Lake surface mixed path planning method and device

Abstract

The invention discloses a lake surface mixed path planning method and device. The method comprises the following steps: acquiring first grid data of a lake surface to be planned and a main road route; acquiring a first starting point and a first ending point input by a user; the first grid data is obtained by performing grid division on the lake surface for a plurality of times; judging whether the first starting point and the first ending point are on the main road route or not; if yes, obtaining a first optimal path from a first starting point to a first ending point through a first path planning algorithm in combination with the main road route; otherwise, obtaining a second optimal path from the first starting point or the first end point except the main road route to the main road route through a second path planning algorithm in combination with the first grid data; and in combination with the second optimal path, through the first path planning algorithm, obtaining a third optimal path on the main road route. According to the invention, the efficiency of large-area lake surface path planning can be improved, and the superiority of the path planning result is ensured.

Description

Technical Field

[0001] The present application relates to the field of waterway planning, and in particular to a method and device for planning a hybrid path on a lake. Background Art

[0002] Due to the disadvantages of navigation on large lakes, such as many obstacles, narrow waterways and a relatively scattered lake surface, existing technologies use conventional route navigation or remote sensing technology to collect lake surface data, divide the lake surface into grids, and use heuristic algorithms such as A-Star for path planning.

[0003] Among them, conventional route navigation often cannot cover all the paths involved in the navigation problem of a large lake in detail, so it is easy for the actually planned path to fail to reach the optimal path. Even if the existing route can cover all the paths on the entire lake in detail, the route structure is complex due to the large area of ​​the lake, and the existing algorithm often cannot give the path planning results in time. The use of remote sensing technology to collect the lake surface for grid division will also result in too many grids due to the large area of ​​the lake, which will further reduce the efficiency of path planning.

[0004] Therefore, how to improve the efficiency of path planning while ensuring the superiority of the path planning results is a difficult problem that needs to be solved in the current large-scale lake navigation problem. Summary of the invention

[0005] The present application provides a lake surface hybrid path planning method and device to solve the technical problems of low path planning efficiency and poor path planning results when navigating on a large lake surface.

[0006] In order to solve the above technical problems, in a first aspect, an embodiment of the present application provides a lake surface hybrid path planning method, comprising:

[0007] Acquire the first grid data of the lake surface to be planned and the main road route; acquire the first starting point and the first end point input by the user; the first grid data is acquired by dividing the lake surface into grids several times;

[0008] Determining whether the first starting point and the first end point are on the main route;

[0009] If yes, obtaining a first optimal path from the first starting point to the first end point by using a first path planning algorithm in combination with the main road route;

[0010] Otherwise, in combination with the first grid data, a second optimal path from the first starting point or the first end point outside the main road route to the main road route is obtained through a second path planning algorithm; in combination with the second optimal path, a third optimal path on the main road route is obtained through the first path planning algorithm.

[0011] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by combining two different path planning algorithms, the path planning efficiency is improved. When the starting point and the end point are both on the main road route, since the main road route usually omits small branch paths, the main road route is usually a low-complexity and clear graph structure. At this time, the optimal path between two nodes in the graph structure can be directly determined by the first path planning algorithm; and when at least one point is not on the main road route, the second path planning algorithm is first used for planning, and the optimal path from the point to the main road route is obtained in combination with the divided grids. When all the currently planned paths have reached the main road route, the first path planning algorithm is used to obtain the optimal path on the main road route, thereby realizing the planning of small paths, while improving the path planning efficiency and ensuring the superiority of the path planning results.

[0012] In some embodiments of the first aspect of the present application, the first grid data is obtained by performing grid division on the lake surface several times, including:

[0013] Calculating a minimum bounding box of the lake surface, dividing the minimum bounding box according to a first size, obtaining a plurality of first grids, and superimposing the first grids with the lake surface to obtain a corresponding first lake surface;

[0014] Divide the minimum bounding box according to a second size to obtain a plurality of second grids; wherein the second size is smaller than the first size;

[0015] In combination with the first lake surface, a third grid is screened from the second grid to obtain the first grid data.

[0016] Compared with the prior art, the above embodiment has the following beneficial effects: by dividing the lake surface into grids of different sizes for multiple times, the accuracy of path planning is effectively improved. Small-sized grids can finely depict complex areas to achieve refined identification of lake surface areas, while large-sized grids help to quickly overview the overall lake surface structure, achieving a balance between global planning and local details, which not only enhances the adaptability of subsequent algorithms, but also improves the planning efficiency of subsequent algorithms.

[0017] In some embodiments of the first aspect of the present application, the step of combining the first lake surface and selecting a third grid from the second grid to obtain the first grid data includes:

[0018] Determine the second grid corresponding to the area to which each of the third grids belongs;

[0019] Superimposing each of the third grids with the first lake surface corresponding to the second grid to which it belongs, to obtain an overlapping area of ​​each of the third grids and the corresponding first lake surface;

[0020] The third grids whose overlapping areas exceed a preset threshold are screened to obtain the first grid data.

[0021] Compared with the prior art, the above embodiment has the following beneficial effects: by screening the overlapping area of ​​the third grid, the accuracy and validity of the grid data are ensured. The screening process can effectively reduce redundant grid data and ensure that the grid data used for path planning is closer to the actual lake environment, thereby improving the rationality and accuracy of the planned path.

[0022] In some embodiments of the first aspect of the present application, after acquiring the first grid data, the method further includes:

[0023] Dividing the minimum bounding box according to a third size to obtain a plurality of fourth grids; wherein the first size is an integer multiple of the third size, and the third size is an integer multiple of the second size;

[0024] The first grid data composed of all the third grids in each of the fourth grids are stored separately.

[0025] Compared with the prior art, the above embodiment has the following beneficial effects: the multi-level grid division and data storage method enables the system to flexibly call grid data according to different requirements. By storing grids of different sizes in layers, the efficiency of data management is achieved, the computational burden is reduced, and the rapid response and accurate output in the path planning process are ensured.

[0026] In some embodiments of the first aspect of the present application, the step of combining the first grid data and obtaining a second optimal path from a first starting point or a first end point outside the trunk route to the trunk route through a second path planning algorithm; and combining the second optimal path and obtaining a third optimal path on the trunk route through the first path planning algorithm includes:

[0027] When the first starting point is on the main route, the first starting point is used as the second starting point;

[0028] When the first destination is on the main route, the first destination is used as the second destination;

[0029] When the first starting point is not on the main road route, the intersection of the second optimal path corresponding to the first starting point and the main road route is used as the second starting point;

[0030] When the first destination is not on the main road route, the intersection of the second optimal path corresponding to the first destination and the main road route is used as the second destination;

[0031] The third optimal path from the second starting point to the second end point is obtained by using the first path planning algorithm in combination with the main road route.

[0032] Compared with the prior art, the above embodiment has the following beneficial effects: the optimal path in the main road route is planned by the first planning algorithm, and the optimal path from a point outside the main road route to the main road route is planned based on the grid data by the second planning algorithm. This segmented planning can combine multiple planning algorithms, thereby reducing the problem of low planning efficiency of existing algorithms due to complex waterways in large-area lake planning problems, while ensuring the superiority of the planned path.

[0033] In some embodiments of the first aspect of the present application, the first path planning algorithm is a path planning algorithm for a scenario with a graph structure; the second path planning algorithm is a path planning algorithm for a scenario with a grid structure.

[0034] Compared with the prior art, the above embodiment has the following beneficial effects: the planning algorithm based on the graph structure is suitable for the route area, while the algorithm based on the grid structure is suitable for the complex non-route area on the lake surface. The combination of the two can effectively improve the efficiency and accuracy of the planned path.

[0035] In a second aspect, an embodiment of the present application further provides a lake surface hybrid path planning device, comprising: a data acquisition module, a first judgment module, a first execution module, and a second execution module;

[0036] The data acquisition module is used to acquire the first grid data of the lake surface to be planned and the main road route; acquire the first starting point and the first end point input by the user; the first grid data is acquired by dividing the lake surface into grids several times;

[0037] The first determination module is used to determine whether the first starting point and the first end point are on the main road route;

[0038] The first execution module is used to obtain a first optimal path from the first starting point to the first end point by using a first path planning algorithm in combination with the main road route;

[0039] The second execution module is used to, otherwise, in combination with the first grid data, obtain a second optimal path from the first starting point or the first end point outside the main road route to the main road route through a second path planning algorithm; in combination with the second optimal path, obtain a third optimal path on the main road route through the first path planning algorithm.

[0040] In some embodiments of the second aspect of the present application, the first grid data is obtained by performing grid division on the lake surface several times, including:

[0041] Calculating a minimum bounding box of the lake surface, dividing the minimum bounding box according to a first size, obtaining a plurality of first grids, and superimposing the first grids with the lake surface to obtain a corresponding first lake surface;

[0042] Divide the minimum bounding box according to a second size to obtain a plurality of second grids; wherein the second size is smaller than the first size;

[0043] In combination with the first lake surface, a third grid is screened from the second grid to obtain the first grid data.

[0044] In some embodiments of the second aspect of the present application, the combining the first lake surface and selecting a third grid from the second grid to obtain the first grid data includes:

[0045] Determine the second grid corresponding to the area to which each of the third grids belongs;

[0046] Superimposing each of the third grids with the first lake surface corresponding to the second grid to which it belongs, to obtain an overlapping area of ​​each of the third grids and the corresponding first lake surface;

[0047] The third grids whose overlapping areas exceed a preset threshold are screened to obtain the first grid data.

[0048] In some embodiments of the second aspect of the present application, after acquiring the first grid data, the method further includes:

[0049] Dividing the minimum bounding box according to a third size to obtain a plurality of fourth grids; wherein the first size is an integer multiple of the third size, and the third size is an integer multiple of the second size;

[0050] The first grid data composed of all the third grids in each of the fourth grids are stored separately.

[0051] In some embodiments of the second aspect of the present application, the step of combining the first grid data and obtaining a second optimal path from a first starting point or a first end point outside the trunk road route to the trunk road route through a second path planning algorithm; and combining the second optimal path and obtaining a third optimal path on the trunk road route through the first path planning algorithm includes:

[0052] When the first starting point is on the main route, the first starting point is used as the second starting point;

[0053] When the first destination is on the main route, the first destination is used as the second destination;

[0054] When the first starting point is not on the main road route, the intersection of the second optimal path corresponding to the first starting point and the main road route is used as the second starting point;

[0055] When the first destination is not on the main road route, the intersection of the second optimal path corresponding to the first destination and the main road route is used as the second destination;

[0056] The third optimal path from the second starting point to the second end point is obtained by using the first path planning algorithm in combination with the main road route.

[0057] In some embodiments of the second aspect of the present application, the first path planning algorithm is a path planning algorithm for a scenario with a graph structure; the second path planning algorithm is a path planning algorithm for a scenario with a grid structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic flow chart of a lake surface hybrid path planning method provided in some embodiments of the present application;

[0059] Figure 2 This is a schematic diagram of the minimum bounding box of a large lake surface;

[0060] Figure 3 A schematic diagram of a path planning result obtained by a lake surface hybrid path planning method provided in some embodiments of the present application;

[0061] Figure 4 This is a schematic diagram of the structure of a lake surface hybrid path planning device provided in some embodiments of the present application. DETAILED DESCRIPTION

[0062] Since conventional route navigation often cannot cover all the paths involved in large-area lake navigation problems in detail, it is easy for the actually planned path to fail to reach the optimal path. Even if the existing route can cover all the paths on the entire lake in detail, the route structure is complex due to the large area of ​​the lake, and the existing algorithm often cannot give the path planning results in time. The use of remote sensing technology to collect lake surface data for grid division will also result in too many grids due to the large area of ​​the lake, further reducing the efficiency of path planning.

[0063] In order to solve the above technical problems, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0064] Embodiment 1

[0065] Please refer to Figure 1 , a lake surface hybrid path planning method provided by an embodiment of the present application, including S10 to S40, specifically:

[0066] S10: Obtain first grid data of the lake surface to be planned and a main road route; obtain a first starting point and a first end point input by a user; the first grid data is obtained by dividing the lake surface into grids several times.

[0067] Further, in some embodiments of the present application, the first grid data is obtained by performing several grid divisions on the lake surface, including:

[0068] Calculating a minimum bounding box of the lake surface, dividing the minimum bounding box according to a first size, obtaining a plurality of first grids, and superimposing the first grids with the lake surface to obtain a corresponding first lake surface;

[0069] Divide the minimum bounding box according to a second size to obtain a plurality of second grids; wherein the second size is smaller than the first size;

[0070] In combination with the first lake surface, a third grid is screened from the second grid to obtain the first grid data.

[0071] By dividing the lake surface into grids of different sizes multiple times, the accuracy of path planning is effectively improved. Small-sized grids can finely depict complex areas to achieve refined identification of lake surface areas, while large-sized grids help to quickly overview the overall lake surface structure, achieving a balance between global planning and local details, which not only enhances the adaptability of subsequent algorithms, but also improves the planning efficiency of subsequent algorithms.

[0072] Further, in some embodiments of the present application, the step of combining the first lake surface and selecting a third grid from the second grid to obtain the first grid data includes:

[0073] Determine the second grid corresponding to the area to which each of the third grids belongs;

[0074] Superimposing each of the third grids with the first lake surface corresponding to the second grid to which it belongs, to obtain an overlapping area of ​​each of the third grids and the corresponding first lake surface;

[0075] The third grids whose overlapping areas exceed a preset threshold are screened to obtain the first grid data.

[0076] By screening the overlapping area of ​​the third grid, the accuracy and validity of the grid data are ensured. This screening process can effectively reduce redundant grid data and ensure that the grid data used for path planning is closer to the actual lake environment, thereby improving the rationality and accuracy of the planned path.

[0077] Furthermore, in some embodiments of the present application, after obtaining the first grid data, the method further includes:

[0078] Dividing the minimum bounding box according to a third size to obtain a plurality of fourth grids; wherein the first size is an integer multiple of the third size, and the third size is an integer multiple of the second size;

[0079] The first grid data composed of all the third grids in each of the fourth grids are stored separately.

[0080] The multi-level grid division and data storage method enables the system to flexibly call grid data according to different needs. By storing grids of different sizes in layers, data management efficiency is achieved, the computational burden is reduced, and fast response and accurate output are ensured during path planning.

[0081] Preferably, in some embodiments of the present application, grid division can be performed in the following manner:

[0082] S11: Obtain water surface data of the lake to be planned, where the water surface data is as follows: Figure 2 As shown, the outer frame is the minimum bounding box of the water surface data. Assume that the minimum bounding box is a rectangle M1 and the water surface data is P1;

[0083] S12: according to the first size (here, the first size can preferably be set by using the longitude and latitude interval of the water surface data in reality as 0.05 degrees), M1 is divided to obtain a plurality of first grids of the first size, assuming that the first grid is a rectangle M2, and then each M2 is superimposed with P1, so as to divide P1 into a plurality of P2 water surface data less than or equal to the number of M2;

[0084] S13: To facilitate subsequent data storage, before dividing according to the second size, first divide M1 according to the third size (here, the third size can be preferably set to 0.005 degrees, that is, the first size is an integer multiple of the third size), and obtain a plurality of fourth grids of the third size, assuming that the fourth grid is a rectangle M3;

[0085] S14: Divide M1 according to the second size (here, the second size can be preferably set to 0.000025 degrees), or divide each M2, or divide M3, so as to obtain multiple second grids of the second size. Assume that the second grid is a rectangle M4, and the first size and the third size are both integer multiples of the second size (in this case, M1 contains multiple M2s, one M2 contains multiple M3s, and one M3 contains multiple M4s);

[0086] S15: Since in an M3, if the actual area corresponding to it is a lake surface, then the M2 to which the M3 belongs will have a corresponding P2 data, so M4 is superimposed with the P2 data corresponding to the M2 to which it belongs, and it is determined whether the ratio of the lake area in the overlapping area to the total area of ​​M4 exceeds the preset value (the preset value can be preferably set to 0.6 here); if it exceeds, it means that there is a valid lake surface in M4, which can be used for subsequent path planning, and the second grid corresponding to M4 is set to the third grid; otherwise, the M4 is eliminated to avoid it being used for subsequent path planning.

[0087] Preferably, in some embodiments of the present application, after the grid division is completed, since one M3 includes multiple M4s, M4s are used as grid data for subsequent path planning and need to be read frequently. Therefore, the grid data can be stored by the following preferred embodiments:

[0088] S16: Treat each M3 as a separate storage file. Each M3 stores the serial number of each M4 it contains. The serial number can be set to a serial number (x, y), that is, x represents that M4 is in the xth row of the M3 area, and y represents that M4 is in the yth column of the M3 area.

[0089] S17: After obtaining the longitude and latitude of the starting point and the end point, or obtaining the serial number of a certain point, the longitude and latitude and serial number of any point can be calculated by the known origin and the longitude and latitude of the origin (here a point of M1 can be set as the origin), combined with the various size information set above, to achieve mutual conversion from grid path to actual longitude and latitude path.

[0090] S20: Determine whether the first starting point and the first end point are on the main road route.

[0091] Preferably, in some embodiments of the present application, before determining whether the first starting point and the first end point are on the main road route, the water surface data of the lake to be planned is first obtained, the main road route is drawn in advance, and then the longitude and latitude information of the main road route is stored in a local database. After the longitude and latitude of the first starting point and the first end point input by the user are obtained, it is determined whether the longitude and latitude of the first starting point and the first end point exist in the longitude and latitude information of the main road route.

[0092] S30: If yes, then using a first path planning algorithm and combining the main road route, a first optimal path from the first starting point to the first end point is obtained.

[0093] Preferably, in some embodiments of the present application, when the longitude and latitude of the first starting point and the first end point both exist in the longitude and latitude information of the main road route, a graph structure path planning algorithm, preferably Dijkstra algorithm (Dijkstra), can be used in combination with the existing main road route to plan the first optimal path from the first starting point to the first end point.

[0094] S40: Otherwise, in combination with the first grid data, a second optimal path from the first starting point or the first end point outside the main road route to the main road route is obtained through a second path planning algorithm; in combination with the second optimal path, a third optimal path on the main road route is obtained through the first path planning algorithm.

[0095] Further, in some embodiments of the present application, the step of combining the first grid data and obtaining a second optimal path from a first starting point or a first end point outside the trunk route to the trunk route through a second path planning algorithm; combining the second optimal path and obtaining a third optimal path on the trunk route through the first path planning algorithm includes:

[0096] When the first starting point is on the main route, the first starting point is used as the second starting point;

[0097] When the first destination is on the main route, the first destination is used as the second destination;

[0098] When the first starting point is not on the main road route, the intersection of the second optimal path corresponding to the first starting point and the main road route is used as the second starting point;

[0099] When the first destination is not on the main road route, the intersection of the second optimal path corresponding to the first destination and the main road route is used as the second destination;

[0100] The third optimal path from the second starting point to the second end point is obtained by using the first path planning algorithm in combination with the main road route.

[0101] The first planning algorithm is used to plan the optimal path in the main road route, and the second planning algorithm is used to plan the optimal path from points outside the main road route to the main road route based on grid data. This segmented planning can combine multiple planning algorithms, thereby reducing the problem of low planning efficiency of existing algorithms due to complex waterways in large-area lake planning problems, while ensuring the superiority of the planned path.

[0102] Furthermore, in some embodiments of the present application, the first path planning algorithm is a path planning algorithm for a scenario with a graph structure; the second path planning algorithm is a path planning algorithm for a scenario with a grid structure.

[0103] The planning algorithm based on graph structure is suitable for route areas, while the algorithm based on grid structure is suitable for complex non-route areas on the lake. The combination of the two can effectively improve the efficiency and accuracy of the planned path.

[0104] Preferably, in some embodiments of the present application, when any point between the first starting point and the first end point is not in the longitude and latitude information of the main route, the optimal path from the first starting point to the first end point is determined by the following preferred embodiments:

[0105] S41: Determine the points that are not in the longitude and latitude information of the main route. For details, please refer to Figure 3 , assuming that the first starting point (i.e. Figure 3 The cross star mark) is not on the main road route (i.e. Figure 3 In the longitude and latitude information of the blue route in FIG. 1 ), the longitude and latitude information of the first starting point is converted into a first serial number through S17;

[0106] S42: Determine each second sequence number corresponding to the grid where the trunk route is located;

[0107] S43: Using the A-Star algorithm (A*), determine the optimal path from the first sequence number to each second sequence number in turn, and select the optimal path with the shortest distance (i.e. Figure 3 The grid with the second serial number corresponding to the red route in the figure is used as the second starting point;

[0108] S44: if the first destination is in the longitude and latitude information of the main road route, then plan the optimal path from the second starting point to the first destination according to the Dijkstra algorithm; otherwise, according to S43, after determining the optimal path from the first destination to the main road route, the grid corresponding to the second sequence number of the optimal path is used as the second destination, and the optimal path from the second starting point to the second destination is obtained by the Dijkstra algorithm;

[0109] S45: merging the optimal path from the first starting point to the second starting point, the optimal path from the second starting point to the second end point, and the optimal path from the second end point to the first end point as the optimal path from the first starting point to the first end point.

[0110] In summary, a hybrid path planning method for lake surface provided in an embodiment of the present application has the following beneficial effects: by combining two different path planning algorithms, the path planning efficiency is improved. When the starting point and the end point are both on the main road route, since the main road route usually omits small branch paths, the main road route is usually a low-complexity and clear graph structure. At this time, the optimal path between the two nodes in the graph structure can be directly determined by the first path planning algorithm; and when at least one point is not on the main road route, the second path planning algorithm is first used for planning, and the optimal path from the point to the main road route is obtained in combination with the divided grid. When all the currently planned paths reach the main road route, the first path planning algorithm is used to obtain the optimal path on the main road route, thereby realizing the planning of small paths, while improving the path planning efficiency, ensuring the superiority of the path planning results.

[0111] Embodiment 2

[0112] refer to Figure 4 , a lake hybrid path planning device provided in an embodiment of the present application, includes: a data acquisition module 11, a first judgment module 12, a first execution module 13 and a second execution module 14.

[0113] Furthermore, in some embodiments of the present application, the data acquisition module 11 is used to obtain the first grid data of the lake surface to be planned and the main road route; obtain the first starting point and the first end point input by the user; the first grid data is obtained by performing several grid divisions on the lake surface; the first judgment module 12 is used to determine whether the first starting point and the first end point are on the main road route; the first execution module 13 is used to, if so, obtain the first optimal path from the first starting point to the first end point through a first path planning algorithm in combination with the main road route; the second execution module 14 is used to, otherwise, obtain the second optimal path from the first starting point or the first end point outside the main road route to the main road route through a second path planning algorithm in combination with the first grid data; and obtain the third optimal path on the main road route through the first path planning algorithm in combination with the second optimal path.

[0114] Furthermore, in some embodiments of the present application, the first grid data is obtained by performing several grid divisions on the lake surface, including: calculating the minimum enclosing box of the lake surface, dividing the minimum enclosing box according to a first size, obtaining several first grids, superimposing the first grids with the lake surface, and obtaining the corresponding first lake surface; dividing the minimum enclosing box according to a second size, and obtaining several second grids; wherein the second size is smaller than the first size; and in combination with the first lake surface, screening a third grid from the second grid to obtain the first grid data.

[0115] Furthermore, in some embodiments of the present application, combining with the first lake surface and screening the third grid from the second grid to obtain the first grid data includes: determining the second grid corresponding to the area to which each of the third grids belongs; superimposing each of the third grids with the first lake surface corresponding to the second grid to which it belongs, to obtain the overlapping area of ​​each of the third grids and the corresponding first lake surface; screening the third grids whose overlapping area exceeds a preset threshold to obtain the first grid data.

[0116] Furthermore, in some embodiments of the present application, after obtaining the first grid data, it also includes: dividing the minimum bounding box according to a third size to obtain a plurality of fourth grids; wherein the first size is an integer multiple of the third size, and the third size is an integer multiple of the second size; and separately storing the first grid data composed of all the third grids in each of the fourth grids.

[0117] Further, in some embodiments of the present application, the second optimal path from the first starting point or the first end point outside the main road route to the main road route is obtained through a second path planning algorithm in combination with the first grid data; in combination with the second optimal path, a third optimal path on the main road route is obtained through the first path planning algorithm, including: when the first starting point is on the main road route, the first starting point is used as the second starting point; when the first end point is on the main road route, the first end point is used as the second end point; when the first starting point is not on the main road route, the intersection of the second optimal path corresponding to the first starting point and the main road route is used as the second starting point; when the first end point is not on the main road route, the intersection of the second optimal path corresponding to the first end point and the main road route is used as the second end point; through the first path planning algorithm, in combination with the main road route, the third optimal path from the second starting point to the second end point is obtained.

[0118] Furthermore, in some embodiments of the present application, the first path planning algorithm is a path planning algorithm for a scenario with a graph structure; the second path planning algorithm is a path planning algorithm for a scenario with a grid structure.

[0119] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention. The lake surface hybrid path planning device provided by the embodiment of the present invention can implement any method item embodiment of the present invention, that is, the lake surface hybrid path planning method provided in embodiment one.

[0120] In summary, a hybrid path planning device for lake surface provided in an embodiment of the present application has the following beneficial effects: by combining two different path planning algorithms, the path planning efficiency is improved. When the starting point and the end point are both on the main road route, since the main road route usually omits small branch paths, the main road route is usually a low-complexity and clear graph structure. At this time, the optimal path between two nodes in the graph structure can be directly determined by the first path planning algorithm; and when at least one point is not on the main road route, the second path planning algorithm is first used for planning, and the optimal path from the point to the main road route is obtained in combination with the divided grid. When all the currently planned paths reach the main road route, the first path planning algorithm is used to obtain the optimal path on the main road route, thereby realizing the planning of small paths, while improving the path planning efficiency, ensuring the superiority of the path planning results.

[0121] Embodiment 3

[0122] Based on the above-mentioned embodiment of the lake surface hybrid path planning method, another embodiment of the present application provides a lake surface hybrid path planning terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the lake surface hybrid path planning method of any embodiment of the present application is implemented.

[0123] Exemplarily, in this embodiment, the computer program can be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the lake hybrid path planning device.

[0124] The lake surface hybrid path planning device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The lake surface hybrid path planning terminal device may include, but is not limited to, a processor and a memory.

[0125] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the lake surface hybrid path planning device, and uses various interfaces and lines to connect the various parts of the entire lake surface hybrid path planning device. The memory may be used to store the computer program and / or module, and the processor implements various functions of the lake surface hybrid path planning device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0126] Embodiment 4

[0127] Based on the above-mentioned embodiment of the lake surface hybrid path planning method, another embodiment of the present application provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the lake surface hybrid path planning method of any embodiment of the present application.

[0128] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0129] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A lake surface hybrid path planning method, characterized in that: include: Obtain the first grid data of the lake surface to be planned and the main road route; Get the first starting point and the first end point input by the user; The first grid data is obtained by dividing the lake surface into grids several times; Determining whether the first starting point and the first end point are on the main route; If yes, obtaining a first optimal path from the first starting point to the first end point by using a first path planning algorithm in combination with the main road route; Otherwise, in combination with the first grid data, a second optimal path from the first starting point or the first end point outside the main road route to the main road route is obtained through a second path planning algorithm; in combination with the second optimal path, a third optimal path on the main road route is obtained through the first path planning algorithm.

2. A lake surface hybrid path planning method as claimed in claim 1, characterized in that: The first grid data is obtained by dividing the lake surface into grids several times, including: Calculating a minimum bounding box of the lake surface, dividing the minimum bounding box according to a first size, obtaining a plurality of first grids, and superimposing the first grids with the lake surface to obtain a corresponding first lake surface; Divide the minimum bounding box according to a second size to obtain a plurality of second grids; wherein the second size is smaller than the first size; In combination with the first lake surface, a third grid is screened from the second grid to obtain the first grid data.

3. A lake surface hybrid path planning method as claimed in claim 2, characterized in that: The step of combining the first lake surface and selecting a third grid from the second grid to obtain the first grid data includes: Determine the second grid corresponding to the area to which each of the third grids belongs; Superimposing each of the third grids with the first lake surface corresponding to the second grid to which it belongs, to obtain an overlapping area of ​​each of the third grids and the corresponding first lake surface; The third grids whose overlapping areas exceed a preset threshold are screened to obtain the first grid data.

4. A lake surface hybrid path planning method as claimed in claim 2, characterized in that: After acquiring the first grid data, the method further includes: Dividing the minimum bounding box according to a third size to obtain a plurality of fourth grids; wherein the first size is an integer multiple of the third size, and the third size is an integer multiple of the second size; The first grid data composed of all the third grids in each of the fourth grids are stored separately.

5. A lake surface hybrid path planning method as claimed in claim 1, characterized in that: combining the first grid data, obtaining a second optimal path from a first starting point or a first end point outside the main road route to the main road route through a second path planning algorithm; In combination with the second optimal path, a third optimal path on the trunk route is obtained by using the first path planning algorithm, including: When the first starting point is on the main route, the first starting point is used as the second starting point; When the first terminal is on the main route, the first terminal is used as the second terminal; When the first starting point is not on the main road route, the intersection of the second optimal path corresponding to the first starting point and the main road route is used as the second starting point; When the first destination is not on the main road route, the intersection of the second optimal path corresponding to the first destination and the main road route is used as the second destination; The third optimal path from the second starting point to the second end point is obtained by using the first path planning algorithm in combination with the main road route.

6. A lake surface hybrid path planning method as claimed in claim 1, characterized in that: The first path planning algorithm is a path planning algorithm used in a scenario with a graph structure; the second path planning algorithm is a path planning algorithm used in a scenario with a grid structure.

7. A lake surface hybrid path planning device, characterized in that: include: A data acquisition module, a first judgment module, a first execution module and a second execution module; The data acquisition module is used to acquire the first grid data of the lake surface to be planned and the main road route; and acquire the first starting point and the first end point input by the user; The first grid data is obtained by dividing the lake surface into grids several times; The first determination module is used to determine whether the first starting point and the first end point are on the main road route; The first execution module is used to obtain a first optimal path from the first starting point to the first end point by combining the first path planning algorithm with the main road route if the first path planning algorithm is used; The second execution module is used to, otherwise, in combination with the first grid data, obtain a second optimal path from the first starting point or the first end point outside the main road route to the main road route through a second path planning algorithm; in combination with the second optimal path, obtain a third optimal path on the main road route through the first path planning algorithm.

8. A lake surface hybrid path planning device as claimed in claim 7, characterized in that: The first grid data is obtained by dividing the lake surface into grids several times, including: Calculating a minimum bounding box of the lake surface, dividing the minimum bounding box according to a first size, obtaining a plurality of first grids, and superimposing the first grids with the lake surface to obtain a corresponding first lake surface; Divide the minimum bounding box according to a second size to obtain a plurality of second grids; wherein the second size is smaller than the first size; In combination with the first lake surface, a third grid is screened from the second grid to obtain the first grid data.

9. A lake surface hybrid path planning device as claimed in claim 8, characterized in that: The step of combining the first lake surface and selecting a third grid from the second grid to obtain the first grid data includes: Determine the second grid corresponding to the area to which each of the third grids belongs; Superimposing each of the third grids with the first lake surface corresponding to the second grid to which it belongs, to obtain an overlapping area of ​​each of the third grids and the corresponding first lake surface; The third grids whose overlapping areas exceed a preset threshold are screened to obtain the first grid data.

10. The lake surface hybrid path planning device according to claim 8, characterized in that: After acquiring the first grid data, the method further includes: Dividing the minimum bounding box according to a third size to obtain a plurality of fourth grids; wherein the first size is an integer multiple of the third size, and the third size is an integer multiple of the second size; The first grid data composed of all the third grids in each of the fourth grids are stored separately.

11. The lake surface hybrid path planning device according to claim 7, characterized in that: combining the first grid data, obtaining a second optimal path from a first starting point or a first end point outside the main road route to the main road route through a second path planning algorithm; In combination with the second optimal path, a third optimal path on the trunk route is obtained by using the first path planning algorithm, including: When the first starting point is on the main route, the first starting point is used as the second starting point; When the first terminal is on the main route, the first terminal is used as the second terminal; When the first starting point is not on the main road route, the intersection of the second optimal path corresponding to the first starting point and the main road route is used as the second starting point; When the first destination is not on the main road route, the intersection of the second optimal path corresponding to the first destination and the main road route is used as the second destination; The third optimal path from the second starting point to the second end point is obtained by using the first path planning algorithm in combination with the main road route.

12. The lake surface hybrid path planning device according to claim 7, characterized in that: The first path planning algorithm is a path planning algorithm used in a scenario with a graph structure; the second path planning algorithm is a path planning algorithm used in a scenario with a grid structure.

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