small unmanned aircraft system rooftop landing | |
| CN112348867A (en) | Method and system for constructing city high-precision three-dimensional terrain based on LiDAR point cloud data | |
| CN114840023A (en) | Unmanned aerial vehicle multi-path planning method and system, computer equipment and storage medium | |
| CN113592891A (en) | Unmanned vehicle passable area analysis method and navigation grid map manufacturing method | |
| CN115346183A (en) | Lane line detection method, terminal and storage medium | |
| CN115328210B (en) | Path planning method, device, terminal equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |