CN114460968A

CN114460968A - Unmanned aerial vehicle path searching method and device, electronic equipment and storage medium

Info

Publication number: CN114460968A
Application number: CN202210131596.3A
Authority: CN
Inventors: 樊宽刚; 黄泰
Original assignee: Jiangxi University of Science and Technology
Current assignee: Jiangxi University of Science and Technology
Priority date: 2022-02-14
Filing date: 2022-02-14
Publication date: 2022-05-10

Abstract

The application provides a method and a device for searching an unmanned aerial vehicle path, electronic equipment and a storage medium, wherein the searching method comprises the following steps: acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; performing path searching according to the starting point and the end point of the flight path and on two sides of the obstacle to determine a plurality of turning points of the flight path; determining a raster path of the unmanned aerial vehicle in the raster map based on the turning points; and optimizing the raster path, and constructing a target flight path of the unmanned aerial vehicle in the environment map. By adopting the technical scheme provided by the application, traversal search can be simultaneously carried out on two sides of the barrier, a plurality of turning points which do not collide with each barrier in the flight path are determined, the raster path consisting of the starting point, the turning points and the end point is optimized to obtain the target flight path, the flight path is simplified, the path search speed is accelerated, the search time is reduced, and therefore the flight time is saved.

Description

Unmanned aerial vehicle path searching method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of path planning technologies, and in particular, to a method and an apparatus for searching for a path of an unmanned aerial vehicle, an electronic device, and a storage medium.

Background

As a main research content of motion planning, path planning is often applied to motion trajectory planning of smart technology products, including: autonomous collision-free action of the robot, obstacle avoidance and prevention of flying of the unmanned plane and the like; for example: in a building, an unmanned aerial vehicle is used to play a role of 'courier' to deliver specified goods from a delivery area to a specific room or station, wherein the goods may need to fly to the stairs, cross a gallery and bypass a stand column, so as to avoid obstacles and ensure the optimal path, and further, the goods need to accurately reach a destination, which relates to the problem of path planning in an environment with obstacles.

At present, route search is mainly performed through an A-x algorithm, turning points on a flight route are searched out to be a plurality of route nodes which are tightly attached to the edges of all obstacles in a flight environment, most edge lines of the obstacles are included in the flight route determined through the route nodes, the flight route is long, the route nodes are read in longitude and latitude or relative coordinate positions, after the route search is completed, the output raster route is obtained to be in a step shape, for the unmanned aerial vehicle, the flight direction needs to be adjusted frequently in a large angle, a large amount of flight time is wasted, the obtained route is complex, and the search time is long. Therefore, how to shorten the search time and plan a simple and efficient path capable of meeting different conditions becomes an urgent problem to be solved.

Disclosure of Invention

In view of the above, an object of the present application is to provide a method, an apparatus, an electronic device and a storage medium for searching a path of an unmanned aerial vehicle, which can perform traversal search on both sides of an obstacle, determine a plurality of turning points in a flight path that do not collide with each obstacle, and optimize a raster path composed of a start point, the plurality of turning points and an end point to obtain a target flight path; the method for acquiring the flight path by simultaneously traversing the search path on the two sides and optimizing the raster path simplifies the flight path, accelerates the path search speed, reduces the search time and saves the flight time.

The application mainly comprises the following aspects:

in a first aspect, an embodiment of the present application provides a method for searching a path of an unmanned aerial vehicle, where the method includes:

acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; the grid map is obtained by rasterizing an environment map, and comprises a plurality of obstacles;

performing path searching according to the starting point and the end point of the flight path and on two sides of the obstacle to determine a plurality of turning points of the flight path;

determining a raster path of the unmanned aerial vehicle in the raster map based on the turning points;

and optimizing the raster path, and constructing a target flight path of the unmanned aerial vehicle in the environment map.

Further, the grid map is determined by:

constructing an environment map according to the edge vertex information of each obstacle in the flight environment;

rasterizing the environment map according to a preset grid unit;

marking the grid unit at the edge of each obstacle according to the edge information of each obstacle in the environment map after rasterization; wherein different obstacles are distinguished by different markers;

for each obstacle, filling grid units of a closed area formed by the edges of the obstacle according to the marks of the edges of the obstacle by using the same marks as the edges of the obstacle;

and determining the environment map filled with the obstacles in the environment map as a grid map.

Further, the turning point of the flight path is determined by the following steps:

taking a connecting line between a starting point and an end point of a flight path as a reference line to search the path, and determining whether the reference line and an obstacle have a touch point according to marks of grid units in a grid map in the path search; the touch point is an intersection point with the minimum distance from the starting point in a plurality of intersection points where the reference line intersects with the edge of at least one obstacle;

if a touch point exists, determining the direction from the starting point to the touch point as the starting direction of the searching direction by taking the starting point as the center of a circle, simultaneously searching paths on two sides of the starting direction, determining a turning point in the searching direction on one side of the obstacle which firstly avoids the touch point in the path searching process, and updating the turning point as the starting point to continuously search the next turning point until no touch point exists between a connecting line between the turning point and the end point and any obstacle; wherein, the straight line path formed by the turning point and the starting point and the straight line path formed by the ending point are not contacted with the obstacle.

Further, a target flight path of the unmanned aerial vehicle is constructed through the following steps:

after the raster paths in the raster map are linearized, the central point of each raster unit from the starting point to the end point is obtained, and the central point is corresponding to the environment map to be used as a path node;

sequentially connecting each path node in the environment map to determine a linear path;

performing node impurity removal on path nodes on the linear path according to each obstacle in the environment map, and determining optimized path nodes;

and constructing a target flight path of the unmanned aerial vehicle in the environment map according to the optimized path nodes.

Further, the optimized path node is determined by the following steps:

sequentially constructing a connecting line between the starting point and each path node until the connecting line is contacted with the barrier;

and determining the last path node of the path nodes which belong to the connecting line as the optimized path node.

Further, according to the optimized path node, a step of constructing a target flight path of the unmanned aerial vehicle in an environment map includes:

according to the optimized path node, all path nodes between the optimized path node and the last optimized path node are eliminated; in the initial state, the last optimized path node is the starting point of the flight path of the unmanned aerial vehicle;

updating the optimized path node as a starting point, and determining the next optimized path node until a connecting line between the optimized path node and the end point is not contacted with any obstacle;

and connecting the starting point, all optimized path nodes and the end point of the unmanned aerial vehicle flight path in sequence, and constructing the target flight path of the unmanned aerial vehicle in the environment map.

In a second aspect, an embodiment of the present application further provides a search apparatus for a path of an unmanned aerial vehicle, where the search apparatus includes:

the acquisition module is used for acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; the grid map is obtained by carrying out rasterization processing on an environment map, and the grid map comprises a plurality of obstacles;

the searching module is used for searching paths according to the starting point and the end point of the flight path and two sides of the barrier to determine a plurality of turning points of the flight path;

a determining module, configured to determine a raster path of the drone in the raster map based on the plurality of turning points;

and the optimization module is used for optimizing the raster path and constructing a target flight path of the unmanned aerial vehicle in the environment map.

Further, the search apparatus further includes a drawing module, where the drawing module is configured to:

rasterizing the environment map according to a preset grid unit;

In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the method of searching for a drone path as described above.

In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method for searching for a route of a drone.

The embodiment of the application provides a method, a device, electronic equipment and a storage medium for searching an unmanned aerial vehicle path, wherein the searching method comprises the following steps: acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; the grid map is obtained by rasterizing an environment map, and comprises a plurality of obstacles; performing path searching according to the starting point and the end point of the flight path and on two sides of the obstacle to determine a plurality of turning points of the flight path; determining a raster path of the unmanned aerial vehicle in the raster map based on the turning points; and optimizing the raster path, and constructing a target flight path of the unmanned aerial vehicle in the environment map.

Therefore, traversal search can be simultaneously carried out on two sides of the barrier by adopting the technical scheme provided by the application, a plurality of turning points which do not collide with each barrier in the flight path are determined, and the raster path consisting of the starting point, the turning points and the end point is optimized to obtain the target flight path; the method for obtaining the flight path by simultaneously traversing the search path on two sides and optimizing the raster path simplifies the flight path, accelerates the path search speed, reduces the search time and saves the flight time.

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 shows a flowchart of a method for searching a path of an unmanned aerial vehicle according to an embodiment of the present application;

fig. 2 is a flowchart illustrating another unmanned aerial vehicle path searching method provided in an embodiment of the present application;

fig. 3 is a schematic diagram illustrating an obstacle boundary search provided in an embodiment of the present application;

FIG. 4 is a schematic diagram illustrating a path search provided by an embodiment of the present application;

fig. 5 is a schematic diagram illustrating a path node heuristic optimization provided in an embodiment of the present application;

FIG. 6 illustrates a flight path diagram of the output before optimization provided by an embodiment of the present application;

FIG. 7 illustrates a schematic flight path diagram of the optimized output provided by embodiments of the present application;

fig. 8 shows one of the schematic structural diagrams of a searching apparatus for a route of an unmanned aerial vehicle according to an embodiment of the present application;

fig. 9 shows a second schematic structural diagram of a searching apparatus for a route of an unmanned aerial vehicle according to an embodiment of the present application;

fig. 10 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.

Detailed Description

To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and that steps without logical context may be performed in reverse order or concurrently. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.

In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

To enable those skilled in the art to use the present disclosure, the following embodiments are given in conjunction with the specific application scenario "route search for drone," and it will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and application scenarios without departing from the spirit and scope of the present disclosure.

The method, the apparatus, the electronic device, or the computer-readable storage medium described in the embodiments of the present application may be applied to any scenario that requires path search, and the embodiments of the present application do not limit a specific application scenario, and any scheme that uses the method, the apparatus, the electronic device, and the storage medium for searching the path of the unmanned aerial vehicle provided in the embodiments of the present application is within the scope of protection of the present application.

It should be noted that, as a main research content of motion planning, path planning is often applied to motion trajectory planning of smart technology products, including: autonomous collision-free action of the robot; obstacle avoidance and sudden prevention flight of the unmanned aerial vehicle; the cruise missile avoids radar search, prevents missile attack, completes a penetration and explosion task and the like. And is also often applied to GPS navigation in daily life, road planning based on a GIS system, urban road network planning navigation and the like. The application in the field of decision management is as follows: vehicle problems (VRP) in logistics and similar resource management resource allocation problems. Routing problems in the field of communications technology, etc. For unmanned aerial vehicles, one of the most basic skill points is: under certain interference conditions, fly from the current origin to another specified target point. For example: the unmanned aerial vehicle is used in a building to play a role of a 'courier' to deliver specified goods from a delivery area to a specific room or station, wherein the goods may need to fly to the stairs, cross a long corridor and bypass a stand column, so that the best path is ensured while obstacles are avoided, and the destination is accurately reached, which relates to the problem of path planning in an environment with obstacles. How to accurately and rapidly plan a simple and efficient path capable of meeting different conditions under a given starting point and end point scene. Distinguishing from the state of the obstacle: the method can be divided into static path planning and dynamic path planning, and can be divided into global path planning based on prior information and local path planning based on information acquired by a sensor according to the grasp of environmental information.

With the innovation and development of path planning algorithms, path planning algorithms have formed deterministic methods and stochastic methods. The deterministic method is established on the basis of global environment information prior and is divided into an intelligent search method, a steepest descent method, a visual image method, an artificial potential field method, an optimal control method, a simulated annealing method, a genetic algorithm and the like. The random search is usually based on a high-efficiency randomization algorithm, such as probabilistic roadmap (prm). In the deterministic approach, the a-algorithm is particularly common, comparing depth search and breadth search: the benefit of deep search is that time is fast, but the optimal solution cannot necessarily be solved; the breadth search does find the optimal solution, but since the breadth search is carried out layer by layer, each point must be expanded, and thus, the time efficiency and the space efficiency are not high. The a algorithm can solve the two disadvantages as much as possible: the optimal solution is solved with great probability, and the redundant time can be reduced. In the application process, however, the A-x algorithm is only limited to processing the map after rasterization, and the map has low adaptability; the A-algorithm gradually determines the next path grid by comparing the current path grid with the heuristic function value F of the adjacent grid, and the A-algorithm cannot ensure the optimal searched path when a plurality of minimum values exist; the optimal solution obtained by the a-algorithm is often in the form of a raster path, and needs to be further converted to be read by the unmanned aerial vehicle as sequential coordinate nodes.

In addition, the algorithm a is a common path finding and graph traversing algorithm, has better performance and accuracy, but the reference obstacles are too regular, and are only limited to the rasterized map, so that the algorithm a cannot be directly applied to the road planning of some specific irregular obstacle areas, and meanwhile, the path output is not simple enough. At present, route search is mainly performed through an A-x algorithm, turning points on a flight route are searched out to be a plurality of route nodes which are tightly attached to the edges of all obstacles in a flight environment, most edge lines of the obstacles are included in the flight route determined through the route nodes, the flight route is long, the route nodes are read in longitude and latitude or relative coordinate positions, after the route search is completed, the output raster route is obtained to be in a step shape, for the unmanned aerial vehicle, the flight direction needs to be adjusted frequently in a large angle, a large amount of flight time is wasted, the obtained route is complex, and the search time is long. Therefore, how to shorten the search time and plan a simple and efficient path capable of meeting different conditions becomes an urgent problem to be solved.

Based on this, the application provides a method, an apparatus, an electronic device and a storage medium for searching a path of an unmanned aerial vehicle, where the searching method includes: acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; the grid map is obtained by rasterizing an environment map, and comprises a plurality of obstacles; performing path searching according to the starting point and the end point of the flight path and on two sides of the obstacle to determine a plurality of turning points of the flight path; determining a raster path of the unmanned aerial vehicle in the raster map based on the turning points; and optimizing the raster path, and constructing a target flight path of the unmanned aerial vehicle in the environment map.

For the purpose of facilitating an understanding of the present application, the technical solutions provided in the present application will be described in detail below with reference to specific embodiments.

Referring to fig. 1, fig. 1 is a flowchart of a method for searching a route of an unmanned aerial vehicle according to an embodiment of the present application, where as shown in fig. 1, the method includes:

s101, acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map;

in the step, a grid map is obtained by carrying out rasterization processing on an environment map, wherein the grid map comprises a plurality of obstacles; the method comprises the steps that the obstacles can be regular or irregular, when a grid map is constructed, the method is not limited to obstacle avoidance of regular obstacles, the irregular obstacles are subjected to fitting rasterization through vertex sampling and an edge detection method, and meanwhile, the method is not limited to a single obstacle and can be suitable for obstacle avoidance of any plurality of irregular obstacles; the environment map is constructed according to the flight environment of the unmanned aerial vehicle, and illustratively, a plurality of obstacles such as stairs, wall surfaces, piers, artworks and the like can be included in the flight environment.

It should be noted that, before step S102 is performed, a grid map needs to be determined, please refer to fig. 2, fig. 2 is a flowchart of another method for searching a route of an unmanned aerial vehicle according to an embodiment of the present application, and as shown in fig. 2, the grid map is determined through the following steps:

s201, constructing an environment map according to edge vertex information of each obstacle in the flight environment;

in this step, if an irregular obstacle is constructed in the flight environment, the obstacle edge can be determined by using the position of the obstacle vertex.

Exemplarily, in the actual measurement process of the real environment, the edge vertex information of the obstacle can be acquired by adopting methods such as vision, ultrasonic waves, infrared and radar to construct an environment map; in the process of testing the virtual environment aiming at the path search scheme, a blank area with a specified size can be selected to be constructed, and then a tested environment map is obtained in a mode that a random point in an area generated by a computer constructs a closed barrier.

S202, rasterizing the environment map according to a preset grid unit;

in the step, proper grid size is selected to grid the environment map, then the boundary line of the edge of the obstacle is fitted, the line edge is converted into the grid edge, relevant information is stored, and finally the single obstacle in the environment map is distinguished in an edge searching mode.

Illustratively, in the process of constructing the tested environment map, an environment map area range is determined firstly, a closed obstacle area is constructed by acquiring random vertex positions in the area range generated by a computer, a required number of obstacles can be constructed according to actual test requirements, the size of a grid needs to be set before the environment map is rasterized, the accuracy of the obstacle is higher after fitting when the grid is smaller, the final output path is more accurate, but the calculation amount in the process of fitting and path searching is correspondingly increased, so the calculation time and the fitting accuracy are in an inverse proportion relation. The tested environment map parameters and rasterized data are shown in table 1:

TABLE 1 environmental map parameters and rasterized data sheet

For example, in this embodiment, the size of the selected test environment map is (500 × 400) square meters, the selected grid unit is (5 × 5) square meters, the environment map is rasterized, and then the grid map is output as 80 rows and 100 columns in a two-dimensional array form, each data corresponds to one grid information of a relative position in the grid map, and the data amount is 8000. If the grid with the size of (1 multiplied by 1) square meter is selected, the fitting effect is better, but the data volume is expanded to 25 times of the original data volume, so that the calculation amount and the calculation time are greatly increased.

S203, marking the grid unit at the edge of each obstacle according to the edge information of each obstacle in the environment map after rasterization;

in this step, different obstacles are distinguished by different markers; before marking the grid unit at the edge of each obstacle, the edge of each obstacle needs to be fitted according to the edge vertex information of each obstacle, the boundary fitting process is also a quantization process, when the quantization series is constant, the number of sampling points has a remarkable influence on the image quality, the number of the sampling points is more, the image quality is better, and when the number of the sampling points is reduced, the block effect on the image is gradually obvious; in the boundary fitting process, the linear boundary is fitted into a grid-shaped boundary in a mode of equally dividing the boundary line between adjacent vertexes and marking the grid at the corresponding position of each equally divided segment.

Illustratively, the length of each equal part of the edge line is set as d, and the larger d is, the fewer the parts are after the equal division is, but the worse the fitting degree is; the smaller d is, the more the number of parts after the equal division is, the better the fitting degree is, so the equal division length d needs to be determined by referring to the size of the set grid unit, and the best fitting effect is achieved while the calculation amount is reduced.

For example, there are two adjacent vertices a (x)_a,y_a)，b(x_b,y_b) And D is the length of the equipartition gap, the equipartition fraction (taking an integer) is D, and the formula is shown as follows:

after that, the corresponding edge lines can be fitted through the grid units at the positions corresponding to the node coordinates after the edge lines of the obstacles are marked and evenly divided into nodes with m_iExpressed as node coordinates of

Wherein:

traversing all edges of the closed barrier, and sequentially aligning m_iMarking and fitting to obtain the grid unit edge of each obstacle, and distinguishing and marking different obstacles in sequence when marking each obstacle; after the edge fitting, a grid map with the information of the edge of the obstacle is obtained, and step S204 is further performed to mark and fill each of the obstacles distinguished by using an edge detection method.

S204, aiming at each obstacle, filling the grid unit of the closed area formed by the edges of the obstacle according to the marks of the edges of the obstacle by using the same mark as the edge of the obstacle;

in this step, the grid cells of the closed area formed by each obstacle edge are filled with the same mark as the obstacle edge.

For example, referring to fig. 3, fig. 3 is a schematic diagram of searching a boundary of an obstacle according to an embodiment of the present disclosure, as shown in fig. 3, a grid cell of a blank area in fig. 3 is marked as 0, and grid cells marked as 1 and 2 represent grid boundaries of different obstacles, and a left boundary and a right boundary are searched and distinguished by searching for a rising edge and a falling edge of a grid cell mark in an obstacle edge searching process, and the number of marks can be determined according to heights of the rising edge and the falling edge to distinguish each obstacle; in the boundary searching process, the boundary searching direction is from bottom to top, the grid line searching area is the line span of the current barrier area, and the boundary is quickly searched by adopting a local searching method; if the horizontal coordinate position of the boundary searched by the current line is i and the search radius is r, the next line boundary search range is j, and the following formula is satisfied:

|j-i|≤r；

in the process of searching and marking the left and right boundaries, the left and right boundaries are accurately distinguished and the boundary positions are stored, and then the blank area which takes the left boundary as a starting point and the right boundary as an end point is filled with the same marks as the boundaries for the single barrier of the current line.

And S205, determining the environment map filled with the obstacles in the environment map as a grid map.

In this step, after filling each obstacle one by one in step S204, the required start point and end point are acquired in other blank areas of the grid map according to the flight environment and the flight requirement, and the current grid map information is output.

Illustratively, after all obstacles complete the boundary fitting and filling, a two-dimensional array is obtained, for example, the two-dimensional data obtained is as follows:

here, when a computer processes a common image, most of the common image is the operation of the numerical values of image pixels, the image can be regarded as a grid image after being infinitely amplified, and the data form of the grid image is also a multidimensional array; by fitting the environment map into the grid map, the readability of data is enhanced compared with a computer, and the search process is effectively simplified and the data processing difficulty is reduced by marking obstacles in the fitting process. The two-dimensional array is grid map information observed from a computer operation visual angle, wherein 0 is a blank area, and 1, 2, 3 and 4 … … represent different obstacles identified and marked in the obstacle grid fitting process and the area occupied by each obstacle; and in the post-processing process, the raster data is read and processed in a horizontal and vertical coordinate mode, and the accuracy of decision is improved in a mode of quantizing image information.

S102, performing path searching according to the starting point and the end point of the flight path and two sides of the obstacle to determine a plurality of turning points of the flight path;

after obtaining complete raster map information, constructing a vector direction from a starting point to an end point as a path searching direction of the raster map, taking a turning point as the starting point in an initial state, performing path detection according to the searching direction by taking the turning point as the starting point, identifying an obstacle in front of the path, traversing two searching paths by taking the current searching direction as a reference, determining the turning point in the searching direction which firstly avoids the current obstacle, updating the turning point as the starting point, establishing new vector directions of the starting point and the end point in the same way, continuously searching a flight path in the raster map according to the searching direction and the turning point, continuously searching the turning point until no obstacle exists between the turning point and the end point, and storing the paths between the turning points according to the sequence to obtain a final raster path.

It should be noted that, the turning point of the flight path is determined by the following steps:

s1021, searching a path by taking a connecting line between a starting point and an end point of the flight path as a reference line, and determining whether a touch point exists between the reference line and an obstacle according to marks of grid units in a grid map in the path searching;

in the step, the touch point is an intersection point with the minimum distance from the starting point in a plurality of intersection points where the reference line intersects with the edge of at least one obstacle; the whole path searching is carried out based on the grid map, the grid map is more convenient to read, and the judgment of the path state is more facilitated in the searching process. When path searching is carried out initially, a connecting line between a starting point and an end point of a flight path needs to be constructed as a reference line, path searching is carried out, in the path searching, whether a touch point exists between the reference line and an obstacle or not is determined according to marks of grid units in a grid map, namely, information of the grid units is read along the direction of the reference line from the starting point, and whether the reference line is in contact with the obstacle or not is judged according to the information of the grid units; for example, when the data information of the grid unit is recognized as a rising edge, the data information is regarded as being blocked by an obstacle, and meanwhile, the height of the rising edge is read to judge the touched obstacle and record a touch point.

And S1022, if a touch point exists, determining the direction from the starting point to the touch point as the starting direction of the search direction by taking the starting point as the center of a circle, simultaneously performing path search on two sides of the starting direction, determining a turning point in the search direction on the side which avoids the obstacle to which the touch point belongs firstly in the path search process, updating the turning point as the starting point, and continuously searching the next turning point until no touch point exists between the connecting line between the turning point and the terminal point and any obstacle.

In this step, neither the straight path formed by the turning point and the starting point nor the straight path formed by the ending point is in contact with the obstacle.

Determining the direction from the starting point to the touch point as the starting direction of the searching direction, taking the starting point as the center of a circle, simultaneously spreading the starting direction as the middle boundary to two sides in a fan-shaped spreading mode to search a path, when searching by traversing two sides, searching to the direction avoiding the obstacle by judging whether the rising edge of the same raster information marked in the current searching direction exists or not, namely whether the rising edge is in contact with the obstacle or not, and searching to the direction avoiding the obstacle if the rising edge is not in contact with the obstacle, and searching for a turning point P in the direction_nMaking turning point P in path search_nThe turning point determination notations shown below are satisfied:

wherein: p_n-1Is the last turning point, P_endAs the end point, the end point is,

is the path formed by the last turning point and the currently searched turning point,

the path formed by the current turning point and the terminal point,

is composed of

Edge of

The first touching obstacle marked in the direction.

S103, determining a raster path of the unmanned aerial vehicle in the raster map based on the turning points;

in the step, after the turning point meeting the condition is searched, the previous turning point is stored, and the raster path from the previous turning point to the current turning point is also stored, in the following searching process, the new turning point is taken as the starting point again, the direction from the turning point to the end point is reconstructed as the starting direction of the searching direction, the searching process is repeated, when no obstacle exists in the path searching process along the searching direction and towards the end point by taking the new turning point as the starting point, the end point is taken as the last turning point to be stored, the stored raster paths are sequentially connected, and the raster path of the unmanned aerial vehicle is determined in the raster map after the searching process is finished.

For example, referring to fig. 4, fig. 4 is a schematic diagram of path search according to an embodiment of the present application, as shown in fig. 4, P_startAnd P_endRespectively serving as a starting point and an end point of a flight path of the unmanned aerial vehicle, A, B, C, D and E five areas represent different obstacles, and mark filling is completed on each obstacle through different marks to construct P_startTo P_endVector of direction is

With P_startStarting point and constructing P_startIn order to be the initial turning point, the turning point is defined as,

and searching a path for the searching direction, detecting the rising edge of the data of the first grid unit, deducing the number of the marks according to the height of the rising edge, judging the touch obstacle A, and marking the touch point as a point m. With P_startIs used as the center of a circle,

traversing the search directions for both sides of the reference direction, when the relative angle reaches alpha,

the upper half side does not avoid the obstacle A, the lower half side avoids the obstacle A, the lower half side direction is selected as the search direction, and a point P is searched along the search direction₁Satisfies the formula of turning point determination, i.e.

And

are not in contact with the obstacle a.

Illustratively, as shown in FIG. 4, P is sought₁Then store P₁For the new turning point and continue to construct P₁To P_endVector of direction

With P₁As a starting point, the method comprises the following steps of,

and searching a path for the searching direction, after detecting the rising edge of the first raster data, deducing the number of the marks according to the height of the rising edge, judging that the obstacle E is touched, and marking the touched point as a point n. With P₁Is used as the center of a circle,

traversing the search directions for both sides of the reference direction, when the relative angle reaches beta,

the upper half side of the block has already avoided the obstacle E, the lower half side of the block has not avoided the obstacle E, the upper half side direction is selected as the search direction, and a point P is searched along the search direction₂Satisfies the formula of turning point determination, i.e.

And

are not in contact with the obstacle E. Store P₂And continue to construct P₂To P_endVector of direction

With P₂As a starting point, the method comprises the following steps of,

performing a path search for the search direction without detecting a rising edge, i.e. P, during the search₂And P_endWith no barrier between them, store P_endAnd reading the turning points in order as path nodes as final turning points.

Here, the turning point and the search direction will be continuously updated in the search process, the raster path between the turning point updated each time and the pre-update turning point will be stored in the set memory, and after the final turning point is obtained, the stored raster paths are sequentially spliced to obtain a complete raster path from the starting point to the end point.

And S104, optimizing the raster path, and constructing a target flight path of the unmanned aerial vehicle in the environment map.

In the step, the raster path is in a step shape, however, for the unmanned aerial vehicle, the flight direction of the step-shaped path needs to be adjusted at a large angle frequently, which is not beneficial to flight, and a large amount of flight time is wasted, so that the path of the step-shaped path needs to be further optimized, path nodes from a starting point to a destination point are screened and purified in a exploration-type mode, the flight path is simplified, and the target flight path of the unmanned aerial vehicle is constructed.

It should be noted that, the target flight path of the drone is specifically constructed through the following steps:

s1041, after the grid paths in the grid map are linearized, obtaining the central point of each grid unit from the starting point to the end point, and corresponding the central point to the environment map as a path node;

in the step, the unmanned aerial vehicle path nodes are read according to longitude and latitude or relative coordinate positions, after the path search is completed, the output raster path is obtained and cannot be directly read by the unmanned aerial vehicle, so that the output raster path needs to be linearized before the optimization processing of the raster path, the central point is corresponding to the environment map as a path node according to the central point of each raster unit from the starting point to the end point in the raster path, and the linear path with node information is obtained.

S1042, sequentially connecting each path node in the environment map to determine a linear path;

s1043, removing nodes of path nodes on the linear path according to each obstacle in the environment map, and determining optimized path nodes;

in the step, the path nodes in the direction from the starting point to the end point are screened and decontaminated in a exploration mode by combining the environment map information, the path nodes are optimized, and simplified path nodes are screened out.

Here, the optimized path node is determined by:

1) sequentially constructing a connecting line between the starting point and each path node until the connecting line is contacted with the barrier;

in the step, a forward-pushing type node updating method is adopted for optimizing the path nodes, redundant path nodes are removed through node forward-pushing, the node forward-pushing process is compared with a raster map, the feasibility of the current forward-pushing is judged, and then the path node information is stored and updated. The specific formula is as follows:

wherein: s_iIs a forward-push starting point, and is a starting point S in an initial state_start，M_jIn order for the path node to be pushed forward,

is a path formed by the connection line of the forward push starting point and the forward push path node, omega_hinderRepresenting the grid obstacle space region formed by all obstacles.

2) And determining the last path node of the path nodes which belong to the connecting line as the optimized path node.

In this step, a path node to which a connection line in contact with the obstacle belongs is determined, and a previous path node of the path node is determined as an optimized path node and stored.

S1044, constructing a target flight path of the unmanned aerial vehicle in the environment map according to the optimized path nodes.

In this step, according to the optimized path node, a step of constructing a target flight path of the unmanned aerial vehicle in an environment map includes:

1) clearing all path nodes between the optimized path node and the last optimized path node according to the optimized path node;

in the step, in an initial state, a last optimized path node is a starting point of a flight path of the unmanned aerial vehicle; and clearing all path nodes between the path node and the last optimized path node according to the determined optimized path node, and clearing all path nodes between the path node and the starting point if no last optimized path node exists in the initial state.

2) Updating the optimized path node as a starting point, and determining the next optimized path node until a connecting line between the optimized path node and the end point is not contacted with any obstacle;

in the step, the determined optimized path node is updated to be a starting point, a connecting line between the starting point and each subsequent path node is continuously constructed until the constructed connecting line is in contact with the obstacle, and the next optimized path node is determined until the determined connecting line between the optimized path node and the end point is not in contact with any obstacle.

For example, please refer to fig. 5 for a schematic diagram of a path node exploration-type optimization, fig. 5 is a schematic diagram of a path node exploration-type optimization provided in the embodiment of the present application, as shown in fig. 5, fig. 5 shows that a linearized path of an environment map is put into a grid map for comparison, and whether a recursion flight path is in contact with an obstacle can be determined more simply and directly by comparing grid map information in a process of path node forward recursion. From the starting point S_startAnd path node M₁Connecting to build a path without contacting with an obstacle, and building S_startAnd M₂The node M is continuously pushed forward without contacting the obstacle_j. Construct S as a graph_startAnd M₆Without contacting the obstacle, and S is constructed_startAnd M₇When the path is contacted with the obstacle, the path node M is updated₆Is S₁And eliminate all M in the early stage_jNode to be connected with S₁Building and subsequent path node M for starting point_jAnd (4) repeating the steps, continuously updating the starting point and removing the intermediate path nodes to obtain a group of simplified path nodes.

3) And connecting the starting point of the flight path of the unmanned aerial vehicle, all optimized path nodes and the end point in sequence, and constructing the target flight path of the unmanned aerial vehicle in the environment map.

In the step, the starting point, all optimized path nodes and the end point of the flight path of the unmanned aerial vehicle are sequentially connected, so that the target flight path of the unmanned aerial vehicle is constructed in the environment map.

For example, please refer to fig. 6 and 7, where fig. 6 is a schematic view of a flight path of output before optimization provided by the embodiment of the present application, fig. 7 is a schematic view of a flight path of output after optimization provided by the embodiment of the present application, and a final output of a path node after optimization is shown in fig. 7, as is apparent from fig. 6 and 7, the number of the path nodes after optimization is greatly reduced, and the flight path is simpler. In the embodiment, the current exploration path is compared with the raster map at all times in the path node optimization process, whether the current exploration path contacts with the barrier is judged, and then the next path exploration is carried out, so that the safety and the reliability of the optimized path node are ensured, and the simplified flight path is constructed.

Illustratively, to verify the technical effect of the present embodiment, 4 test maps with a size of 500m × 400m are created for testing, and the testing process mainly includes the following four steps: firstly, creating a test map and carrying out initialization setting: here, the number of obstacles in the 4 test maps is set to 4, 6, 8, 10, respectively, the grid cell size is set to 5m × 5m, and the positions of the start point and the end point are set; wherein, the obstacle is a closed obstacle area constructed by generating random vertexes; secondly, rasterizing the test map: the method comprises the steps that a test map is rasterized according to a grid unit, and a grid map and grid edge information of an obstacle area are obtained; on the premise of knowing the grid edge of the barrier, filling the closed barrier by using the idea of local binary image edge detection and distinguishing and marking different barriers one by one; thirdly, path searching: the path searching process is based on the thinking of traversing the searching direction and updating the searching vector direction, different obstacles are marked in the initial stage of rasterization of the test map, the information readability of the current searching stage is enhanced, and the raster path is obtained through the searching thinking; fourthly, grid path linearization and optimization: here, the unmanned aerial vehicle acquires a path by reading a path node, the raster path cannot be directly read, the raster unit of the raster map is mapped into the original map by a central point, the raster path is sequentially mapped into a series of path nodes, the path nodes are sequentially connected to form a linear path, unprocessed path nodes in the linear path are in a step shape, further optimization is needed, and the path nodes are subjected to impurity removal according to the idea of node forward test in the optimization process, so that the path is simplified to obtain a target flight path. In the technical effect comparison, mainly comparing with the flight path searched by the a-algorithm, comparing the output results obtained by the two ideas on the basis of containing 4, 6, 8 and 10 obstacles respectively, wherein the path obtained by the a-algorithm is in an obvious wrapping state, because the a-algorithm is only applied to a grid map, the output is also only a grid path, in order to carry out quantitative comparison, the linear operation is also carried out on the path output obtained after the a-algorithm operation, the grid path output is converted into a coordinate node path, after the coordinate node path of the a-algorithm is obtained, the flight path length is calculated, the flight path length is compared with the flight path lengths before and after optimization, and the summarized flight path length data is shown in table 2:

TABLE 2 flight path length contrast table

	4 disorders of the heart	6 disorders	8 disorders of the heart	10 obstacles
					Algorithm	607.27	742.78	580.35	655.20
Before optimization of the embodiment	497.78	579.49	535.71	579.85
					After the embodiment is optimized	406.39	518.71	457.39	489.44

As can be seen from the above table, the flight path before optimization is shorter than the flight path output by the a-x algorithm; the flight path after the optimization of the path search scheme of the embodiment can be further simplified, so that the flight time is shortened.

The embodiment of the application provides a method for searching unmanned aerial vehicle paths, which comprises the following steps: acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; the grid map is obtained by rasterizing an environment map, and comprises a plurality of obstacles; performing path searching according to the starting point and the end point of the flight path and on two sides of the obstacle to determine a plurality of turning points of the flight path; determining a raster path of the unmanned aerial vehicle in the raster map based on the turning points; and optimizing the raster path, and constructing a target flight path of the unmanned aerial vehicle in the environment map.

Based on the same application concept, the embodiment of the present application further provides a searching apparatus for an unmanned aerial vehicle path corresponding to the searching method for an unmanned aerial vehicle path provided by the above embodiment, and as the principle of the apparatus in the embodiment of the present application for solving the problem is similar to the searching method for an unmanned aerial vehicle path provided by the above embodiment of the present application, the implementation of the apparatus can refer to the implementation of the method, and repeated details are not repeated.

Please refer to fig. 8 and 9, in which fig. 8 is a first structural diagram of a searching apparatus for unmanned aerial vehicle route according to an embodiment of the present application, and fig. 9 is a second structural diagram of the searching apparatus for unmanned aerial vehicle route according to the embodiment of the present application. As shown in fig. 8, the search means 810 includes:

the obtaining module 811 is used for obtaining a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; the grid map is obtained by rasterizing an environment map, and comprises a plurality of obstacles;

a searching module 812, configured to perform path searching according to the starting point and the ending point of the flight path and on two sides of the obstacle to determine multiple turning points of the flight path;

a determining module 813, configured to determine a raster path of the drone in the raster map based on the plurality of turning points;

and an optimizing module 814, configured to perform optimization processing on the raster path, and construct a target flight path of the unmanned aerial vehicle in the environment map.

Optionally, as shown in fig. 9, the search apparatus 810 further includes a drawing module 815, where the drawing module 815 is configured to:

rasterizing the environment map according to a preset grid unit;

Optionally, the searching module 812 is configured to determine the turning point of the flight path by:

Optionally, the optimization module 814 is configured to construct a target flight path of the drone by:

Optionally, the optimization module is configured to determine the optimized path node through the following steps:

Optionally, when the optimization module 814 is configured to construct a target flight path of the unmanned aerial vehicle in the environment map according to the optimized path node, the optimization module 814 is specifically configured to:

The utility model provides a search arrangement in unmanned aerial vehicle route, search arrangement includes: the acquisition module is used for acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; the grid map is obtained by rasterizing an environment map, and comprises a plurality of obstacles; the searching module is used for searching paths according to the starting point and the end point of the flight path and two sides of the barrier to determine a plurality of turning points of the flight path; a determining module, configured to determine a raster path of the drone in the raster map based on the plurality of turning points; and the optimization module is used for optimizing the raster path and constructing a target flight path of the unmanned aerial vehicle in the environment map.

Referring to fig. 10, fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 10, the electronic device 1000 includes a processor 1010, a memory 1020, and a bus 1030.

The memory 1020 stores machine-readable instructions executable by the processor 1010, when the electronic device 1000 runs, the processor 1010 and the memory 1020 communicate through the bus 1030, and when the machine-readable instructions are executed by the processor 1010, the steps of the method for searching for a path of an unmanned aerial vehicle in the method embodiments shown in fig. 1 and fig. 2 may be executed.

An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for searching for a route of an unmanned aerial vehicle in the method embodiments shown in fig. 1 and fig. 2 may be executed.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application 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 functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A searching method for unmanned aerial vehicle paths is characterized by comprising the following steps:

2. The search method of claim 1, wherein the grid map is determined by:

rasterizing the environment map according to a preset grid unit;

3. The search method according to claim 2, characterized in that the turning point of the flight path is determined by the following steps:

4. The search method of claim 1, wherein the target flight path of the drone is constructed by:

5. The search method of claim 4, wherein the optimized path node is determined by:

6. The searching method according to claim 4, wherein the step of constructing the target flight path of the drone in the environment map according to the optimized path node comprises:

7. A search apparatus for a route of an unmanned aerial vehicle, the search apparatus comprising:

the acquisition module is used for acquiring a starting point and an end point of a flight path of the unmanned aerial vehicle from a pre-constructed grid map; the grid map is obtained by rasterizing an environment map, and comprises a plurality of obstacles;

8. The apparatus of claim 7, further comprising a rendering module configured to:

rasterizing the environment map according to a preset grid unit;

9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is run, the machine-readable instructions when executed by the processor performing the steps of the drone path searching method of any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of searching for a drone path according to any one of claims 1 to 6.