Water surface vehicle route planning navigation method for step-by-step spatial scale search

Abstract

The invention discloses a step-by-step spatial scale search-based water surface vehicle route planning navigation method, which comprises the following steps: S1, constructing a rasterized electronic chart containing water depth information, and marking non-navigable data influencing the navigation of a water surface vehicle on the electronic chart; s2, a task target from the navigation starting point to the navigation ending point of the water surface vehicle is set in the electronic chart, m layers of sub-grid masks with sequentially reduced grid space scales are constructed on the electronic chart, the front m-1 layers of sub-grid masks sequentially execute grid route chain search of the front and rear layers of accumulated heuristic task targets, and the (m-1) th layer of sub-grid mask outputs a route chain; and S3, performing optimal path search and optimization on the mth layer of sub-grid mask by using the route chain to obtain a water surface vehicle planning route. According to the method, search guidance of front and rear layer accumulated heuristic task targets is realized, and a refined route chain is obtained through quick search of a multi-layer sub-grid mask, so that the search calculation amount and complexity are reduced, and the water surface navigation path planning efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of surface vehicle planning and navigation, and more particularly to a method for surface vehicle route planning and navigation that searches at progressive spatial scales. Background Technology

[0002] Surface vehicles are vehicles that rely on buoyancy or hydrodynamics to navigate on the water surface. They are widely used in transportation, operations, scientific research, and military fields. Surface vehicles are a collective term for ships (including cargo ships and passenger ships), unmanned surface vessels (including unmanned boats and other unmanned maritime vehicles), and naval vessels. With the advancement of the smart ocean strategy, autonomous maritime vehicles such as unmanned boats, unmanned surface vessels, and unmanned underwater vehicles are increasingly being used in marine surveying, environmental monitoring, maritime patrol, and port operations. When surface vehicles navigate in waterways, taking the ocean as an example, the complex and ever-changing marine environment affects route planning and subsequent navigation. For instance, obstacles such as islands (e.g., navigating complex archipelagos or reef areas), glaciers, and reefs can hinder navigation. Various control zones (e.g., military no-navigation zones and ecological protection no-navigation zones) are also established in the ocean. Weather and other factors also influence navigation. Therefore, surface vehicles need to plan routes in advance or in real-time to avoid or bypass these non-navigable areas to facilitate navigation and prevent being trapped in unsafe conditions. Current map navigation path search methods calculate path costs from pixel to pixel from the starting point and progressively find the shortest (optimal) path. These methods perform pixel-by-pixel searches, employing a one-time global search algorithm. When dealing with large-scale sea areas or waterways, they lack macroscopic spatial scale guidance, leading to difficulties in processing massive amounts of data and low search efficiency. Summary of the Invention

[0003] The purpose of this invention is to solve the technical problems pointed out in the background art and provide a method for planning and navigating the route of a surface vehicle by searching at progressively higher spatial scales. This method utilizes multi-layer sub-mesh masks to search for grid route chains at progressively higher spatial resolutions, obtaining progressively more detailed route chains. The route chains of the upper-layer sub-mesh mask provide search guidance for the grid route chains of the lower-layer sub-mesh mask. The multi-layer sub-mesh mask achieves cumulative heuristic search guidance for the task objective, improving the efficiency of grid route chain search. The final sub-mesh mask uses the refined route chains to search for and optimize the optimal path, enabling the rapid acquisition of a refined planned route for the surface vehicle.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A method for route planning and navigation of surface vehicles using a progressive spatial scale search, the method comprising:

[0006] S1. Construct a rasterized electronic nautical chart containing water depth information, and mark non-navigational data that affects the navigation of surface vehicles on the electronic nautical chart;

[0007] S2. Set the mission objective of the surface vehicle's journey from the starting point to the destination on the electronic nautical chart. The electronic chart is constructed with m layers of sub-grid masks of progressively decreasing grid scale. The first m-1 layers of sub-grid masks sequentially perform a grid route chain search based on the cumulative heuristic mission objective of the preceding and following layers. The (m-1)th layer of sub-grid mask outputs the route chain. ;

[0008] S3, using route chains in the m-th layer sub-mesh mask. The optimal path search and optimization are performed to obtain the planned route for the surface vehicle.

[0009] To better implement the present invention, in method S1, the non-navigation data includes absolute obstacle data and conditional obstacle data. The absolute obstacle data includes obstacle data including islands, glaciers, reefs, and offshore platform facilities. The conditional obstacle data includes military restricted navigation zones, ecological protection restricted navigation zones, navigation areas affected by meteorological conditions, navigation areas affected by navigation conditions, and shallow water areas where the water depth is less than the draft of a surface vehicle.

[0010] Preferably, the m-layer sub-mesh mask is divided into upper and lower layers as sub-mesh masks. ~ Sub-grid mask ~ The grid spatial scale decreases sequentially and is constructed according to the hierarchical hierarchy.

[0011] Preferably, in method S2, the sub-mesh mask ~ Each has a corresponding non-navigation factor density threshold. ~ The following is a method for searching the grid route chain of the cumulative heuristic task objective of the first m-1 layer sub-mesh mask:

[0012] S21, In the sub-mesh mask Perform a grid search to achieve the mission objective and filter for non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. ;

[0013] S22, Submesh Mask By route chain The grid search serves as a cumulative heuristic grid route chain guide for executing the mission objective, filtering out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. ;

[0014] S23, Sub-mesh Mask By route chain The grid search serves as a cumulative heuristic grid route chain guide for executing the mission objective, filtering out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. ;

[0015] S24. Obtain the sub-mesh mask by following the methods S22 to S23. route chain .

[0016] Preferably, the density of non-navigation factors is the percentage of pixels corresponding to non-navigation factors in the grid of the sub-grid mask; sub-grid mask The grid search for achieving the task objective uses subgrid masks. The path search method, which uses internal grid cells as nodes, searches for the optimal grid-connected route from the starting point to the ending point in the task objective, as a path chain. Sub-mesh mask ~ The following methods are used in all cases:

[0017] Mask the top layer of mesh route chain As a sub-mesh mask of this layer Macro-level route grid search uses a cumulative heuristic grid route chain guide, with the current layer's sub-grid mask. Guided by the cumulative heuristic grid route chain in the previous layer, perform a grid search for the mission objective and filter out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. , The hierarchy of the sub-mesh mask. .

[0018] Preferably, from the sub-mesh mask Mesh density to sub-mesh mask The mesh density increases sequentially while the spatial scale of the mesh decreases sequentially; the upper-level sub-mesh mask A specific mesh in the mapping includes the mask of the next layer of sub-mesh. Several grids in the middle.

[0019] Preferably, in method S3, the corresponding projection route chain is located in the m-th sub-grid mask of the electronic chart. Serial mesh , grid The m-th layer sub-mesh mask contains several meshes. The m-th sub-mesh mask performs a mesh search for the mission objective and filters out meshes that are not part of the navigation data. The optimal grid connection route is the route chain. .

[0020] Preferably, the route chain is displayed on the electronic nautical chart. A preliminary path is obtained by connecting lines. The preliminary path is then optimized by processes including redundancy elimination, path smoothing, and Bresenham straight line algorithm to obtain the planned route for the surface vehicle.

[0021] Preferably, the sub-mesh mask Before the path search method, in the submesh mask Define a virtual straight line from the starting point to the end point of the task objective as a path search guide; utilize the mask of the upper-level sub-mesh. route chain Define a virtual polyline from the starting point to the ending point of the task objective as the sub-mesh mask for this layer. Path search guide lines; mask the upper sub-mesh. route chain grid Corresponding projection of the sub-mesh mask of this layer In the middle, grid In the A sub-mesh mask contains several meshes. In each grid The process employs a distributed parallel processing approach, utilizing cumulative heuristic grid route chains and / or path search guide lines to mask the sub-grid at this layer. Grid search.

[0022] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0023] (1) The present invention constructs a multi-layer sub-grid mask on the electronic nautical chart. The multi-layer sub-grid mask is used to perform grid route chain search with progressively higher spatial resolution to obtain progressively more detailed route chains. The route chain of the upper sub-grid mask provides search guidance for the grid route chain search of the lower sub-grid mask. The multi-layer sub-grid mask realizes the search guidance of the cumulative heuristic task objectives of the front and rear layers, which improves the efficiency of grid route chain search. The last sub-grid mask uses the refined route chain to search and optimize the optimal path, which can quickly obtain a refined plan route for surface vehicles. It provides a safe navigation plan route for surface vehicles to avoid non-navigable areas and risk areas, which improves the safe navigation capability and planning decision-making capability of surface vehicles.

[0024] (2) The present invention constructs the upper and lower levels of the m-layer sub-mesh mask according to the grid space scale reduction factor, and accumulates the search guidance and obtains the route chain layer by layer from the macro route to the refined route. It realizes the search guidance of the cumulative heuristic task target of the front and back layers and obtains the refined route chain quickly through the multi-layer sub-mesh mask, which reduces the search calculation amount and complexity and improves the efficiency of water surface navigation path planning. Attached Figure Description

[0025] Figure 1 This is a flowchart of the method for route planning and navigation of a surface vehicle according to the present invention;

[0026] Figure 2 This is a schematic diagram illustrating how a preliminary path is obtained by progressively descending a certain region based on spatial scale, as exemplified in the embodiment.

[0027] Figure 3 for Figure 2 A schematic diagram of the planned route for the surface vehicle is obtained after preliminary path processing. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to embodiments:

[0029] Example

[0030] like Figure 1 As shown, a method for route planning and navigation of a surface vehicle using a progressive spatial scale search is presented, the method comprising:

[0031] S1. Construct a rasterized electronic nautical chart containing water depth information (this embodiment takes ocean application as an example, the electronic nautical chart is a nautical map containing latitude and longitude information), acquire ocean bathymetry maps and extract water depth information according to geographical latitude and longitude, and collect the extracted water depth information into the corresponding raster of the electronic nautical chart according to geographical location. The electronic nautical chart marks non-navigational data that affects the navigation of surface vehicles. In this embodiment, taking ocean application as an example, non-navigational data includes absolute obstacle data and conditional obstacle data. Absolute obstacle data includes obstacle data including islands, glaciers, reefs, and offshore platform facilities. Conditional obstacle data includes military restricted navigation zones, ecological protection restricted navigation zones, areas where meteorological conditions affect navigation, areas where navigation conditions affect navigation, and shallow water areas where the water depth is less than the draft of the surface vehicle.

[0032] S2. Set the mission objective (the mission objective is the surface vehicle navigation mission from the starting point to the end point) on the electronic chart. The electronic chart is constructed with m layers of sub-grid masks with progressively decreasing grid spatial scales. The m layers of sub-grid masks are classified as sub-grid masks according to their upper and lower levels. ~ Sub-grid mask ~ The grid space scale decreases sequentially and is constructed according to the hierarchical hierarchy. Specifically, starting from the sub-grid mask... Mesh density to sub-mesh mask The mesh density increases sequentially while the spatial scale of the mesh decreases sequentially; the upper-level sub-mesh mask ( In the hierarchy of sub-mesh masks, a specific mesh j corresponds to a mapping that includes the next level of sub-mesh mask. There are several grids in the middle. Taking a grid mapping from one grid of the upper-level sub-grid mask to include 10×10 grids (i.e., 100 grids) of the lower-level sub-grid mask as an example, if the sub-grid mask... If the grid space scale is 100m × 100m, then the upper-level sub-grid mask The grid space scale is 1km×1km, and the upper layer sub-grid mask The mesh mapping includes 100 sub-mesh masks of the next layer. The mesh (100 meshes of the next layer sub-mesh mask correspond to one mesh belonging to the previous layer sub-mesh mask, i.e., constructed sequentially according to the hierarchical hierarchy), is the sub-mesh mask. ~ The grid spatial scale decreases sequentially in increments of 100; if the subgrid mask... The grid spatial scale is 1km × 1km, and so on, to obtain the sub-grid mask. The grid spatial scale. Taking a three-layer sub-grid mask as an example, in an example where the reduction factor of the grid spatial scale between the upper and lower sub-grid layers is 100, the grid spatial scale of the first sub-grid mask is 10km × 10km, the grid spatial scale of the second sub-grid mask is 1km × 1km, and the grid spatial scale of the third sub-grid mask is 100m × 100m. The sub-grid mask hierarchy constructed on the electronic nautical chart of this invention, as well as the reduction factor of the grid spatial scale between the upper and lower sub-grid masks, are specifically set in actual practice.

[0033] The first m-1 layers of sub-mesh masks sequentially perform mesh route chain search for the cumulative heuristic task objective of the preceding and following layers, and the m-1th layer of sub-mesh masks outputs the route chain. In some embodiments, a sub-mesh mask ~ Each has a corresponding threshold set for the density of non-navigation factors (the density of non-navigation factors is the proportion of pixels corresponding to non-navigation factors in the grid of the sub-grid mask). ~ From sub-mesh mask ~ The grid search is a cumulative heuristic guided search that progresses sequentially from macroscopic coarse to fine. Therefore, generally speaking, the set density threshold... ~ The process is repeated sequentially. The following is the method for searching the grid route chain of the cumulative heuristic task objective for the first m-1 layers of sub-mesh masks:

[0034] S21, In the sub-mesh mask Perform a grid search to achieve the mission objective and filter for non-navigation factor densities (the non-navigation factor density is the percentage of non-navigation factor corresponding pixels in the sub-grid mask) that are less than a density threshold. The optimal grid connection route is the route chain. Sub-grid mask The grid space scale is the largest, which facilitates macroscopic searches at large grid scales, in the subgrid mask. The optimal search for a macro-grid-connected route is performed based on the starting point to the ending point of the mission objective, forming a route chain. The density of non-navigable factors in each grid in the series is less than the density threshold. Density threshold A larger threshold can be set to facilitate macro-level route selection.

[0035] S22, Submesh Mask By route chain The grid search serves as a cumulative heuristic grid route chain guide for executing the mission objective, filtering out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. By route chain The mesh chain path serves as a cumulative heuristic mesh path chain guide in the sub-mesh mask. The optimal route chain is obtained by performing a grid-connected route search from the starting point to the ending point of the task objective. .

[0036] S23, Sub-mesh Mask By route chain The grid search serves as a cumulative heuristic grid route chain guide for executing the mission objective, filtering out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. By route chain The mesh chain path serves as a cumulative heuristic mesh path chain guide in the sub-mesh mask. The optimal route chain is obtained by performing a grid-connected route search from the starting point to the ending point of the task objective. .

[0037] S24. Obtain the sub-mesh mask by following the methods S22 to S23. route chain .

[0038] In some embodiments, submesh mask The grid search for achieving the task objective uses subgrid masks. The path search method, which uses internal grid cells as nodes, searches for the optimal grid-connected route from the starting point to the ending point in the task objective, as a path chain. Sub-mesh mask ~ The following methods are used in all cases:

[0039] Mask the top layer of mesh route chain As a sub-mesh mask of this layer Macro-level route grid search uses a cumulative heuristic grid route chain guide, with the current layer's sub-grid mask. Guided by the cumulative heuristic grid route chain in the previous layer, perform a grid search for the mission objective and filter out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. , The hierarchy of the sub-mesh mask. .

[0040] In some embodiments, submesh mask Before the path search method, in the submesh mask A virtual straight line is drawn from the starting point to the end point of the task objective as a path search guide. The upper-level sub-mesh mask is then used. route chain Define a virtual polyline from the starting point to the ending point of the task objective as the sub-mesh mask for this layer. Path search guide lines. Mask the upper-level sub-mesh. route chain grid Corresponding projection of the sub-mesh mask of this layer In the middle, grid In the A sub-mesh mask contains several meshes. In each grid The process employs a distributed parallel processing approach, utilizing cumulative heuristic grid route chains and / or path search guide lines to mask the sub-grid at this layer. Grid search, that is, in each grid The system utilizes a cumulative heuristic grid route chain guide and a path search guide line for joint guidance, facilitating rapid masking of the current layer's sub-grid. The grid search is performed simultaneously with the sub-grid masking of this layer. During grid search, each grid Distributed parallel processing (i.e., grid processing) can be used. In the corresponding mapped grids Medium grid search, each grid (Processing in parallel) to accelerate processing efficiency.

[0041] S3, using route chains in the m-th layer sub-mesh mask. The optimal path search and optimization process yields the planned route for the surface vehicle. Specifically, the corresponding projected route chain is mapped into the m-th sub-grid mask of the electronic chart. Serial mesh , grid The m-th layer sub-mesh mask contains several meshes. (In the example where the grid space scale reduction factor is 100, there are 100 grids.) The m-th sub-mesh mask performs a mesh search for the mission objective and filters out meshes that avoid non-navigational data. The optimal grid connection route is the route chain. In electronic nautical charts, the route chain... A preliminary path is obtained by connecting the lines, such as... Figure 2 As shown, this embodiment takes a mission objective from the starting point to the ending point in a certain area as an example. This area has large obstacles or no-fly zones. This embodiment obtains the route chain based on a grid search with the spatial scale decreasing step by step (referring to the grid spatial scale decreasing step by step). For route chains By performing connection processing, Figure 2 The preliminary path (i.e., the initial planned route) for this area is shown. This preliminary path avoids obstacle areas and no-navigation zones in the complex conditions of this region. Then, the preliminary path undergoes optimization processes including redundancy elimination, path smoothing, and Bresenham's straight-line algorithm to obtain the planned route for the surface vehicle (e.g., ...). Figure 3 As shown in the figure, the redundancy elimination method is as follows: if the straight line segment between two points does not pass through any non-navigable data (such as islands or shoals), then all intermediate points between them will be removed. Redundancy elimination is carried out step by step to reduce the number of critical navigation points. When processing the route planning of surface vehicles, it is necessary to set the avoidance of non-navigable data as a constraint condition.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for route planning and navigation of a surface vehicle using a progressive spatial scale search, characterized in that: The methods include: S1. Construct a rasterized electronic nautical chart containing water depth information, and mark non-navigational data that affects the navigation of surface vehicles on the electronic nautical chart; S2. Set the mission objective of the surface vehicle's journey from the starting point to the destination on the electronic nautical chart. The electronic chart is constructed with m layers of sub-grid masks of progressively decreasing grid scale. The first m-1 layers of sub-grid masks sequentially perform a grid route chain search based on the cumulative heuristic mission objective of the preceding and following layers. The (m-1)th layer of sub-grid mask outputs the route chain. ; S3, using route chains in the m-th layer sub-mesh mask. The optimal path search and optimization are performed to obtain the planned route for the surface vehicle.

2. The method for route planning and navigation of a surface vehicle using a step-by-step spatial scale search according to claim 1, characterized in that: In method S1, the non-navigation data includes absolute obstacle data and conditional obstacle data. The absolute obstacle data includes obstacle data including islands, glaciers, reefs, and offshore platform facilities. The conditional obstacle data includes military restricted navigation zones, ecological protection restricted navigation zones, navigation areas affected by meteorological conditions, navigation areas affected by navigation conditions, and shallow water areas where the water depth is less than the draft of surface vehicles.

3. The method for route planning and navigation of a surface vehicle based on a progressive spatial scale search according to claim 1, characterized in that: The m-layer sub-mesh mask is divided into sub-mesh masks according to the upper and lower layers. ~ Sub-grid mask ~ The grid spatial scale decreases sequentially and is constructed according to the hierarchical hierarchy.

4. The method for route planning and navigation of a surface vehicle using a progressive spatial scale search according to claim 3, characterized in that: In method S2, sub-mesh mask ~ Each has a corresponding non-navigation factor density threshold. ~ The mesh route chain search method for the cumulative heuristic task objective of the first m-1 layer sub-mesh mask is as follows: S21, In the sub-mesh mask Perform a grid search to achieve the mission objective and filter for non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. ; S22, Submesh Mask By route chain The grid search serves as a cumulative heuristic grid route chain guide for executing the mission objective, filtering out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. ; S23, Sub-mesh Mask By route chain The grid search serves as a cumulative heuristic grid route chain guide for executing the mission objective, filtering out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. ; S24. Obtain the sub-mesh mask by following the methods S22 to S23. route chain .

5. The method for route planning and navigation of a surface vehicle using a progressive spatial scale search according to claim 4, characterized in that: The density of non-navigation factors is the percentage of pixels corresponding to non-navigation factors in the grid of the sub-grid mask; sub-grid mask The grid search for achieving the task objective uses subgrid masks. The path search method, which uses internal grid cells as nodes, searches for the optimal grid-connected route from the starting point to the ending point in the task objective, as a path chain. Sub-mesh mask ~ The following methods are used in all cases: Apply the top-level mesh mask route chain As a sub-mesh mask of this layer Macro-level route grid search uses a cumulative heuristic grid route chain guide, with the current layer's sub-grid mask. Guided by the cumulative heuristic grid route chain in the previous layer, perform a grid search for the mission objective and filter out non-navigable factors with a density less than a density threshold. The optimal grid connection route is the route chain. , The hierarchy of the sub-mesh mask. .

6. The method for route planning and navigation of a surface vehicle based on a progressive spatial scale search according to claim 3, characterized in that: From subgrid mask Mesh density to sub-mesh mask The mesh density increases sequentially while the spatial scale of the mesh decreases sequentially; the upper-level sub-mesh mask A specific mesh in the mapping includes the mask of the next layer of sub-mesh. Several grids in the middle.

7. The method for route planning and navigation of a surface vehicle using a progressive spatial scale search according to claim 1, characterized in that: In method S3, the corresponding projection route chain is located in the m-th subgrid mask of the electronic chart. Serial mesh , grid The m-th layer sub-mesh mask contains several meshes. The m-th sub-mesh mask performs a mesh search for the mission objective and filters out meshes that are not part of the navigation data. The optimal grid connection route is the route chain. .

8. The method for route planning and navigation of a surface vehicle based on a progressive spatial scale search according to claim 7, characterized in that: Route chain on electronic nautical chart A preliminary path is obtained by connecting lines. The preliminary path is then optimized by processes including redundancy elimination, path smoothing, and Bresenham straight line algorithm to obtain the planned route for the surface vehicle.

9. The method for route planning and navigation of a surface vehicle using a progressive spatial scale search according to claim 5, characterized in that: The sub-mesh mask Before the path search method, in the submesh mask Define a virtual straight line from the starting point to the end point of the task objective as a path search guide; utilize the mask of the upper-level sub-mesh. route chain Define a virtual polyline from the starting point to the ending point of the task objective as the sub-mesh mask for this layer. Path search guide lines; mask the upper sub-mesh. route chain grid Corresponding projection of the sub-mesh mask of this layer In the middle, grid In the A sub-mesh mask contains several meshes. In each grid The process employs a distributed parallel processing approach, utilizing cumulative heuristic grid route chains and / or path search guide lines to mask the sub-grid at this layer. Grid search.

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