UAV path planning method based on large-scale cruising

Abstract

The invention relates to the technical field of path planning, and discloses a method for unmanned aerial vehicle path planning based on large-scale cruising, comprising the steps of: geographic information collection, geographic information processing, regional discretization, starting point and target point setting, feasible path search, and path optimization; the geographic information collection of the invention is comprehensive and accurate, covering data such as terrain, obstacles, and no-fly zones, laying a foundation for path planning; regional discretization fits the characteristics of the unmanned aerial vehicle, and divides cells according to its minimum turning radius and sensor effective range to ensure flight safety and perception; feasible path search is efficient and reasonable, and through carefully designed cost and heuristic functions, multiple factors are taken into consideration to find the optimal path; path optimization is effective, based on a B-spline curve smoothing method, the curvature and length are reduced, energy consumption is reduced, and wear is reduced; safety verification is increased, and collision and cross-zone risks can be accurately judged, thereby enhancing safety assurance.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a method for unmanned aerial vehicle path planning based on large-scale cruising. Background Art

[0002] With the widespread application of drone technology in many fields, such as agricultural monitoring, geographic mapping, and emergency rescue, the path planning of drones in large-scale cruise missions has become a key technical challenge. When performing these missions, drones need to fly in complex and changing geographical environments and face many obstacles and limitations.

[0003] Traditional UAV path planning methods are often difficult to effectively respond to the needs of large-scale cruising. On the one hand, the acquisition and processing of geographic information is not accurate and comprehensive enough. Some methods only consider simple terrain data or limited obstacle information, and fail to fully integrate key data such as no-fly zones, resulting in potential safety hazards in the planned paths and being unable to adapt to the actual complex airspace control and geographical environment constraints. On the other hand, in terms of path search algorithms, there is a lack of in-depth integration of the performance characteristics of the UAV itself. For example, there is no targeted design based on the minimum turning radius of the UAV and the effective range of the sensor, which may cause the planned path to exceed the maneuverability of the UAV or cause the appearance of a perception failure area, affecting the execution of the mission and flight safety. In addition, in terms of path optimization, most traditional methods fail to find a good balance between ensuring path smoothness, reducing curvature changes, and reducing the total length, which not only increases the energy consumption and mechanical wear of the UAV, but also may affect the accuracy of data collection or mission operations due to the instability of the path. Moreover, traditional path planning is relatively weak in the safety verification link, and often cannot accurately detect the potential risks between the path and obstacles or no-fly zones, making it difficult to ensure the relative safety of the UAV during long-term, large-scale cruising.

[0004] In the field of engineering construction, engineering supervision plays a vital role in ensuring the quality, progress and safety of the project. Engineering supervision requires comprehensive supervision and management of the construction site. The traditional supervision method relies on manual inspections, which has problems such as low efficiency, untimely information acquisition and possible blind spots in supervision. The application of drones has brought new opportunities and challenges to engineering supervision. The drone cruise path planning in the engineering supervision scenario also needs to comprehensively consider the complex geographical environment of the construction site, the distribution of buildings and some specific no-fly or restricted-fly areas. At the same time, it is also necessary to combine the performance characteristics of the drone to ensure that it can efficiently and safely complete the inspection task of the construction site and collect accurate engineering construction information so that supervisors can find problems and make decisions in time.

[0005] However, the existing UAV path planning methods still have many shortcomings in meeting the needs of engineering supervision. There is an urgent need for an innovative, comprehensive UAV path planning method that can fully consider multiple factors such as geographic information, UAV performance, and mission requirements, so as to achieve efficient, safe, and accurate execution of large-scale cruise missions. Summary of the invention

[0006] The purpose of the present invention is to provide a UAV path planning method based on large-scale cruising, which solves the technical problems raised in the background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The UAV path planning method based on large-scale cruising includes the following steps:

[0009] Step 1: Geographic information collection:

[0010] Obtain geographic information data of the target cruising area, including terrain height data, obstacle distribution information, and no-fly zone information;

[0011] Step 2: Geographic Information Processing:

[0012] Integrate geographic information data into a unified coordinate system;

[0013] Step 3: Regional discretization:

[0014] Determine the side length of the discrete cell based on the minimum turning radius of the drone and the effective range of the sensor, and then divide the target cruising area into multiple cells based on its side length. Then calculate the center coordinates of each cell based on the coordinates of the lower left corner of the area, and then mark the cells where obstacles exist and belong to the no-fly zone;

[0015] Step 4: Setting the starting point and target point:

[0016] Determine the starting point and target point of the drone according to the flight mission requirements, and determine the cells where they are located;

[0017] Step 5: Search for feasible paths:

[0018] A graph search-based algorithm is used to search for a feasible path from the starting point to the target point on a discretized area graph;

[0019] Step 6: Path optimization:

[0020] The feasible path obtained by searching is optimized, and the optimized path is obtained.

[0021] As a further solution of the present invention: wherein, the data sources of the geographic information data are satellite images and geographic information system databases.

[0022] As a further solution of the present invention: the geographic information processing method is to use the earth coordinate system and convert it into a plane rectangular coordinate system for local calculation. The conversion formula is as follows:

[0023] ;

[0024] Where L is the longitude of the acquisition, B is the latitude of the acquisition, L0 and B0 are the longitude and latitude of the regional reference, x and y are the coordinate values ​​after the longitude and latitude conversion, respectively, and k1 and k2 are the scaling factors predetermined according to the earth's curvature and the regional range.

[0025] As a further solution of the present invention: the specific method of regional discretization is as follows:

[0026] StepA1: Cell division and side length determination of target cruising area

[0027] Divide the target cruising area into a number of discrete cells;

[0028] Among them, the side length of the cell is set to a;

[0029] And the value of a is determined according to the minimum turning radius of the drone and the effective range of the sensor;

[0030] That is, a=min{Rmin, Seff}, where Rmin refers to the minimum turning radius of the drone and Seff refers to the effective range of the sensor;

[0031] StepA2: Cell marking and center coordinate calculation

[0032] Label each cell as C ij , i=1, 2, ...m, j=1, 2, ...n, where m and n are the number of cells in the region in the x and y directions, respectively;

[0033] Then through:

[0034] ;

[0035] Calculate each cell C ij The center coordinates (x ij ,y ij ):

[0036] where x min and min is the coordinate value of the lower left corner of the area, which represents the minimum coordinate of the target cruising area;

[0037] Step A3: Setting cell obstacles and no-fly zone markings

[0038] By Q ij Mark whether there are obstacles in each cell;

[0039] When cell C ij If there is an obstacle, let Q ij The value of is 1;

[0040] When cell C ij If there is no obstacle, then let Q ij The value of is 0;

[0041] And through F ij Mark whether each cell belongs to a no-fly zone;

[0042] When cell C ij belongs to a no-fly zone, then F ij The value of is 1;

[0043] When cell C ij It belongs to the flyable area, then let F ij The value of is 0.

[0044] As a further solution of the present invention: the starting point and the target point are set as follows:

[0045] The starting point and target point of the drone are marked as P A (x A ,y A ) and P B (x B ,y B ), and mark the cells where the starting point and the target point are located as C A and C B .

[0046] As a further solution of the present invention: the feasible path search method is as follows:

[0047] Step B1. Definition and calculation rules of node cost function

[0048] Define the node cost function g(R), which is used to determine the cost from the starting point to the corresponding node P R (x R ,y R ) and is based on the flight distance, penalty costs for crossing obstacles or no-fly zones;

[0049] Among them, node P R (x R ,y R ) refers to any node from the starting point to the target point;

[0050] The penalty cost for crossing an obstacle is a value pre-assigned based on the type, size, degree of danger of the obstacle, and the performance of the drone. The penalty cost for a no-fly zone is a value preset based on the level of security sensitivity of the no-fly zone, which is divided into three levels: high, medium, and low.

[0051] If the node P R There are obstacles in the cell;

[0052] Then: g(R)=g(R0)+d(R0,R)+p1;

[0053] Among them, g(R0) is the cost of the parent node, d(R0, R) is the flight distance from the parent node to the current node, and p1 is the penalty value for crossing obstacles;

[0054] Among them, for each node P except the starting point R , its parent node refers to the node P in the current search path R It is extended from this node, that is, the drone reaches the current node from the parent node;

[0055] and ;

[0056] If the node P R The cell is a no-fly zone;

[0057] Then: g(R)=g(R0)+d(R0,R)+p2;

[0058] Among them, p2 is the penalty value for entering the no-fly zone;

[0059] If there is no special case, that is, node P R There are no obstacles in the cell, and it is not a no-fly zone;

[0060] Then: g(R)=g(R0)+d(R0,R);

[0061] Step B2: Setting and calculating the heuristic function

[0062] Define the heuristic function h(R); it is used to determine the R (x R ,y R ) to the target point P B (x B ,y B )’s estimated cost;

[0063] Among them, Euclidean distance is used as a heuristic estimate, specifically:

[0064] ;

[0065] Step B3: Path search process based on cost function and heuristic function

[0066] StepB3.1, set the starting point P A Join the open list;

[0067] StepB3.2, when the open list is not empty, select the node with the smallest cost function value f(R) from the list;

[0068] Among them, the cost function value is: f(R)=g(R)+h(R);

[0069] StepB3.3, if node P R The target point P B , it means the search is successful, and then the path from the starting point to the target point is constructed by backtracking all the corresponding parent nodes to determine the feasible path;

[0070] If the node P R Not the target point P B , the node P R Move from the open list to the closed list and expand its adjacent nodes, and record its adjacent nodes as P RL ;

[0071] For the adjacent node P RL :

[0072] If P RL If the vehicle is not in the closed list and is not in the no-fly zone and has no obstacles or the cost of crossing the obstacles is acceptable, then calculate its cost function f(RL);

[0073] If P RL If it is not in the open list, add it to the open list;

[0074] If P RL If it is already in the list, update its cost function value and parent node.

[0075] Repeat the above steps until the target point is found or the list is empty, where an empty list indicates that the path search has failed.

[0076] As a further solution of the present invention: wherein, the path optimization adopts a path smoothing algorithm, and the path smoothing algorithm is based on a B-spline curve smoothing method.

[0077] As a further solution of the present invention: the path optimization method is as follows:

[0078] The nodes on the feasible path are used as control points of the B-spline curve;

[0079] The nodes on the feasible path are grouped into a path node sequence Pg, where g = 1, 2, ... z;

[0080] Then construct a cubic B-spline curve:

[0081] , where is the cubic B-spline basis function, and its calculation formula is:

[0082] ;

[0083] Where j = 1, 2, 3, t g is the node parameter of the path node sequence, which is determined uniformly according to the distribution of the path nodes. The value range of u is 0 to 1, and different u values ​​correspond to different points on the B-spline curve;

[0084] When the value of u is 0, the starting point of the corresponding curve is the starting point of the drone path in the path planning scenario;

[0085] When the value of u is 1, the end point of the corresponding curve is the target point of the UAV path in the path planning scenario;

[0086] By adjusting the node parameters corresponding to the B-spline curve, the curve can be kept close to the original path nodes while reducing the curvature change and total length of the path, thereby obtaining the optimized path.

[0087] As a further solution of the present invention: the optimized path is also verified for safety, to check whether there is a risk of collision or crossing the zone between the path and obstacles or no-fly zones;

[0088] Specifically:

[0089] On the optimized path, select the corresponding node P W (x W ,y W ), where w represents the node number on the optimized path;

[0090] Then get F ij The value is 1 and Q ij When the value is 1, the corresponding cell center coordinates (x ij ,y ij );

[0091] Then pass:

[0092] ;

[0093] Calculate the corresponding node P W Distance D from obstacles or no-fly zones wij , and then select the distance D with the smallest value wij and mark it as Dmin ;

[0094] Then D min The preset safety time threshold DY is compared:

[0095] If D min ≥DY, it is considered that there is no collision risk at the Wth node on the optimized path;

[0096] If D min <DY, it is considered that the Wth node on the optimized path has a collision risk.

[0097] Beneficial effects of the present invention:

[0098] Accurately adapt to the characteristics of drones: By determining the side length of discrete cells based on the drone's minimum turning radius and the effective range of the sensor, the drone's safe flight and effective perception in each cell are ensured, so that path planning closely fits the drone's actual performance, avoiding flight risks and perception blind spots caused by unreasonable planning, and improving flight safety and reliability of mission execution.

[0099] Comprehensive multi-source geographic information: Collect and integrate multi-source geographic information such as terrain height data, obstacle distribution information, and no-fly zone information to provide a comprehensive and accurate environmental data basis for path planning. It can effectively cope with the complex and changeable cruising area environment and reduce flight accidents and mission interruptions caused by unknown geographical factors.

[0100] Optimized path search strategy: It adopts a graph search-based algorithm and carefully designs a cost function that includes the flight distance, the penalty cost of crossing obstacles and no-fly zones, and a heuristic function based on Euclidean distance. It can efficiently search for feasible paths from the starting point to the target point on the discretized area graph, taking into account the feasibility, safety and efficiency of the path, quickly finding a relatively optimized initial path, and reducing the path search time and computing resource consumption.

[0101] Effective path optimization capability: Use a smoothing method based on B-spline curves to optimize the path. By adjusting the node parameters, the curve is kept close to the original path nodes while reducing the path curvature change and total length, reducing the energy consumption and mechanical wear of the drone during flight, extending the drone's flight time and service life, and improving the overall mission execution efficiency and economy.

[0102] Rigorous safety verification: Safety verification is performed on the optimized path. By accurately calculating the distance between the path nodes and obstacles or no-fly zones and comparing them with the preset safety threshold, potential collision risks and cross-zone risks can be discovered in a timely manner, ensuring the relative safety of the flight path, and ensuring the safety and stability of the UAV and the surrounding environment, providing a solid guarantee for the smooth completion of various cruise missions.

[0103] Wide data applicability: The sources of geographic information data include satellite images and geographic information system databases, which can obtain large-area, high-precision geographic information and are suitable for various large-scale cruising scenarios. Whether it is land, sea or complex terrain areas, it can provide sufficient data support for drone path planning, expanding the application scope and scene adaptability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] The present invention will be further described below in conjunction with the accompanying drawings.

[0105] Figure 1 It is a flow chart of the UAV path planning method based on large-scale cruising of the present invention.

[0106] Figure 2 It is a flow chart of the steps of regional discretization in the UAV path planning method based on large-scale cruising of the present invention.

[0107] Figure 3 It is a flow chart of the steps of searching for feasible paths in the path planning method for unmanned aerial vehicles based on large-scale cruising of the present invention. DETAILED DESCRIPTION

[0108] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0109] Embodiment 1

[0110] See also Figure 1 , Figure 2 and Figure 3 As shown, the present invention is a UAV path planning method based on large-scale cruising, comprising the following steps:

[0111] Step 1: Geographic information collection:

[0112] Obtain detailed geographic information data of the target cruising area, including terrain height data, obstacle distribution information and no-fly zone information;

[0113] In this embodiment, the data sources are satellite images and geographic information system databases;

[0114] Step 2: Geographic Information Processing:

[0115] The geographic information data is integrated into a unified coordinate system by adopting the geodetic coordinate system and converting it into a plane rectangular coordinate system for local calculation. The conversion formula is as follows:

[0116] ;

[0117] Where L is the longitude of the acquisition, B is the latitude of the acquisition, L0 and B0 are the longitude and latitude of the regional reference, x and y are the coordinate values ​​after the longitude and latitude conversion, respectively, and k1 and k2 are the scaling factors predetermined according to the earth's curvature and the regional range;

[0118] Step 3: Regional discretization:

[0119] StepA1, divide the target cruising area into several discrete cells;

[0120] Among them, the side length of the cell is set to a;

[0121] And the value of a is determined according to the minimum turning radius of the drone and the effective range of the sensor to ensure that the drone can fly safely and effectively perceive the environment in each cell;

[0122] That is, a=min{Rmin, Seff}, where Rmin refers to the minimum turning radius of the drone and Seff refers to the effective range of the sensor;

[0123] StepA2, mark each cell as C ij , i=1, 2, ...m, j=1, 2, ...n, where m and n are the number of cells in the region in the x and y directions, respectively;

[0124] Then through:

[0125] ;

[0126] Calculate each cell C ij The center coordinates (x ij ,y ij ):

[0127] where x min and min is the coordinate value of the lower left corner of the area, which represents the minimum coordinate of the target cruising area;

[0128] StepA3, at the same time, through Q ij Mark whether there are obstacles in each cell;

[0129] When cell C ij If there is an obstacle, let Q ij The value of is 1;

[0130] When cell C ij If there is no obstacle, then let Q ij The value of is 0;

[0131] And through F ij Mark whether each cell belongs to a no-fly zone;

[0132] When cell C ij belongs to a no-fly zone, then F ij The value of is 1;

[0133] When cell C ij It belongs to the flyable area, then let F ij The value of is 0;

[0134] Step 4: Setting the starting point and target point:

[0135] Determine the starting point and target point of the drone according to the flight mission requirements, and determine the cells where they are located;

[0136] The starting point and target point of the drone are marked as P A (x A ,y A ) and P B (x B ,y B ), and mark the cells where the starting point and the target point are located as C A and C B ;

[0137] Step 5: Search for feasible paths:

[0138] A graph search-based algorithm is used to search for a feasible path from the starting point to the target point on a discretized area graph;

[0139] The specific method is as follows:

[0140] StepB1. Define the cost function g(R) of the node:

[0141] It is used to determine the distance from the starting point to the corresponding node P. R (x R ,y R ) and is based on the flight distance, penalty costs for crossing obstacles or no-fly zones;

[0142] Among them, node P R (x R ,y R ) refers to any node from the starting point to the target point;

[0143] The penalty cost for crossing an obstacle is a value pre-assigned based on the type, size, degree of danger of the obstacle, and the performance of the drone. The penalty cost for a no-fly zone is a value preset based on the level of security sensitivity of the no-fly zone, which is divided into three levels: high, medium, and low.

[0144] If the node P R There are obstacles in the cell;

[0145] Then: g(R)=g(R0)+d(R0,R)+p1;

[0146] Among them, g(R0) is the cost of the parent node, d(R0, R) is the flight distance from the parent node to the current node, and p1 is the penalty value for crossing obstacles;

[0147] Among them, for each node P except the starting point R , its parent node refers to the node P in the current search path R It is extended from this node, that is, the drone reaches the current node from the parent node;

[0148] and ;

[0149] If the node P R The cell is a no-fly zone;

[0150] Then: g(R)=g(R0)+d(R0,R)+p2;

[0151] Among them, p2 is the penalty value for entering the no-fly zone;

[0152] If there is no special case, that is, node P R There are no obstacles in the cell, and it is not a no-fly zone;

[0153] Then: g(R)=g(R0)+d(R0,R);

[0154] StepB2, define the heuristic function h(R);

[0155] It is used to determine the R (x R ,y R ) to the target point P B (x B ,y B )’s estimated cost;

[0156] Among them, Euclidean distance is used as a heuristic estimate, specifically:

[0157] ;

[0158] Step B3, path search:

[0159] StepB3.1, set the starting point P A Join the open list;

[0160] StepB3.2, when the open list is not empty, select the node with the smallest cost function value f(R) from the list;

[0161] Among them, the cost function value is: f(R)=g(R)+h(R);

[0162] StepB3.3, if node P R The target point P B , it means the search is successful, and then the path from the starting point to the target point is constructed by backtracking all the corresponding parent nodes to determine the feasible path;

[0163] If the node P R Not the target point P B , the node P R Move from the open list to the closed list and expand its adjacent nodes, and record its adjacent nodes as P RL ;

[0164] For the adjacent node P RL :

[0165] If P RL If the vehicle is not in the closed list and is not in the no-fly zone and has no obstacles or the cost of crossing the obstacles is acceptable, then calculate its cost function f(RL);

[0166] If P RL If it is not in the open list, add it to the open list;

[0167] If P RL If it is already in the list, update its cost function value and parent node.

[0168] Repeat the above steps until the target point is found or the list is empty, where an empty list indicates that the path search has failed;

[0169] Step 6: Path optimization:

[0170] Optimize the searched feasible path to reduce the path length and flight energy consumption;

[0171] Wherein, the path optimization adopts a path smoothing algorithm, which is based on a smoothing method of a B-spline curve in this embodiment;

[0172] The path optimization method is as follows:

[0173] The nodes on the feasible path are used as control points of the B-spline curve;

[0174] The nodes on the feasible path are grouped into a path node sequence Pg, where g = 1, 2, ... z;

[0175] Then construct a cubic B-spline curve:

[0176] , where is the cubic B-spline basis function, and its calculation formula is:

[0177] ;

[0178] Where j = 1, 2, 3, t g is the node parameter of the path node sequence, which is determined uniformly according to the distribution of the path nodes. The value range of u is 0 to 1, and different u values ​​correspond to different points on the B-spline curve;

[0179] When the value of u is 0, the starting point of the corresponding curve is the starting point of the drone path in the path planning scenario;

[0180] When the value of u is 1, the end point of the corresponding curve is the target point of the UAV path in the path planning scenario;

[0181] By adjusting the node parameters corresponding to the B-spline curve, the curve can be kept close to the original path nodes while reducing the curvature change and total length of the path, thereby obtaining the optimized path.

[0182] This embodiment obtains detailed geographic information data including terrain height data, obstacle distribution information, and no-fly zone information from satellite images and geographic information system databases, and integrates them into a unified coordinate system, thereby providing a comprehensive and accurate environmental basis for subsequent path planning, helping to plan a flight path that fits the actual environment and reducing flight risks caused by missing or inaccurate information; the target cruising area is divided by determining the side length of discrete cells based on the minimum turning radius of the drone and the effective range of the sensor, which can ensure the safe flight and effective perception of the drone in each cell, fully consider the performance characteristics of the drone itself, avoid the situation where the drone exceeds the maneuverability range or has a perception blind spot due to unreasonable area division, and improve flight safety and mission execution reliability; adopts a graph search-based algorithm, and carefully designs a cost function and a heuristic function, comprehensively considers multiple factors such as flight distance, penalty costs for crossing obstacles and no-fly zones to determine the actual flight cost, and uses Euclidean distance for estimated cost calculation, which can efficiently search for a feasible path from the starting point to the target point on the discretized area map, while taking into account the feasibility and safety of the path, improving the path search efficiency and reducing unnecessary computing resource consumption; uses a B-based algorithm The spline curve smoothing method is used to optimize the path, and the nodes on the feasible path are used as control points to construct the curve. By adjusting the node parameters, the curve is made close to the original path nodes while effectively reducing the curvature change and total length of the path, thereby reducing the UAV flight energy consumption, reducing mechanical wear, and extending the UAV's flight time and service life, which has a positive effect on the economy and efficiency of the overall mission execution.

[0183] Embodiment 2

[0184] As the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the difference between the technical solution of this embodiment and the first embodiment is that the safety of the optimized path is also verified in this embodiment to check whether there is a collision risk or a risk of crossing the zone between the path and the obstacle or the no-fly zone;

[0185] Specifically:

[0186] On the optimized path, select the corresponding node P W (x W ,y W ), where w represents the node number on the optimized path;

[0187] Then get F ij The value is 1 and Q ij When the value is 1, the corresponding cell center coordinates (x ij ,y ij );

[0188] Then pass:

[0189] ;

[0190] Calculate the corresponding node P W Distance D from obstacles or no-fly zones wij , and then select the distance D with the smallest value wij and mark it as D min ;

[0191] Then D min The preset safety time threshold DY is compared:

[0192] If D min ≥DY, it is considered that there is no collision risk at the Wth node on the optimized path;

[0193] If D min <DY, it is considered that the Wth node on the optimized path has a collision risk.

[0194] Based on the first embodiment, this embodiment further adds a safety verification link for the optimized path. By accurately calculating the distance between the path node and the obstacle or no-fly zone and comparing it with the preset safety threshold, it can timely and accurately determine whether the path has a collision risk or a cross-zone risk. This measure greatly enhances the safety guarantee of path planning, allowing drones to more effectively avoid flight accidents caused by unnoticed potential dangers during large-scale cruise missions, ensuring that flight missions can be completed safely and stably, especially suitable for cruise scenarios in complex environments or with extremely high safety requirements.

[0195] Embodiment 3

[0196] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0197] This embodiment combines the solutions of embodiment one and embodiment two, and integrates the advantages of embodiment one in terms of geographic information utilization, regional division, feasible path search and path optimization, while having the advantage of embodiment two in terms of safety verification of the optimized path. This combination makes the drone path planning method more complete and comprehensive, which can not only efficiently plan a high-quality flight path that meets the performance of the drone, but also ensure the relative safety of the entire flight process through strict safety verification, and fully meet the multiple requirements of large-scale cruise missions for path planning accuracy, efficiency and safety, and can show good applicability and reliability in various practical application scenarios.

[0198] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0199] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A UAV path planning method based on large-scale cruising, characterized in that: The following steps are involved: Step 1: Geographic information collection: Obtain geographic information data of the target cruising area, including terrain height data, obstacle distribution information, and no-fly zone information; Step 2: Geographic Information Processing: Integrate geographic information data into a unified coordinate system; Step 3: Regional discretization: The side length of the discrete cell is determined based on the minimum turning radius of the drone and the effective range of the sensor. Then, the target cruising area is divided into multiple cells based on its side length. Then, the center coordinates of each cell are calculated based on the coordinates of the lower left corner of the area. Then, the cells with obstacles and no-fly zones are marked. The specific method of regional discretization is as follows: StepA1, divide the target cruising area into several discrete cells; Among them, the side length of the cell is set to a; And the value of a is determined according to the minimum turning radius of the drone and the effective range of the sensor; That is, a=min{Rmin, Seff}, where Rmin refers to the minimum turning radius of the drone and Seff refers to the effective range of the sensor; StepA2, mark each cell as C ij , i=1, 2, ...m, j=1, 2, ...n, where m and n are the number of cells in the region in the x and y directions, respectively; Then through: ； Calculate each cell C ij The center coordinates (x ij ,y ij ): where x min and min is the coordinate value of the lower left corner of the area, which represents the minimum coordinate of the target cruising area; StepA3, at the same time, through Q ij Mark whether there are obstacles in each cell; When cell C ij If there is an obstacle, let Q ij The value of is 1; When cell C ij If there is no obstacle, then let Q ij The value of is 0; And through F ij Mark whether each cell belongs to a no-fly zone; When cell C ij belongs to a no-fly zone, then F ij The value of is 1; When cell C ij It belongs to the flyable area, then let F ij The value of is 0; Step 4: Setting the starting point and target point: Determine the starting point and target point of the drone according to the flight mission requirements, and determine the cells where they are located; Step 5: Search for feasible paths: A graph search-based algorithm is used to search for a feasible path from the starting point to the target point on a discretized area graph; Step 6: Path optimization: The feasible path obtained by searching is optimized, and the optimized path is obtained.

2. The method for UAV path planning based on large-scale cruising according to claim 1 is characterized in that: The geographic information processing method is to use the geodetic coordinate system and convert it into a plane rectangular coordinate system for local calculation. The conversion formula is as follows: ； Where L is the longitude of the acquisition, B is the latitude of the acquisition, L0 and B0 are the longitude and latitude of the regional reference, x and y are the coordinate values ​​after the longitude and latitude conversion, respectively, and k1 and k2 are the scaling factors predetermined according to the earth's curvature and the regional range.

3. The method for UAV path planning based on large-scale cruising according to claim 1 is characterized in that: The starting point and target point are set as follows: The starting point and target point of the drone are marked as P A (x A ,y A ) and P B (x B ,y B ), and mark the cells where the starting point and the target point are located as C A and C B .

4. The method for UAV path planning based on large-scale cruising according to claim 3 is characterized in that: The feasible path search method is as follows: StepB1. Define the cost function g(R) of the node: It is used to determine the distance from the starting point to the corresponding node P R (x R ,y R ) based on the actual flight costs of the aircraft, and based on the flight distance, penalty costs for crossing obstacles or no-fly zones; Among them, node P R (x R ,y R ) refers to any node from the starting point to the target point; If the node P R There are obstacles in the cell; Then: g(R)=g(R0)+d(R0,R)+p1; Among them, g(R0) is the cost of the parent node, d(R0, R) is the flight distance from the parent node to the current node, and p1 is the penalty value for crossing obstacles; and ; If the node P R The cell is a no-fly zone; Then: g(R)=g(R0)+d(R0,R)+p2; Among them, p2 is the penalty value for entering the no-fly zone; If the node P R There are no obstacles in the cell, and it is not a no-fly zone; Then: g(R)=g(R0)+d(R0,R); StepB2, define the heuristic function h(R); It is used to determine the R (x R ,y R ) to the target point P B (x B ,y B )’s estimated cost; Among them, Euclidean distance is used as a heuristic estimate, specifically: ; Step B3, path search: StepB3.1, set the starting point P A Join the open list; StepB3.2, when the open list is not empty, select the node with the smallest cost function value f(R) from the list; Among them, the cost function value is: f(R)=g(R)+h(R); StepB3.3, if node P R The target point P B , it means the search is successful, and then the path from the starting point to the target point is constructed by backtracking all the corresponding parent nodes to determine the feasible path.

5. The method for UAV path planning based on large-scale cruising according to claim 4 is characterized in that: in, The penalty cost for crossing an obstacle is a value pre-assigned based on the type, size, degree of danger of the obstacle, and the performance of the drone. The penalty cost for a no-fly zone is a value preset based on the level of security sensitivity of the no-fly zone, which is divided into three levels: high, medium, and low.

6. The method for UAV path planning based on large-scale cruising according to claim 4 is characterized in that: In StepB3.3, if node P R Not the target point P B , the node P R Move from the open list to the closed list and expand its adjacent nodes, and record its adjacent nodes as P RL ; For the adjacent node P RL : If P RL If the vehicle is not in the closed list and is not in the no-fly zone and has no obstacles or the cost of crossing the obstacles is acceptable, then calculate its cost function f(RL); If P RL If it is not in the open list, add it to the open list; If P RL If it is already in the list, update its cost function value and parent node; Repeat the above steps until the target point is found or the list is empty, where an empty list indicates that the path search has failed.

7. The method for UAV path planning based on large-scale cruising according to claim 3 is characterized in that: in, Path optimization uses a path smoothing algorithm based on a B-spline curve smoothing method.

8. The method for UAV path planning based on large-scale cruising according to claim 7, characterized in that: The path optimization method is as follows: The nodes on the feasible path are used as control points of the B-spline curve; The nodes on the feasible path are grouped into a path node sequence Pg, where g = 1, 2, ... z; Then construct a cubic B-spline curve: , where is the cubic B-spline basis function, and its calculation formula is: ; Where j = 1, 2, 3, t g is the node parameter of the path node sequence, which is determined uniformly according to the distribution of the path nodes. The value range of u is 0 to 1, and different u values ​​correspond to different points on the B-spline curve; When the value of u is 0, the starting point of the corresponding curve is the starting point of the drone path in the path planning scenario; When the value of u is 1, the end point of the corresponding curve is the target point of the UAV path in the path planning scenario; By adjusting the node parameters corresponding to the B-spline curve, the curvature change and the total length of the path are reduced, and the optimized path is obtained.

9. The method for UAV path planning based on large-scale cruising according to claim 1, characterized in that: The optimized path is also verified for safety, checking whether there is a risk of collision or overrunning between the path and obstacles or no-fly zones; Specifically: On the optimized path, select the corresponding node P W (x W ,y W ), where w represents the node number on the optimized path; Then get F ij The value is 1 and Q ij When the value is 1, the corresponding cell center coordinates (x ij ,y ij ); Then pass: Calculate the corresponding node P W Distance D from obstacles or no-fly zones wij , and then select the distance D with the smallest value wij and mark it as D min ; Then D min The preset safety time threshold DY is compared: If D min ≥DY, it is considered that there is no collision risk at the Wth node on the optimized path; If D min <DY, it is considered that the Wth node on the optimized path has a collision risk.

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