CN113188547A - Unmanned aerial vehicle path planning method and device, controller and storage medium - Google Patents
Unmanned aerial vehicle path planning method and device, controller and storage medium Download PDFInfo
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Abstract
The application relates to a method, a device, a controller and a storage medium for planning a path of an unmanned aerial vehicle, wherein the method comprises the following steps: acquiring a signal coverage area of each base station in a task area; according to the signal coverage area of each base station, constructing a two-dimensional path planning space model of the unmanned aerial vehicle in the task area; obtaining an unmanned aerial vehicle preliminary planning path in a signal coverage area of a base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model; and obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm. The points on the optimal planned path are all in the signal coverage area of the base station, the communication stability of the unmanned aerial vehicle in the flight process is ensured, the flight reliability and safety are ensured, and the optimal planned path obtained by utilizing the ant colony algorithm can guide the unmanned aerial vehicle to safely fly from the starting point to the end point at the highest speed so as to complete the flight task. Have positive influence to unmanned aerial vehicle low latitude navigation efficiency.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle path planning method, an unmanned aerial vehicle path planning device, a controller and a storage medium.
Background
Along with the innovation and the development of unmanned aerial vehicle flight control technology, unmanned aerial vehicle shows high quality, low-cost advantage in many commercial activities, for example, goods are delivered, reconnaissance and monitoring in the air, these activities mainly exist in the middle of the urban environment, because the city stands when high-rise building stands, unmanned aerial vehicle must realize beyond visual range work when carrying out above-mentioned task, so cellular networking unmanned aerial vehicle has just become a fine choice, when specifically realizing, control its flight through inserting unmanned aerial vehicle into the internet, but the inventor discovers in the implementation process, unmanned aerial vehicle can be because lose contact with ground base station and can't distinguish instruction information in the task execution process, and then out of control, bring threat to resident's life and property safety.
Disclosure of Invention
Therefore, it is necessary to provide a method, an apparatus, a controller and a storage medium for planning a path of an unmanned aerial vehicle, which can avoid the loss of control of the unmanned aerial vehicle due to communication problems during the flight process according to the planned path, so as to improve the flight stability and safety of the unmanned aerial vehicle in urban environments, especially low altitude.
An embodiment of the present application provides an unmanned aerial vehicle path planning method, including:
acquiring a signal coverage area of each base station in a task area;
according to the signal coverage area of each base station, constructing a two-dimensional path planning space model of the unmanned aerial vehicle in the task area;
obtaining an unmanned aerial vehicle preliminary planning path in a signal coverage area of a base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model;
and obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm.
The unmanned aerial vehicle path planning method provided by the embodiment of the application fully considers the signal coverage condition of base stations in the unmanned aerial vehicle flight area, and constructs an unmanned aerial vehicle two-dimensional path planning space model of a height horizontal plane where the unmanned aerial vehicle is located according to the signal coverage area of each base station, firstly, according to the position information of a starting point and the position information of a destination point of the unmanned aerial vehicle, the shortest path of the unmanned aerial vehicle in the coverage area of the base stations in the two-dimensional path planning space needs to be searched, the shortest path is used as an unmanned aerial vehicle primary planning path, the points on the path are all in the signal coverage area of the base stations, good communication conditions can be ensured in the unmanned aerial vehicle flight process under the path, and the flight reliability and safety are ensured, on the basis, the unmanned aerial vehicle primary planning path is further optimized by utilizing an ant colony algorithm, the optimal planning path of the unmanned aerial vehicle is obtained, and the optimal planning path is used as a final unmanned aerial vehicle planning path, and guiding the unmanned aerial vehicle to safely fly from the starting point to the end point at the highest speed to finish the flight task.
In one embodiment, the step of acquiring the signal coverage area of each base station in the task area includes:
gridding the task area;
determining a signal transmission mode between each grid point and each base station according to the position relation between each grid point and each base station in the task area, wherein the signal transmission mode comprises line-of-sight transmission and non-line-of-sight transmission;
and determining the signal coverage area of each base station according to the signal transmission mode between each grid point and each base station and the farthest distance calculation model from the base station coverage point of the unmanned aerial vehicle in the two-dimensional plane to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle.
In one embodiment, the process of constructing the farthest distance calculation model from the coverage point of the base station in the two-dimensional plane where the drone is located to the projection of the base station in the two-dimensional plane where the drone is located includes:
obtaining a signal-to-noise ratio model of signals transmitted from each base station and received by the current unmanned aerial vehicle by using the signal power transmitted by each base station and the current position information of the unmanned aerial vehicle according to the following formula:
where ρ isk(v (t)) represents the signal-to-noise ratio of the signal transmitted from the kth base station and received by the unmanned aerial vehicle at present, P represents the signal power transmitted by the base station, v (t) represents the two-dimensional coordinate information of the unmanned aerial vehicle in the two-dimensional plane where the unmanned aerial vehicle is located, and gamma (t) represents the two-dimensional coordinate information of the unmanned aerial vehicle in the two-dimensional plane where the unmanned aerial vehicle is locatedk,s(t) represents the channel gain from the kth base station to the unmanned aerial vehicle channel, s belongs to { LoS, NLoS }, LoS represents line-of-sight propagation, NLoS represents non-line-of-sight propagation, and sigma represents non-line-of-sight propagation2Representing the noise power of the drone;
wherein, the channel gain calculation model from the kth base station to the unmanned aerial vehicle channel is as follows:
wherein d isk(t) is the distance, alpha, from the drone to the kth base stationsAnd betasTwo constant parameters which are dependent on the signal transmission mode between the base station and each base station;
the signal-to-noise ratio of the signals transmitted from each base station and received by the unmanned aerial vehicle is equal to the minimum receiving signal-to-noise ratio of the unmanned aerial vehicle
And combining the signal-to-noise ratio model and the channel gain calculation model to obtain a farthest distance calculation model from each base station coverage point in a two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located:
wherein d issThe maximum distance from each base station coverage point in the two-dimensional plane of the unmanned aerial vehicle to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle, h represents the height of the unmanned aerial vehiclegRepresenting the altitude of the base station.
In one embodiment, the step of determining the signal coverage area of each base station according to the signal transmission mode between each grid point and each base station and the farthest distance calculation model from the base station coverage point in the two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located includes:
determining grid point coordinates (x, y, h) in a signal coverage area of the base station according to the following formula and a farthest distance calculation model from a base station coverage point in a two-dimensional plane where the unmanned aerial vehicle is located to a projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located:
(x-xk)2+(y-yk)2≤ds 2
wherein (x)k,yk) And representing the projection coordinates of the base station on the two-dimensional plane of the height of the unmanned aerial vehicle.
In one embodiment, the step of constructing the two-dimensional path planning space model of the unmanned aerial vehicle in the task area according to the signal coverage area of each base station includes:
generating a plurality of MAKINK connecting lines in the task area based on the signal coverage area of each base station and the MAKINK graph theory method, and establishing an unmanned aerial vehicle two-dimensional path planning space model of the task area; the MAKLINK connecting line refers to a vertex connecting line which is not intersected with the area of the uncovered base station signal between the areas of the two uncovered base station signals and a connecting line which is intersected with the boundary of the task area by the vertex of the area of the uncovered base station signal.
In one embodiment, the step of obtaining a preliminary planned path of the drone located in a signal coverage area of a base station according to the start position information, the end position information, and the two-dimensional path planning space model of the drone includes:
and solving the two-dimensional path planning space model of the unmanned aerial vehicle by utilizing a Dijkstra algorithm, starting point position information and end point position information to obtain the shortest path from the starting point position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end point position of the unmanned aerial vehicle, wherein the primary planning path of the unmanned aerial vehicle is the shortest path from the starting point position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end point position of the unmanned aerial vehicle.
In one embodiment, the step of obtaining an optimal planned path of the unmanned aerial vehicle according to the preliminary planned path of the unmanned aerial vehicle and the ant colony algorithm includes:
initializing the number m of ants, the maximum iteration times, pheromones of all paths, a parameter alpha reflecting pheromone tracks of the ants in the activity process, a parameter beta reflecting the relative importance of visibility in ant selection paths and an attenuation coefficient rho of the pheromone tracks;
each ant selects the next connecting line L at the starting point position successively according to the following formulai+1Node j above until reaching the unmanned aerial vehicle end position:
wherein the primary planned path of the unmanned aerial vehicle passes through nodes S and P1,P2,…PdT; s represents a node of the starting position of the unmanned aerial vehicle in the unmanned aerial vehicle two-dimensional path planning space model, T represents a node of the destination position of the unmanned aerial vehicle in the unmanned aerial vehicle two-dimensional path planning space model, and P represents1,P2,…PdRepresenting the midpoint of each MAKLINK connecting line through which the primary planned path of the unmanned aerial vehicle passes; i denotes the next connecting line Li+1Set of all the above points, τikRepresenting intensity, η, of pheromones on the path (i, k)ik＝1/dikRepresenting visibility on path (i, k), dikDenotes the length of the path (i, k), q is [0,1 ]]Random number between q0Is [0,1 ]]Adjustable parameters therebetween; j denotes the last connecting line Li(i ═ 1,2, …, d) probability, τ, of selecting node j of the next connection lineijRepresenting intensity of pheromone, η, on path (i, j)ij＝1/dijRepresents a path (i)Visibility in j) dijDenotes the length of the path (i, j), τisRepresenting node i to the next connecting line Li+1Intensity of pheromones, η, on each node pathis＝1/disRepresenting node i to the next connecting line Li+1Visibility over each node path, disRepresenting node i to the next connecting line Li+1The length of each node path;
each ant updates the pheromone of each path that the ant passes according to the following formula according to the path that the ant passes by:
τij＝(1-ρ)τij+Δτij
wherein,
represents the pheromone quantity, delta tau, left on the path (i, j) by the kth ant in the current cycleijIndicates the increment of the pheromone quantity of the path (i, j) in the current cycle, LkThe path length of the kth ant in the cycle is shown, and Q is a set constant;
recording and updating the shortest paths traveled by all ants in the iteration to be global optimal paths;
if the iteration times are not more than the maximum iteration times after adding 1, skipping to execute the current connecting line LiSelecting the next connecting line L at the node i by each ant according to the following formulai+1The node j is added until the destination position of the unmanned aerial vehicle is reached;
and if the iteration times are added by 1 and then are larger than the maximum iteration times, outputting the updated global optimal path as the optimal planning path of the unmanned aerial vehicle.
On the other hand, this application embodiment still provides an unmanned aerial vehicle path planning device, and the device includes:
a base station coverage area acquisition module, configured to acquire a signal coverage area of each base station in a task area;
the two-dimensional path planning space construction module is used for constructing an unmanned aerial vehicle two-dimensional path planning space model of the task area according to the signal coverage area of each base station;
the primary path planning module is used for obtaining a primary planned path of the unmanned aerial vehicle in a signal coverage area of the base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model;
and the optimal path planning module is used for obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm.
A controller comprising a memory storing a computer program and a processor implementing the steps of the method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
Drawings
Fig. 1 is a schematic flow chart of a method for planning a path of an unmanned aerial vehicle according to an embodiment;
fig. 2 is a schematic flow chart of a method for planning a path of an unmanned aerial vehicle according to yet another embodiment;
FIG. 3 is a diagram of an environment within a task area, according to one embodiment;
fig. 4 is a flowchart illustrating a step of acquiring signal coverage areas of base stations in a task area according to an embodiment;
FIG. 5 is a diagram illustrating coverage of base station signals in a task area according to an embodiment;
FIG. 6 is a diagram illustrating task area partitioning, according to one embodiment;
fig. 7 is a schematic diagram of a two-dimensional path planning space model of an unmanned aerial vehicle of the MAKLINK line and the task area in one embodiment;
FIG. 8 is a net undirected graph of paths from start to end through the midpoint of the MAKLINK line within the task area in one embodiment;
fig. 9 is a schematic diagram illustrating a step of solving the two-dimensional path planning space model of the unmanned aerial vehicle by using Dijkstra algorithm, start point position information, and end point position information to obtain a shortest path from the start point position of the unmanned aerial vehicle to a midpoint of each MAKLINK connection line and an end point position of the unmanned aerial vehicle in one embodiment;
fig. 10 is a schematic diagram of a preliminary planned path of the unmanned aerial vehicle in the mission area shown in fig. 3 according to an embodiment;
FIG. 11 is a diagram illustrating the partitioning of links found using Dijkstra's algorithm, according to one embodiment;
fig. 12 is a schematic flow chart illustrating a step of obtaining an optimal planned path of the unmanned aerial vehicle according to the preliminary planned path of the unmanned aerial vehicle and the ant colony algorithm in one embodiment;
fig. 13 is a schematic diagram of an optimal planned path of the unmanned aerial vehicle in the task area shown in fig. 3;
fig. 14 is a schematic structural diagram of the unmanned aerial vehicle path planning apparatus in one embodiment;
FIG. 15 is an internal block diagram of a controller in accordance with one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
As described in the background, the implementation of drone flight control in low-altitude cities often requires over-the-horizon operation through networking with cellular networks. When controlling its flight through accessing unmanned aerial vehicle into the internet, require unmanned aerial vehicle to keep constantly in the in-process of carrying out the task and contact between the ground basic station, the signal to noise ratio of the signal that a certain base station in ground that unmanned aerial vehicle received promptly needs to be greater than its resolution ratio, otherwise unmanned aerial vehicle just can probably lead to out of control because can't distinguish instruction information, and then brings the threat for resident's life and property safety. Meanwhile, in order to execute the task with the lowest cost, after the starting point and the end point of the task are known, a shortest path meeting the conditions needs to be planned for the unmanned aerial vehicle, and therefore the task can be completed safely and efficiently.
Based on this, an aspect of the present embodiment provides an unmanned aerial vehicle path planning method, as shown in fig. 1, the method includes:
s200: and acquiring the signal coverage area of each base station in the task area.
The mission area refers to an area where the unmanned aerial vehicle performs a flight mission, and may be, for example, a rectangular area formed by a start point and an end point where the unmanned aerial vehicle performs the flight mission, and the area may include a plurality of base stations and other objects, such as buildings. The signal-to-noise ratio of the signal transmitted by the base station and received by the unmanned aerial vehicle in the signal coverage area of the base station is greater than the resolution ratio of the signal, namely the area is an area range capable of ensuring normal communication of the unmanned aerial vehicle.
S400: and constructing a two-dimensional path planning space model of the unmanned aerial vehicle in the task area according to the signal coverage area of each base station. The unmanned aerial vehicle two-dimensional path planning space model is a space model capable of representing the coverage condition of base station signals in the horizontal plane of the height of the unmanned aerial vehicle.
S600: and obtaining a preliminary planned path of the unmanned aerial vehicle in a signal coverage area of the base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model.
The starting point position information refers to position information of a given starting point when the unmanned aerial vehicle executes a task, and may be coordinate data of the starting point in a world coordinate system, for example. Similarly, the destination position information refers to position information of a flight destination given when the unmanned aerial vehicle performs a task, and the position information may be coordinate data of the destination in a world coordinate system. The primary planned path of the unmanned aerial vehicle is the shortest flight path obtained by calculation in a two-dimensional path planning space of the unmanned aerial vehicle.
S800: and obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm. The optimal planned path is that on the basis of the primary planned path of the unmanned aerial vehicle, points in a coverage area of a base station around the points where the primary planned path of the unmanned aerial vehicle passes are found, and the shortest flight path found from the points is utilized by using an ant colony algorithm, namely, the primary planned path of the unmanned aerial vehicle is optimized, so that the unmanned aerial vehicle flies according to the optimal planned path, the flight time is shortest, and the communication is stable and safe in the flight process.
The unmanned aerial vehicle path planning method provided by the embodiment of the application fully considers the signal coverage condition of base stations in the unmanned aerial vehicle flight area, and constructs an unmanned aerial vehicle two-dimensional path planning space model of a height horizontal plane where the unmanned aerial vehicle is located according to the signal coverage area of each base station, firstly, according to the position information of a starting point and the position information of a destination point of the unmanned aerial vehicle, the shortest path of the unmanned aerial vehicle in the coverage area of the base stations in the two-dimensional path planning space needs to be searched, the shortest path is used as an unmanned aerial vehicle primary planning path, the points on the path are all in the signal coverage area of the base stations, good communication conditions can be ensured in the unmanned aerial vehicle flight process under the path, and the flight reliability and safety are ensured, on the basis, the unmanned aerial vehicle primary planning path is further optimized by utilizing an ant colony algorithm, the, and guiding the unmanned aerial vehicle to safely fly from the starting point to the end point at the highest speed to finish the flight task.
In addition, the traditional path planning algorithm comprises a global planning algorithm and a local planning algorithm, the global planning algorithm is a vertex image method, a grid division method and the local planning algorithm is mainly an artificial potential field method, and for simple scenes, when the transmission mode of electromagnetic waves between the unmanned aerial vehicle and the base station is unified and simplified into line-of-sight transmission, the method can obtain a good solution. However, the actual situation of the unmanned aerial vehicle in the flying area is far from the same, because of the standing of the urban environment high buildings and the shadow effect in the electromagnetic wave propagation, the direct simplification is that the sight distance propagation is unscientific and has no practical significance, after various electromagnetic wave propagation modes are considered, the path planning of the unmanned aerial vehicle is very complicated, and a good result is difficult to obtain by the traditional algorithm. However, with the improvement and the proposal of the bionic algorithm, the idea of solving the optimization problem by using the intelligence of the natural creatures enters the field of view of the public, and the ant colony algorithm is one of the representatives. Considering that the ant colony algorithm has better global optimization and complex problem solving capabilities, the unmanned aerial vehicle path planning method provided by the embodiment of the application optimizes the path by using the ant colony algorithm to obtain the optimal planned path, and can better solve the practical problem of unmanned aerial vehicle path planning in urban environment.
In one embodiment, as shown in fig. 2, the step S200 of acquiring signal coverage areas of base stations in the task area includes:
s220: gridding the task area.
S240: and determining signal transmission modes between each grid point and each base station according to the position relation between each grid point and each base station in the task area, wherein the signal transmission modes comprise line-of-sight transmission and non-line-of-sight transmission. In urban environment, high buildings stand in urban environment, the transmission of electromagnetic waves has shadow effect, so that the direct simplification to the line-of-sight transmission is not scientific, and the signal transmission mode between each grid point and the base station can be determined to be a transmission realization mode or non-line-of-sight transmission mode according to the building condition between the grid point and the base station based on the position relation between the grid point and the base station. Wherein, the sight distance propagation means that the electromagnetic ray propagates along a straight line. The non-line-of-sight propagation mode refers to indirect point-to-point communication between the unmanned aerial vehicle and the base station.
S260: and determining the signal coverage area of each base station according to the signal transmission mode between each grid point and each base station and the farthest distance calculation model from the base station coverage point of the unmanned aerial vehicle in the two-dimensional plane to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle. The model for calculating the farthest distance from the coverage point of the base station in the two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located is a model which can meet the minimum resolution (namely, the allowable minimum signal-to-noise ratio) of the unmanned aerial vehicle when the unmanned aerial vehicle is communicated with the base station at each grid point.
Specifically, a plurality of grid points in a task area are obtained through gridding, according to the position relation between the grid points and each base station, whether the signal transmission mode between each unmanned aerial vehicle and the base station at each grid point is line-of-sight transmission or non-line-of-sight transmission can be determined, under different transmission modes, the signal strength of the unmanned aerial vehicle when each grid point is communicated with the base station can be obtained, then the farthest grid point which can meet the minimum resolution (namely the allowable minimum signal-to-noise ratio) of the unmanned aerial vehicle when each grid point is communicated with the base station is calculated by using the farthest distance determination model from the base station coverage point of the unmanned aerial vehicle in the two-dimensional plane to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle, and the area formed by the grid points which are closer to the base station than the farthest grid point is the signal coverage area of the base station. The signal coverage area obtained by the method fully considers the condition that electromagnetic wave propagation between the base station and the unmanned aerial vehicle is not line-of-sight propagation when the unmanned aerial vehicle communicates with the base station at certain positions due to standing of a high building in an urban environment, so that the communication between the unmanned aerial vehicle and the base station is more stable in the finally determined base station signal coverage area, and the safety of the unmanned aerial vehicle in the flying process is further improved.
In one embodiment, determining the signal transmission mode of the drone between each mesh point and each base station may be determined by: a line of sight between a location and a base station is considered line of sight propagation when it is above the height of any building between the two, and non-line of sight propagation otherwise.
In one embodiment, the process of constructing the farthest distance calculation model from the coverage point of the base station in the two-dimensional plane where the drone is located to the projection of the base station in the two-dimensional plane where the drone is located includes:
obtaining a signal-to-noise ratio model of signals transmitted from each base station and received by the current unmanned aerial vehicle by using the signal power transmitted by each base station and the current position information of the unmanned aerial vehicle according to the following formula:
where ρ isk(v (t)) represents the signal-to-noise ratio of the signal transmitted from the kth base station received by the unmanned aerial vehicle currently, P represents the signal power transmitted by the base station, and v (t) represents the two-dimensional seat of the unmanned aerial vehicle in the two-dimensional plane where the unmanned aerial vehicle is locatedSubject information, γk,s(t) represents the channel gain from the kth base station to the unmanned aerial vehicle channel, s belongs to { LoS, NLoS }, LoS represents line-of-sight propagation, NLoS represents non-line-of-sight propagation, and sigma represents non-line-of-sight propagation2Representing the noise power of the drone;
wherein, the channel gain calculation model from the kth base station to the unmanned aerial vehicle channel is as follows:
wherein d isk(t) is the distance, alpha, from the drone to the kth base stationsAnd betasTwo constant parameters which are dependent on the signal transmission mode between the base station and each base station;
the signal-to-noise ratio of the signals transmitted from each base station and received by the unmanned aerial vehicle is equal to the minimum receiving signal-to-noise ratio of the unmanned aerial vehicle
And combining the signal-to-noise ratio model and the channel gain calculation model to obtain a farthest distance calculation model from each base station coverage point in a two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located:
wherein d issThe maximum distance from each base station coverage point in the two-dimensional plane of the unmanned aerial vehicle to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle, h represents the height of the unmanned aerial vehiclegRepresenting the altitude of the base station.
To better explain the implementation process of the unmanned aerial vehicle path planning method provided by the embodiment of the application, the number is (mxn) km2The starting point of the drone is one vertex of the task area, and the task end point of the drone is the other vertex of the diagonal line. A plurality of base stations with certain transmitting power and height and buildings with random positions and with certain heights and Rayleigh distribution are randomly distributed in the task area, and a shortest path is planned for the unmanned aerial vehicleThe unmanned aerial vehicle can complete the task and simultaneously ensure that the unmanned aerial vehicle keeps contact with a certain base station on the ground all the time. The schematic diagram of the environment in the task area is shown in fig. 3, in which the internal rectangular box represents a building, different gray scales represent different heights, the five-pointed star represents a starting point and an end point, and the four-pointed star represents a base station.
In the task area, the total time for the unmanned aerial vehicle to execute a certain task is T, and the time T belongs to [0, T ∈ [0 ]]V (t) ═ x (t), y (t), h]TRepresenting the unmanned aerial vehicle's position, h represents unmanned aerial vehicle flying height, and this value is a constant, and in order to avoid colliding, its size can be dependent on the height of the highest building in the city, and unmanned aerial vehicle can be furnished with the device that has the locate function such as GPS simultaneously, can acquire unmanned aerial vehicle current position v (t).
If the position of the unmanned aerial vehicle at the moment 0 is set as vIThe end position is vFIt flies at a constant speed. Throughout the course of the mission performed by the drone, it must be in contact with one of K base stations distributed randomly on the ground. Let the k base station position be uk＝[xk,yk,hg]T,k∈[1,K]，hgIndicating the base station altitude and assuming all base station altitudes are the same. At the same time, is provided with
k∈[1,K]The projection position of the base station in the two-dimensional horizontal plane of the height of the unmanned aerial vehicle is represented.
Since the purpose of the path planning is to find the unmanned aerial vehicle from the departure point vITo the end point vFThe shortest path(s) of (1) can be converted into the shortest time for the unmanned aerial vehicle to execute the task due to the constant speed of the unmanned aerial vehicle, and meanwhile, the signal-to-noise ratio (SNR) (rho) of the signal received by the unmanned aerial vehicle is required to be met in the whole pathk(v (t)) is not less than the minimum received signal-to-noise ratio of the unmanned aerial vehicle
Therefore, a single-objective optimization model is established as follows:
since the optimization problem is difficult to solve directly, and we note that satisfying the constraint condition in the optimization model indicates that the flight path of the drone is within the coverage area of the base station, the problem can be transformed to find a shortest path from the starting point to the end point within the coverage area of the base station.
Considering the downlink, if the power of the signal transmitted by the base station is P, the signal to noise ratio of the signal transmitted by the kth base station and received by the drone at the position v (t) is:
channel gain γ for the k base station to drone channelk,s(t) the calculation method is as follows:
defining the coverage area of each base station as a series of points consistent with the flight height of the unmanned aerial vehicle, wherein the signal-to-noise ratio of base station transmitting signals received by the unmanned aerial vehicle at the points is not less than the resolution ratio of the unmanned aerial vehicle
Can meet the requirement of communication stability, so that the kth base station (K epsilon [1, K ]) can be obtained]) The signal coverage area of (a) is:
the coverage area of the base station can be obtained by obtaining the boundary of the coverage area of the base station, and the point on the boundary of the signal coverage area of the base station satisfies the following conditions:
obtaining the distance d from the unmanned aerial vehicle to the kth base station through the united type (3), (4) and (6)k(t)：
So in the two-dimensional plane of the height of the unmanned aerial vehicle, the farthest distance d from the point which can be covered by the base station to the base stationsThe square of (d) is:
the maximum distance calculation model is used as a maximum distance calculation model from each base station coverage point in the two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located, and is used for further determining the coverage point in the boundary range to obtain the signal coverage area of the base station.
In one embodiment, as shown in fig. 4, the step S260 of determining the signal coverage area of each base station according to the signal transmission mode between each grid point and each base station and the farthest distance calculation model from the base station coverage point in the two-dimensional plane where the drone is located to the projection of the base station in the two-dimensional plane where the drone is located includes:
determining grid point coordinates (x, y, h) in a signal coverage area of the base station according to the following formula and a farthest distance calculation model from a base station coverage point in a two-dimensional plane where the unmanned aerial vehicle is located to a projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located:
(x-xk)2+(y-yk)2≤ds 2 (9)
wherein (x)k,yk) Represents the aboveAnd the base station projects coordinates on a two-dimensional plane at the height of the unmanned aerial vehicle. According to the above steps of determining the coverage area of the base station, we obtain the coverage condition of the base station of the task area shown in fig. 3 as shown in fig. 5 (because the coverage area is wider, the uncovered area is marked for inconvenient indication).
In practical situations, the unmanned aerial vehicle can plan the path of the unmanned aerial vehicle by determining points through which the unmanned aerial vehicle passes, so that after the coverage area of each base station is determined, the trajectory of the unmanned aerial vehicle can be discretized and set
Representing a series of points on the unmanned aerial vehicle track, wherein the path traveled by the unmanned aerial vehicle between two adjacent points is a straight line, and then the original optimization problems (1) and (2) can be converted into the following optimization conditions:
wherein L (v)n,vn+1) Denotes vn,vn+1The line between the two points.
In one embodiment, the step of constructing the two-dimensional path planning space model of the unmanned aerial vehicle in the task area according to the signal coverage area of each base station includes:
generating a plurality of MAKINK connecting lines in the task area based on the signal coverage area (such as the area outside the polygon in FIG. 6) of each base station and the MAKINK graph theory method, and establishing an unmanned aerial vehicle two-dimensional path planning space model of the task area; the MAKLINK connecting line refers to a vertex connecting line which is not intersected with the area of the uncovered base station signal between the areas of the two uncovered base station signals and a connecting line which is intersected with the boundary of the task area by the vertex of the area of the uncovered base station signal.
Discretizing a signal coverage area of the base station by using an MAKINK graph theory method to obtain a plurality of MAKINK connecting lines, constructing a two-dimensional path planning space model of the unmanned aerial vehicle, and further solving the problem of the shortest path in the above equations (10) and (11).
In one embodiment, the step of obtaining a preliminary planned path of the drone located in a signal coverage area of a base station according to the start position information, the end position information, and the two-dimensional path planning space model of the drone includes:
and solving the two-dimensional path planning space model of the unmanned aerial vehicle by utilizing a Dijkstra algorithm, starting point position information and end point position information to obtain the shortest path from the starting point position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end point position of the unmanned aerial vehicle, wherein the primary planning path of the unmanned aerial vehicle is the shortest path from the starting point position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end point position of the unmanned aerial vehicle.
After the coverage area of the base station is determined, when the starting point S and the end point T of the unmanned aerial vehicle are known, a shortest driving path of the unmanned aerial vehicle is found in the task area, the path cannot pass through a solving path of an uncovered area (a polygonal inner area in fig. 6) of the base station, a primary planned path of the unmanned aerial vehicle is firstly solved by utilizing a Dijkstra algorithm after a two-dimensional path planning space model (shown in fig. 7) of the unmanned aerial vehicle is constructed by utilizing a MAKLINK graph theory, and an optimal planned path is solved by utilizing an ant colony algorithm on the basis, so that the path planning efficiency is improved.
On the MAKLINK diagram, L free connecting lines exist, and the positions of the midpoints of the connecting lines are v in sequence1,v2,…,vLThe midpoint of adjacent MAKLINK lines is connected with a start point S and an end point T to form a non-directional network diagram for initial path planning, as shown in fig. 8, after the connection is completed, a midpoint connection matrix is obtained, the dimensionality is (L +2) × (L +2), any two midpoints, the start point S and the end point T are connected to be 1, otherwise, the two midpoints are connected to be 0, and thus a solution space for the two-dimensional path planning of the unmanned aerial vehicle is obtained.
In practical situations, the optimal path may pass through any one connection line and may pass through any position of the connection line, but the discretization solving complexity of all the connection lines is high, so that the connection line through which the path passes is determined by using a Dijkstra algorithm, the obtained connection line is subdivided, the optimal solution is obtained by using an ant colony algorithm, and the planning accuracy of the shortest flight path can be found while the planning efficiency is ensured.
The basic idea of Dijkstra' S algorithm is to divide all nodes in the weighted graph into two groups, the first group S being nodes for which the shortest path has been determined, and the second group U being nodes for which the shortest path has not been determined. The nodes of the second group are added to the first group one by one in increasing order of shortest path until all nodes reachable from the source point are included in the first group. Based on the above ideas, the Dijkstra algorithm, the starting point position information and the end point position information are used for solving the two-dimensional path planning space model of the unmanned aerial vehicle (i.e. solving the connecting lines through which the paths pass), and the steps of obtaining the shortest paths from the starting point position of the unmanned aerial vehicle to the middle points of the MAKLINK connecting lines and the end point position of the unmanned aerial vehicle are shown in fig. 9:
initializing a node set V of undetermined shortest paths and a node set S of determined shortest paths;
calculating the distance from the starting point to each point by the current starting point position, the current end point position and the midpoint position of each MAKLINK connecting line of the pattern;
if the midpoint of the connecting line is directly connected with the starting point, the shortest path D between the midpoint and the starting point is obtainedij＝dijIf the end point of the connecting line is not directly connected with the starting point, the shortest path D between the end point and the starting point is obtainedij＝∞；
Taking out the node i meeting the i fine (D min (D)) from the set V and putting the node i into the set S;
updating the path length from the starting point in the path D to each point in the set V according to the node i;
if set
Determining the shortest path from the starting position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end position of the unmanned aerial vehicle according to the node i in the set S;
if set
That is, all points are not traversed, the jump executes the step of taking the node i meeting i find (D min (D)) out of the set V and putting the node i into the set S until all points are traversed.
The shortest path from the starting point to the midpoint and the end point of each connecting line is obtained by the above algorithm, a schematic diagram of the shortest path from the starting point to the end point under a possible condition is shown in fig. 10, the shortest path from the starting point to the end point at this time is a suboptimal solution, because the real unmanned aerial vehicle path can pass through any position of the connecting line, the suboptimal solution (unmanned aerial vehicle primary planning path) is only the connecting line through which the optimal path of the unmanned aerial vehicle passes, and therefore, the optimal solution needs to be obtained by using the ant colony algorithm on the basis of the suboptimal solution.
Utilizing dijkstra algorithm to generate successive nodes S and P on two-dimensional path planning space model (namely, MAKLINK graph) of unmanned aerial vehicle1,P2,…PdAnd T, primarily planning a path by the unmanned aerial vehicle. The connecting lines corresponding to the nodes are respectively Li(i ═ 1,2, …, d), adopt the ant colony algorithm to need the discretization workspace, consider that the length of every connecting wire is different, adopt the fixed distance method to divide the connecting wire, set up to divide length and be δ, then the division figure of every connecting wire is:
in the formula
Denotes rounding up, |iRepresents the connecting line LiLength of (d).
Dividing each connecting line into piiAfter being equally divided, the connecting line Li-1To the connecting line LiHas (pi)i+1) paths. Is provided with
Respectively representConnecting line LiThe two end points of (a) are,
respectively represent connecting lines LiTwo endpoint coordinates. Then L will beiIs divided into piiAfter portioning, n thi(i ═ 1,2, …, d) piiThe coordinates of the bisector points are:
based on the above analysis, a schematic diagram of the connection lines found by the Dijkstra algorithm after being divided is shown in fig. 11. It can thus be seen that a given set of niThe value is that the unmanned aerial vehicle path can be known at which point of each connecting line, and a path from the starting point to the end point can be obtained, so that the optimal solution obtained by the ant colony algorithm search can be expressed as (n)1,n2,…,nd)。
Specifically, in one embodiment, as shown in fig. 12, the step of obtaining the optimal planned path of the unmanned aerial vehicle according to the preliminary planned path of the unmanned aerial vehicle and the ant colony algorithm includes:
initializing the number m of ants, the maximum iteration times, pheromones of all paths, a parameter alpha reflecting pheromone tracks of the ants in the activity process, a parameter beta reflecting the relative importance of visibility in ant selection paths and an attenuation coefficient rho of the pheromone tracks;
each ant selects the next connecting line L at the starting position S successively according to the following formulai+1Node j above until reaching the unmanned aerial vehicle end position T:
wherein I represents the next connecting line Li+1Set of all the above points, τikRepresenting intensity, η, of pheromones on the path (i, k)ik＝1/dikRepresenting visibility on path (i, k), dikDenotes the length of the path (i, k), q is [0,1 ]]Random number between q0Is [0,1 ]]Adjustable parameters therebetween; j denotes the last connecting line Li(i ═ 1,2, …, d) probability, τ, of selecting node j of the next connection lineijRepresenting intensity of pheromone, η, on path (i, j)ij＝1/dijRepresenting visibility on path (i, j), dijDenotes the length of the path (i, j), τisRepresenting node i to the next connecting line Li+1Intensity of pheromones, η, on each node pathis＝1/disRepresenting node i to the next connecting line Li+1Visibility over each node path, disRepresenting node i to the next connecting line Li+1The length of each node path;
each ant updates the pheromone of each path that the ant passes according to the following formula according to the path that the ant passes by:
τij＝(1-ρ)τij+Δτij (17)
to update pheromones with global information, a periant system calculation is employed
Wherein,
the pheromone quantity of the kth ant left on the path (i, j) in the current cycle is shown, the value of the pheromone quantity is determined according to the quality degree of the ant, and the shorter the path is, the more pheromones are released; delta tauijIndicates the increment of the pheromone quantity of the path (i, j) in the current cycle, LkThe path length of the kth ant in the cycle is shown, and Q is a set constant;
recording and updating the shortest paths traveled by all ants in the iteration to be global optimal paths;
if the iteration times are not more than the maximum iteration times after adding 1, skipping to execute the current connecting line LiSelecting the next connecting line L at the node i by each ant according to the following formulai+1The node j is added until the destination position of the unmanned aerial vehicle is reached;
and if the iteration times are added by 1 and then are larger than the maximum iteration times, outputting the updated global optimal path as the optimal planning path of the unmanned aerial vehicle.
A possible optimal path obtained by using the above algorithm is shown in fig. 13, a dotted line is a suboptimal solution (primary planned path of the unmanned aerial vehicle) found by Dijkstra algorithm, and a solid line is an optimal planned path found by the ant colony algorithm on the basis of the suboptimal solution, so that the problem of path planning of the cellular networked unmanned aerial vehicle in the urban environment is better solved by using the ant colony algorithm.
Where the attenuation coefficient p < 1 of the pheromone track is typically set to avoid infinite accumulation of pheromones on the path.
It should be understood that although the various steps in the flowcharts of fig. 1,2, 4, 9, 12 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1,2, 4, 9, and 12 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least some of the other steps or stages.
On the other hand, this application embodiment still provides an unmanned aerial vehicle path planning device, as shown in fig. 14, the device includes:
a base station coverage area obtaining module 200, configured to obtain a signal coverage area of each base station in a task area;
a two-dimensional path planning space construction module 400, configured to construct a two-dimensional path planning space model of the unmanned aerial vehicle in the task area according to the signal coverage area of each base station;
a preliminary path planning module 600, configured to obtain a preliminary planned path of the unmanned aerial vehicle located in a signal coverage area of the base station according to the start point position information, the end point position information, and the two-dimensional path planning space model of the unmanned aerial vehicle;
and an optimal path planning module 800, configured to obtain an optimal planned path of the unmanned aerial vehicle according to the preliminary planned path of the unmanned aerial vehicle and the ant colony algorithm.
For specific definition of the unmanned aerial vehicle path planning device, reference may be made to the above definition of the unmanned aerial vehicle path planning method, which is not described herein again. All modules in the unmanned aerial vehicle path planning device can be completely or partially realized through software, hardware and a combination of the software and the hardware. The modules can be embedded in a hardware form or independent from a processor in the controller, and can also be stored in a memory in the controller in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a controller is provided, which may be a server, and the internal structure thereof may be as shown in fig. 15. The controller includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the controller is configured to provide computational and control capabilities. The memory of the controller comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the controller is used for storing data such as the maximum iteration number. The network interface of the controller is used for communicating with an external terminal through network connection. The computer program is executed by a processor to implement a method of unmanned aerial vehicle path planning.
Those skilled in the art will appreciate that the configuration shown in fig. 15 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation on the controller to which the present application is applied, and that a particular controller may include more or less components than those shown, or combine certain components, or have a different arrangement of components.
In one embodiment, a controller is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
s200: acquiring a signal coverage area of each base station in a task area;
s400: according to the signal coverage area of each base station, constructing a two-dimensional path planning space model of the unmanned aerial vehicle in the task area;
s600: obtaining an unmanned aerial vehicle preliminary planning path in a signal coverage area of a base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model;
s800: and obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm.
According to the controller provided by the embodiment of the application, the processor of the controller can also realize other steps of the unmanned aerial vehicle path planning method when executing a computer program, and corresponding beneficial effects are achieved.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
s200: acquiring a signal coverage area of each base station in a task area;
s400: according to the signal coverage area of each base station, constructing a two-dimensional path planning space model of the unmanned aerial vehicle in the task area;
s600: obtaining an unmanned aerial vehicle preliminary planning path in a signal coverage area of a base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model;
s800: and obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not 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 concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An unmanned aerial vehicle path planning method, the method comprising:
acquiring a signal coverage area of each base station in a task area;
according to the signal coverage area of each base station, constructing a two-dimensional path planning space model of the unmanned aerial vehicle in the task area;
obtaining an unmanned aerial vehicle preliminary planning path in a signal coverage area of a base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model;
and obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm.
2. The method of claim 1, wherein the step of obtaining signal coverage areas of base stations in the task area comprises:
gridding the task area;
determining a signal transmission mode between each grid point and each base station according to the position relation between each grid point and each base station in the task area, wherein the signal transmission mode comprises line-of-sight transmission and non-line-of-sight transmission;
and determining the signal coverage area of each base station according to the signal transmission mode between each grid point and each base station and the farthest distance calculation model from the base station coverage point of the unmanned aerial vehicle in the two-dimensional plane to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle.
3. The method of claim 2, wherein the building process of the farthest distance calculation model from the coverage point of the base station in the two-dimensional plane of the unmanned aerial vehicle to the projection of the base station in the two-dimensional plane of the unmanned aerial vehicle comprises:
obtaining a signal-to-noise ratio model of signals transmitted from each base station and received by the current unmanned aerial vehicle by using the signal power transmitted by each base station and the current position information of the unmanned aerial vehicle according to the following formula:
where ρ isk(v (t)) represents the signal-to-noise ratio of the signal transmitted from the kth base station and received by the unmanned aerial vehicle at present, P represents the signal power transmitted by the base station, v (t) represents the two-dimensional coordinate information of the unmanned aerial vehicle in the two-dimensional plane where the unmanned aerial vehicle is located, and gamma (t) represents the two-dimensional coordinate information of the unmanned aerial vehicle in the two-dimensional plane where the unmanned aerial vehicle is locatedk,s(t) represents the channel gain from the kth base station to the unmanned aerial vehicle channel, s belongs to { LoS, NLoS }, LoS represents line-of-sight propagation, NLoS represents non-line-of-sight propagation, and sigma represents non-line-of-sight propagation2Representing the noise power of the drone;
wherein, the channel gain calculation model from the kth base station to the unmanned aerial vehicle channel is as follows:
wherein d isk(t) is the distance, alpha, from the drone to the kth base stationsAnd betasTwo constant parameters which are dependent on the signal transmission mode between the base station and each base station;
the signal-to-noise ratio of the signals transmitted from each base station and received by the unmanned aerial vehicle is equal to the minimum receiving signal-to-noise ratio of the unmanned aerial vehicle
And combining the signal-to-noise ratio model and the channel gain calculation model to obtain a farthest distance calculation model from each base station coverage point in a two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located:
wherein d issCovering points of all base stations in a two-dimensional plane of the unmanned aerial vehicle to the base stationsThe farthest distance of the projection of the unmanned plane in the two-dimensional plane, h represents the height of the unmanned plane, and h represents the height of the unmanned planegRepresenting the altitude of the base station.
4. The method of claim 3, wherein the step of determining the signal coverage area of each base station according to the signal transmission mode between each grid point and each base station and the farthest distance calculation model from the base station coverage point of the two-dimensional plane where the unmanned aerial vehicle is located to the projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located comprises:
determining grid point coordinates (x, y, h) in a signal coverage area of the base station according to the following formula and a farthest distance calculation model from a base station coverage point in a two-dimensional plane where the unmanned aerial vehicle is located to a projection of the base station in the two-dimensional plane where the unmanned aerial vehicle is located:
(x-xk)2+(y-yk)2≤ds 2
wherein (x)k,yk) And representing the projection coordinates of the base station on the two-dimensional plane of the height of the unmanned aerial vehicle.
5. The method according to any one of claims 1-4, wherein the step of constructing the two-dimensional path planning space model of the unmanned aerial vehicle for the mission area according to the signal coverage area of each base station comprises:
generating a plurality of MAKINK connecting lines in the task area based on the signal coverage area of each base station and the MAKINK graph theory method, and establishing an unmanned aerial vehicle two-dimensional path planning space model of the task area; the MAKLINK connecting line refers to a vertex connecting line which is not intersected with the area of the uncovered base station signal between the areas of the two uncovered base station signals and a connecting line which is intersected with the boundary of the task area by the vertex of the area of the uncovered base station signal.
6. The method of claim 5, wherein the step of obtaining a preliminary planned path of the UAV within a signal coverage area of a base station according to the starting location information, the ending location information and the two-dimensional path planning space model of the UAV comprises:
and solving the two-dimensional path planning space model of the unmanned aerial vehicle by utilizing a Dijkstra algorithm, starting point position information and end point position information to obtain the shortest path from the starting point position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end point position of the unmanned aerial vehicle, wherein the primary planning path of the unmanned aerial vehicle is the shortest path from the starting point position of the unmanned aerial vehicle to the midpoint of each MAKINK connecting line and the end point position of the unmanned aerial vehicle.
7. The method of claim 6, wherein the step of obtaining the optimal planned path of the UAV according to the primary planned path of the UAV and the ant colony algorithm comprises:
initializing the number m of ants, the maximum iteration times, pheromones of all paths, a parameter alpha reflecting pheromone tracks of the ants in the activity process, a parameter beta reflecting the relative importance of visibility in ant selection paths and an attenuation coefficient rho of the pheromone tracks;
each ant selects the next connecting line L at the starting point position successively according to the following formulai+1Node j above until reaching the unmanned aerial vehicle end position:
wherein the primary planned path of the unmanned aerial vehicle passes through nodes S and P1,P2,…PdT; s represents a node of the starting position of the unmanned aerial vehicle in the unmanned aerial vehicle two-dimensional path planning space model, T represents a node of the destination position of the unmanned aerial vehicle in the unmanned aerial vehicle two-dimensional path planning space model, and P represents1,P2,…PdRepresenting the midpoint of each MAKLINK connecting line through which the primary planned path of the unmanned aerial vehicle passes; i denotes the next connecting line Li+1All aboveSet of points, τikRepresenting intensity, η, of pheromones on the path (i, k)ik＝1/dikRepresenting visibility on path (i, k), dikDenotes the length of the path (i, k), q is [0,1 ]]Random number between q0Is [0,1 ]]Adjustable parameters therebetween; j denotes the last connecting line Li(i ═ 1,2, …, d) probability, τ, of selecting node j of the next connection lineijRepresenting intensity of pheromone, η, on path (i, j)ij＝1/dijRepresenting visibility on path (i, j), dijDenotes the length of the path (i, j), τisRepresenting node i to the next connecting line Li+1Intensity of pheromones, η, on each node pathis＝1/disRepresenting node i to the next connecting line Li+1Visibility over each node path, disRepresenting node i to the next connecting line Li+1The length of each node path;
each ant updates the pheromone of each path that the ant passes according to the following formula according to the path that the ant passes by:
τij＝(1-ρ)τij+Δτij
wherein,
represents the pheromone quantity, delta tau, left on the path (i, j) by the kth ant in the current cycleijIndicates the increment of the pheromone quantity of the path (i, j) in the current cycle, LkThe path length of the kth ant in the cycle is shown, and Q is a set constant;
recording and updating the shortest paths traveled by all ants in the iteration to be global optimal paths;
if the iteration times are not more than the maximum iteration times after adding 1, skipping to execute the current connecting line LiSelecting the next connecting line L at the node i by each ant according to the following formulai+1The node j is added until the destination position of the unmanned aerial vehicle is reached;
and if the iteration times are added by 1 and then are larger than the maximum iteration times, outputting the updated global optimal path as the optimal planning path of the unmanned aerial vehicle.
8. An unmanned aerial vehicle path planning apparatus, the apparatus comprising:
a base station coverage area acquisition module, configured to acquire a signal coverage area of each base station in a task area;
the two-dimensional path planning space construction module is used for constructing an unmanned aerial vehicle two-dimensional path planning space model of the task area according to the signal coverage area of each base station;
the primary path planning module is used for obtaining a primary planned path of the unmanned aerial vehicle in a signal coverage area of the base station according to the starting point position information, the end point position information and the unmanned aerial vehicle two-dimensional path planning space model;
and the optimal path planning module is used for obtaining an optimal planned path of the unmanned aerial vehicle according to the primary planned path of the unmanned aerial vehicle and the ant colony algorithm.
9. A controller comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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